StimulStat: A lexical database for Russian


In this article, we present StimulStat – a lexical database for the Russian language in the form of a web application. The database contains more than 52,000 of the most frequent Russian lemmas and more than 1.7 million word forms derived from them. These lemmas and forms are characterized according to more than 70 properties that were demonstrated to be relevant for psycholinguistic research, including frequency, length, phonological and grammatical properties, orthographic and phonological neighborhood frequency and size, grammatical ambiguity, homonymy and polysemy. Some properties were retrieved from various dictionaries and are presented collectively in a searchable form for the first time, the others were computed specifically for the database. The database can be accessed freely at
StimulStat: A lexical database for Russian
Svetlana Alexeeva
&Natalia Slioussar
&Daria Chernova
Published online: 7 December 2017
#Psychonomic Society, Inc. 2017
Abstract In this article, we present StimulStat a lexical
database for the Russian language in the form of a web appli-
cation. The database contains more than 52,000 of the most
frequent Russian lemmas and more than 1.7 million word
forms derived from them. These lemmas and forms are char-
acterized according to more than 70 properties that were dem-
onstrated to be relevant for psycholinguistic research, includ-
ing frequency, length, phonological and grammatical proper-
ties, orthographic and phonological neighborhood frequency
and size, grammatical ambiguity, homonymy and polysemy.
Some properties were retrieved from various dictionaries and
are presented collectively in a searchable form for the first
time, the others were computed specifically for the database.
The database can be accessed freely at http://stimul.
Keywords Lexical database .Russian .Frequency .
Neighborhood .Grammatical properties
Introducing the StimulStat database
Experimental studies of language identified a large list of
lexical properties that play a role in speech production and
comprehension, including lemma and word form frequency
and length (e.g., New et al., 2006;Kliegletal.,2004;
Monsell, 1991;Rayner,1998; Yap & Balota, 2015), the
number of syllables (e.g., Ashby & Rayner, 2004;
Carreiras & Grainger, 2004;Taft&Forster,1976), stress
pattern (e.g., Arciuli & Cupples, 2006; Colombo, 1992;
Schiller, Fikkert, & Levelt, 2004), homonymy and polyse-
my (e.g., Beretta, Fiorentino, & Poeppel, 2005;Mirman
et al., 2010;Roddetal.,2004), grammatical characteristics,
including part of speech, inflectional paradigm, etc. (e.g.,
Baayen, Dijkstra, & Schreuder, 1997;Taft,1979), different
properties of orthographic and phonological neighborhoods
(e.g., Adelman et al., 2013; Andrews, 1997a,b; Perea,
2015), etc. Preparing stimuli for a psycholinguistic experi-
ment usually requires taking many of these characteristics
into account at once: selected items should differ with re-
spect to the factors of interest, but should be closely
matched with respect to other relevant properties. This task
might be very difficult to accomplish without searchable
lexical databases that include various characteristics for a
large number of words.
Such databases have been created for several languages
and are available in the form of a web application or computer
software. Among them are the English lexicon project (Balota
et al., 2007), eDom (Armstrong, Tokowicz, & Plaut, 2012), N-
Watch (Davis, 2005) and MRC database (Coltheart, 1981)for
English; DlexDB for German (Heister et al., 2011); Lexique
(New, Pallier, Brysbaert, and Ferrand, 2004) for French; EsPal
(Duchon, Perea, Sebastián-Gallés, Martí, & Carreiras, 2013)
and BuscaPalabras (Davis & Perea, 2005) for Spanish; EHME
(Acha, Laka, Landa, & Salaburu, 2014) and E-Hitz (Perea
et al., 2006) for Basque; GreekLex (Ktori, van Heuven, &
Pitchford, 2008) and GreekLex2 (Kyparissiadis et al., 2017)
for Modern Greek; Aralex (Boudelaa & Marslen-Wilson,
2010) for Modern Standard Arabic; the Malay Lexicon
Project (Yap, Liow, Jalil, & Faizal, 2010) for Malay;
KelemetriK (Erten, Bozsahin, & Zeyrek, 2014)f
*Svetlana Alexeeva
St. Petersburg State University, St. PetersburgGalernya 58/60,
190000, Russia
National Research University Higher School of Economics,
Moscow, Russia
Behav Res (2018) 50:23052315
the Brazilian Portuguese Lexicon (Estivalet & Meunier, 2015)
for Brazilian Portuguese, etc. All these databases are equipped
with effective search and filtering tools.
In this article, we present StimulStat the first lexical
database of this type created for Russian. Among distinc-
tive features of Russian are its rich inflectional and deriva-
tional morphology, a complicated system of inflectional
paradigms, a flexible stress, and a Cyrillic alphabet.
Russian has a rich lexicographic tradition, with the most
recent projects relying on large corpora, primarily on the
Russian National Corpus ( However,
while some dictionaries are available electronically, the
others are not, and no existing resource allows combining
information from different sources. Moreover, certain
characteristics relevant for psycholinguistic research, for
example, orthographic neighborhood properties, are not
represented in any dictionary or database at all.
The StimulStat database addresses these problems, provid-
ing an effective tool for conducting psycholinguistic research.
It includes more than 52,000 lemmas and more than 1.7 mil-
lion word forms derived from them. StimulStat is available as
a web application and allows searching for lemmas and word
forms with particular properties, as well as retrieving required
properties for a predefined list of items.
Sources used in the database
The StimulStat database contains 52,139 lemmas included in
the Frequency dictionary of modern Russian language
(Lyashevskaya & Sharov, 2009). This dictionary is based on
the subcorpus of the Russian National Corpus (www. containing 92 million words. Lemma
frequency values and part of speech tags were taken from
this dictionary. The Russian National Corpus includes 150
million words; the collection of texts dating from XVIII to
XXI century is balanced by genre and style. The 92 million-
word subcorpus the dictionary is based on includes texts dat-
ing from 19502007. It is considered to be the most represen-
tative frequency dictionary of modern Russian.
Grammatical characteristics of lemmas were defined using
the morphological parser Pymorphy2 (Korobov, 2015), which
relies on the OpenCorpora morphological dictionary
(Bocharov et al., 2013). The OpenCorpora dictionary is dis-
tributed under the free content license of Creative Commons.
Given this advantage and the considerable size of the dictio-
nary (389,232 lemmas and 5,097,247 word forms),
we pre-
ferred the Pymorphy2 parser to Mystem2 (Segalovich, 2003),
another morphological parser widely used for Russian. It was
originally developed as an internal commercial tool, and its
morphological dictionary is not publicly available.
Information about stress position and different properties of
inflectional paradigms was taken from the Grammatical dic-
tionary of Russian language (Zaliznjak, 1987). This dictio-
nary contains more than 100,000 lemmas and gives a full
morphological description for each lemma. Most morpholog-
ical parsers developed for Russian are based on the electronic
version of this dictionary. However, it does not include unin-
flected parts of speech, i.e., adverbs, prepositions, conjunc-
tions, particles, and interjections, as wellas some novel words.
Thus, for a number of lemmas included in StimulStat we had
to define the stress position and inflectional properties indi-
vidually. This was done by two authors of the project who
have a linguistic background.
In general, an important part
of our project was to align different sources used in the data-
base. This could be done automatically in the majority of
cases, but still, a lot of manual checking was required.
The Explanatory dictionary of Modern Russian (Efremova,
2000) was used to extract information about polysemy and
homonymy. As is well known, deciding on the number of
meanings a word has and differentiating between polysemy
and homonymy is often difficult. For example, two meanings
of one word can go very far apart in the process of diachronic
change, but it is a matter of controversy when the relation
between them becomes so obscure that it makes sense to iden-
tify two homonymous words. However, since these problems
cannot be avoided, we chose Efremovas dictionary as the
largest electronically available explanatory dictionary of
Russian it contains more than 120,000 lemmas.
The data on the subjective age of acquisition and
imageability were drawn from the database Verb and action
(Akinina et al., 2014) that contains 375 verbs. The number of
verbs is relatively small, but the goal of StimulStat is to aggre-
gate all available reliable resources that could be useful to psy-
cholinguists and to allow using them simultaneously.
Subjective parameters were shown to be relevant in many ex-
perimental studies (e.g., Bates et al., 2003), so it makes sense to
include them for as many lemmas as is currently possible.
We used the morphological parser Pymorphy2 (Korobov,
2015) to generate all forms from the lemmas in the database
and to specify their grammatical features. Forms are included
in the database as separate items (one can search for forms or
for lemmas) and are linked to lemmas. In total, we have
1,700,842 word forms. For 355,935 forms, frequency values
were extracted from the database of the corpus-based project
Frequency grammar of Russian (
freaky_frequency/freq_main.html) (Lyashevskaya, 2013).
There are several ways totransliterate Russian words into the Latin alphabet.
Providing examples in this paper, we use the so-called scholarly transliteration
The numbers were taken from the project website (
dict.php) on 10 April 2017.
Russian has flexible stress, and in some cases, the norm is unstable, so we
need to mention that there were no such controversial cases among the words
we had to annotate manually.
2306 Behav Res (2018) 50:23052315
This is the only resource that provides frequency information
for morphologically disambiguated word forms in Russian.
Finally, we used the CORPRES dictionary of phonological
variants created at the Laboratory for Experimental Phonetics
of St. Petersburg State University (Skrelin et al., 2010). The
dictionary is based on the CORPRES, which includes 60 h of
recorded speech: texts of different genres pronounced by eight
speakers. The corpus contains more than 100,000 word forms
with two types of phonemic transcription taking some allophonic
variation into account: so-called ideal transcription (generated
automatically based on the existing conventions for standard
Russian and then manually checked) and real transcription (gen-
erated manually and reflecting how a given form was actually
pronounced by recorded speakers). A full list of symbols used in
transcription and their description can be found here: http://
For example, the word leto, Bsummer,^has one ideal tran-
scription /l' e0 t a4/ and three real transcriptions associated
with it in the dictionary: /l' e0 t a4/, /l' e0 t e4/ and /l' e0 t y4/
. Real transcriptions differ in the quality of the post-stressed
vowel. In total, there are 9,965 unique pairs of word forms and
their ideal phonemic transcriptions and 26,778 unique pairs of
word forms and their real phonemic transcriptions.
Russian orthography is relatively transparent, so transcrip-
tions are not provided in the dictionaries, except for small
dictionaries intended for beginner L2 learners. However, it is
not the case that transcriptions can be easily derived from
orthographic representations in a rule-based fashion. Firstly,
Russian has a morphologically-based type of orthography: in
most cases, different realizations of a morpheme have the
same spelling even when they are pronounced differently.
Secondly, stress position that influences vowel quality cannot
be predicted from the orthographical representation.
We ch o s e t o r e ly on the CORPRES dictionary because
there is no publicly available spelling-to-sound converter for
Russian. It also has an important advantage over the rule-
based approach taken, for example, in the Espal (Duchon
et al., 2013)andtheMalay Lexicon project (Yap et al.,
2010). It reflects not only the pronunciation of isolated words,
but also captures phonetic phenomena at word boundaries,
like progressive assimilation of voice that is very widespread
in Russian. To give an example, the word vopros, Bquestion^
is pronounced as /v a1 p r o0 s/ (ideal transcription) in isola-
tion. But if it precedes a word starting with a voiced obstruent,
like in vopros zadannyj, Bquestion asked,^the last phoneme
would be /z/: /v a1 p r o0 z/. Both transcriptions are represent-
ed in the StimulStat database.
Available information
The database contains 52,139 lemmas and 1,700,842 word
forms derived from them. 451 lemmas are morphologically
ambiguous: for example, dobro is a noun meaning Bgood,
welfare,^aparticleBdeal, granted,^and an adverb Bamicably,
tenderly.^Thus, the number of orthographically unique
lemmas is 51,688. The number of orthographically unique
forms is only 963,257, due to widespread syncretism: many
forms are morphologically ambiguous. For example, koške
can be a dative singular or a locative
singular from the noun
koška, Bcat.^.
Frequency information
StimulStat provides information about frequency measured in
ipm (instances per million) for all lemmas and for 355,935
word forms, out of them 252,091 orthographically unique
ones. We also calculated ln-transformed and lg-transformed
frequency values because there is a logarithmic relationship
between word frequency and reaction time to this word during
lexical access (e.g., Duyck, Desmet, Verbeke, & Brysbaert,
2004; Keuleers et al., 2012; Kinoshita, 2015; Kliegl,
Grabner, Rolfs, & Engbert, 2004; Kliegl, Nuthmann, &
Engbert, 2006; Monsell, Doyle, & Haggard, 1989; Oldfield
& Wingfield, 1965). In addition to that, we computed different
frequency measures for ideal and real transcriptions included
in the database. Since the number of these transcriptions is
relatively small so far, these statistics are mostly useful to
determine which phonological variants of high frequency
words are more widespread.
Information based on orthographic and phonological
First of all, StimulStat can provide ideal or real phonological
representations paired with a given orthographic representa-
tion (only for word forms because phonological representa-
tions are associated with word forms). This information and
other characteristics relying on phonological representations
can be obtained only if the form in question is included in the
CORPRES dictionary (Skrelin et al., 2010). We also calculat-
ed various parameters for all representations included in the
database. When using these parameters in the search, one
should specify which representations orthographic, ideal,
or real phonemic to rely on.
For all lemmas and word forms, StimulStat provides infor-
mation about length (in letters and in phonemes) and so-called
uniqueness point. This is the letter/phoneme position reading
from left to right that distinguishes a word from all other
words (Marslen-Wilson & Tyler, 1980). The uniqueness point
was shown to be relevant for psycholinguistic research; for
example, this factor affects naming and lexical decision
The tag Bprepositional^is used instead of Blocative^in some morphological
descriptions of Russian.
Behav Res (2018) 50:23052315 2307
latencies (e.g., Kwantes & Mewhort, 1999;Lindell,Nicholls,
Among other supplementary parameters are the first and
last letter/phoneme of the word, and its reversed orthographic
and phonological representation (e.g., okolom for moloko,
Bmilk^). The reversed representation is useful for experiments
dealing with morphology. One cannot directly search
StimulStat for words with a particular affix because the data-
base does not provide morphological segmentation. However,
one can select the pool of words satisfying other relevant
parameters (e.g., frequency, length, etc.) and then sort them
by their reversed representation. Then the words with the same
affixes will be grouped together. For this reason, the
Grammatical dictionary of Russian language (Zaliznjak,
1987) relies on reversed orthographic representation.
Modern Russian alphabet has 33 letters, but one of them, ё,
is often substituted for eboth in books and other printed pro-
duction and in handwriting. Words with these two letters are
pronounced differently, but it is easy for an advanced reader to
recover this information in the absolute majority of cases,
unless the word is an infrequent proper name etc., in which
cases ёwould be used much more consistently. The database
takes this into account: all orthographic parameters including
information about neighbors can be computed assuming that ё
is a separate letter or that it coincides with e.Manysourceswe
relied on do not use ё, so we had to insert it.
StimulStat has information on syllable structure that can be
computed based on the orthographic, ideal and real phonolog-
ical representation. It is more sensible to rely on phonological
representations in this case, but not all words in the database
have transcriptions, so all options were realized. StimulStat
includes word length in syllables (the number of syllables is
computed based on the number of vowels), information about
syllable boundaries and the CV notation. Information about
syllable boundaries is provided in the following form: e.g.,
2_4 for moloko, Bmilk,^indicating that the boundaries are
after the second and the fourth symbol. Syllable boundaries
are a matter of controversy in Russian linguistics. We relied on
the approach developed by Bondarko (1977), according to
which Russian syllables are always open except for terminal
syllables ending in a consonant and for non-terminal syllables
ending in /j/. It is supported by strong experimental evidence
(Bondarko, 1977).
In the CV notation, Vstands for a vowel, Cfor a consonant,
and Fdenotes the letters ьand ъcalled soft and hard sign,
which are neither vowels nor consonants. The soft sign signals
that the preceding consonant is palatalized, and, if it is follow-
ed by a vowel, that /j/ is pronounced between this consonant
and this vowel. The hard sign indicates that the consonant is
not palatalized despite the following front vowel and that /j/ is
pronounced between this consonant and this vowel. Thus, the
symbol Fis used only if the CV notation is computed on the
basis of orthographic representation: the soft and hard sign
influence phonemic transcription, but do not correspond to
any phonemes.
The database also contains information about the main and
additional stress position: on which vowel or on which sylla-
ble counting from left to right the stress falls. For example, in
the word more, Bsea,^the stress falls on the vowel in the first
syllable (it is underlined). So the stress position in symbols is
2, and the stress position in syllables is 1. For lemmas, it is also
indicated whether there is a stress shift in the inflectional par-
adigm. For example, the word ruka, Bhand,^has it: the stress
falls on the ending in nominative singular and on the root in
some other forms, like the accusative singular form ruku.The
word strana, Bcountry,^has no stress shift, for example, its
accusative singular form is stranu.
Grammatical information
StimulStat provides information about parts of speech and
different grammatical features for lemmas and forms, includ-
ing gender, number, person, case, animacy, tense, mood, as-
pect, voice, transitivity, and comparative and superlative de-
grees. It is also specified whether a given verb form is finite or
not, and, in the latter case, whether it is an infinitive, participle
or gerund, and whether an adjective or participle form is short
or full. These two types of forms have different morphological
and syntactic properties in Russian.
Two approaches to parts of speech are represented in the
database. The first is adopted in the Frequency dictionary of
modern Russian language (Lyashevskaya & Sharov, 2009)
and the Russian National Corpus ( It
distinguishes nouns, verbs, adjectives, adverbs, cardinal
and ordinal numbers, pronominal nouns, adjectives and
adverbs, as well as prepositions, conjunctions, particles,
and interjections. The second approach is adopted in the
OpenCorpora morphological dictionary (Bocharov et al.,
2013) and relies primarily on the inflectional characteris-
tics. According to this, ordinal numbers, pronominal adjec-
tives and pronominal adverbs are not separate parts of
speech because they do not differ from other adjectives
and adverbs with respect to their inflectional properties. At
the same time, short forms of adjectives, non-finite verb
forms, and comparatives form separate groups.
It is possible to search for various grammatical character-
istics separately, and the full list of grammatical features and
the full inflectional paradigm can be requested for every item.
The database also includes inflectional indices from the
Grammatical dictionary of Russian language (Zaliznjak,
1987). These indices were introduced to capture different
properties of paradigms: inflectional classes, the presence or
absence of consonant and vowel alternations, stress shifts, etc.
For lemmas, StimulStat also provides grammatical features of
the citation form, and for forms, the lemma can be found.
2308 Behav Res (2018) 50:23052315
Orthographic and phonological neighborhood
Neighborhood characteristics have not been addressed in any
previous work on Russian, so calculating them was an impor-
tant part of our project. We will describe orthographic neigh-
borhoods first and then will turn to phonological ones.
Different properties of orthographic neighborhoods were
demonstrated to play a role in a variety of reading tasks, in-
cluding lexical decision, naming, perceptual identification,
and semantic categorization. Several types of orthographic
neighbors have been identified:
&Substitution neighbors, or sns (e.g., Coltheart et al., 1977).
These are words obtained by changing one letter in a given
word (in any position) while preserving the other letters,
for example, syn, Bson^syr, Bcheese.^
&Transposition neighbors, or tns (e.g., Andrews, 1996;
Perea & Lupker, 2003). These are words that share the
same letters, but the positions of two of them are
interchanged. These letters can be adjacent, as in setka,
Bnet^sekta, Bsect,^or not, as in buk, Bbeech^kub,
&Addition and deletion neighbors, or ans and dns (e.g.,
Davis, Perea, & Acha, 2009). A deletion neighbor of a
word is a letter string that differs from it by deletion of a
single letter (in any position), and an addition neighbor isa
string with an extra letter in any position. For example,
karta, Bmap, card^is an addition neighbor of kara,
Bpenalty,^and kara, Bpenalty^is a deletion neighbor of
karta, Bmap, card.^
&Subset and superset neighbors, or pns and wns (e.g.,
Bowers,Davis,&Hanley,2005). A subset (part)
neighbor of a given word is a letter string embedded
within this word. A superset (whole) neighbor is a
letter string that contains the given word. For example,
sort, Bsort,^is a superset neighbor of sor, Blitter,^and
a subset neighbor of sortirovat, Bto sort.^When subset
and superset neighbors were computed for the
StimulStat database, we did not take words that are
shorter than three letters into account.
&Bigram and trigram neighbors, or bins and trins (e.g.,
Davis, 2005). A bigram neighbor of a word is a letter
string that shares with it a bigram (two successive letters)
in the same position. Trigram neighbors share three suc-
cessive letters in the same position. For example, spina,
Bback,^is a bigram neighbor of volna, Bwave,^whereas
volk, Bwolf,^is its trigram neighbor.
We identified orthographic neighbors for all lemmas
and word forms in the database. In addition to that, for
every neighborhood, the number of words in it and their
summed frequency (also ln-transformed and log-
transformed) was calculated. StimulStat also provides in-
formation about the most frequent and the least frequent
word in every neighborhood, and the number of neighbors
that are more frequent than the given word. For transpo-
sition neighbors, there is a parameter showing whether the
transposed letters are adjacent or not.
We calculated the same neighborhood parameters for real
phonological representations included in the database. It is
important to keep in mind that for many forms, we only have
an orthographic representation, so the data set we rely on is
smaller in this case. Notably, many word forms have different
phonemic realizations that can be classified as phonological
neighbors (for example, with a voiced and a voiceless final
consonant its realization in the connected speechdepends on
the following word). We decided not to count different reali-
zations of one and the same form as neighbors: only realiza-
tions of different word forms were taken into account. To give
an example, /v a1 p r o0 s/ and /v a1 p r o0 z/ are different
realizations of the word vopros, Bquestion,^so they are not
counted as neighbors.
Homonymy, homography, and morphological ambiguity
StimulStat provides various information about lemmas and
forms that have the same spelling, but differ in other proper-
ties. Firstly, we relied on the Explanatory dictionary of
Modern Russian (Efremova, 2000) that tags lemmas having
homonyms and homographs.
However, this dictionary does
not differentiate between three following options. Homonyms
and homographs may (i) have the same grammatical proper-
ties (like bor, Bpine forest^and bor, B(dental) drill^); or (ii)
differ in some of them, for example, in animacy, which influ-
ences the choice of case endings in Russian (like operator,
Boperator, mechanic, camera man^or Boperator, abstract func-
tion, statement (in programming)^); or (iii) even belong to
different parts of speech (like zlo, which can be a noun Bevil^
or an adverb Bin an evil way^).
Homonyms of the first type are not differentiated in any
other source used in StimulStat, but homonyms of the second
and third type and all types of homographs can be identifiedin
the pool of lemmas included in the database.
StimulStat rep-
resents these results and the results based on Efremovasdic-
tionary separately. An additional reason to do so is that fact
that Efremovas dictionary is relatively conservative, so it
does not contain many lemmas included in StimulStat. Of
Homographs are words that are orthographically the same, but have different
stress, e.g., zamok, Bcastle^zamok, Block^(the stressed vowel isunderlined).
As we explained in the beginning of the paper, the relevant part of the
database (the pool of lemmas with grammatical characteristics and stress)
relies on several sources and on our own work synchronizing these sources
we had to identify stress position and grammatical characteristics for a
number of lemmas. Thus, we cannot identify one particular source we rely
on here.
Behav Res (2018) 50:23052315 2309
course, the opposite is also true: many archaic, dialectal, and
simply infrequent words covered by this dictionary are not
included in StimulStat.
Word forms that have the same spelling can also coincide
or differ with respect to the stress position. Obviously, the
crucial parameter for such forms is whether they belong to
the same lemma or not. Accordingly, StimulStat allows
searching for orthographically identical forms that (i) belong
to one lemma and have the same stress (e.g., koške is a dative
singular or a locative singular from the noun koška, Bcat^); (ii)
belong to one lemma and have different stresses (e.g., rukiis a
genitive singular and ruki is a nominative plural from the noun
ruka, Bhand^); (iii) belong to different lemmas and have the
same stress (e.g., bystro is a neuter short form from the adjec-
tive bystryj, Bquick^or an adverb Bquickly^); (iv) belong to
different lemmas and have different stresses (e.g., tušuis an
accusative singular from the noun tuša, Bhand^and tušuis a
person singular present tense form from the verb tušit, Bto
Semantic information and subjective parameters
We provide information about polysemy: the number of mean-
ings the word has according to the Explanatory dictionary of
Modern Russian (Efremova, 2000). Obviously, the dictionary
also contains the definition of every meaning, but we did not
include this information. We specify whether the word is an
abbreviation or a proper name (bothin general and in particular
a first or a last name, a patronymic or a place name). For 375
verb lemmas, we provide mean values and standard deviations
of so called subjective parameters: the age of acquisition and
imageability based on (Akinina et al., 2014).
Technical specifications and the web interface
We used Python scripts to extract and compute all parameters
mentioned above. The output of the scripts were several lists,
including two main lists: one for lemmas and another one for
word forms. To make the database available as a web appli-
cation, we imported these lists with linguistic parameters to a
PostgreSQL database. The web interface was created using
Django web application library.
The website has four
pages (in English and in Russian). The title page contains a
description of the database, a user manual, and references to
all external sources used in the project. Another page contains
additional materials from an independent project: information
about frequencies of different grammatical features and
inflectional affixes in Russian nouns. The other two pages
are for searching the database.
Firstly, it is possible to look for lemmas and word forms
with certain characteristics. For all numeric parameters, =, <
and > signs are available, so one can search for exact values or
for a particular range. Secondly, StimulStat can supply select-
ed characteristics for a predefined list of lemmas or forms.
Lemmas or forms can be typed into a search field or uploaded
as a list in a *.txt or *.csv file (in utf-8 encoding). The output
will appear on a separate web page and can be downloaded as
a *.csv file.
An overview and cross-linguistic comparisons
For some of the parameters included in StimulStat,wecom-
puted average values and the range of possible values. The
results are presented in Table 1, except for orthographic neigh-
borhood characteristics, which will be discussed below.
Calculations were done separately for all lemmas in the data-
base, for word forms with frequency values and for all word
forms generated from the lemmas included in the database. In
addition to that, when a certain parameter, like the average
lemma length in letters, is calculated, lemma frequency can
be taken into account. The average length of all lemmas in
StimulStat is 9.1, but more frequent lemmas tend to be shorter,
and the average length corrected for frequency is 5.5. In the
first case, we rely on the number of words that consist of one,
two, three and more letters. In the second case, we rely on the
summed frequencies of these words.
Several papers discussing lexical databases created for oth-
er languages also report average values of different parame-
ters, but a cross-linguistic comparison is often complicated by
various differences in database sources. The CLEARPOND
project (Marian et al., 2012)aimstoovercomethisproblem.It
relies on the databases for five languages: English, French,
German, Dutch, and Spanish, which are based on movie sub-
title corpora. The databases are of the same size: they contain
the most frequent 27,751 word forms encountered in the cor-
pus of the relevant language. To arrive at this number, the
authors took word form frequencies for every corpus and ex-
cluded the forms whose frequency was lower than 0.34 ipm.
The list of remaining forms was the shortest in the English
corpus: it contained 27,751 items, so this number was taken as
a threshold for all five languages.
The CLEARPOND project reports the following average
form frequency values: 32.6 ipm for Dutch, 32.7 ipm for
English, 30.9 ipm for French, 33.7 ipm for German, and
33.9 ipm for Spanish. The values presented for Russian in
Tab le 1are dramatically different, but the size of the
StimulStat database is much bigger. For the sake of compari-
son, we recalculated the values of several parameters for
27,751 most frequent word forms. The resulting average form
frequency is 29.4 ipm (SD = 379.5, range: 3.238,107.4). This
is very close to the values reported by Marian et al., (2012),
especially taking into account that CLEARPOND databases
are based on movie subtitles, while the Frequency grammar of
2310 Behav Res (2018) 50:23052315
Russian project (Lyashevskaya, 2013)StimulStat relies on is
based mainly on fiction and newspaper texts. The average
frequency of 40,481 forms in the English Lexicon Project
(Balota et al., 2007) is 29.7. This project relies on frequency
measures from Kučera and Francisfrequency list (Kučera &
Francis, 1967), which was based on fiction and newspaper
Information about average lemma frequency is available
for Greek: 33.9 ipm (Ktori, van Heuven, & Pitchford,
2008).ThefigureinTable1is much lower: 18.5 ipm.
However, the GreekLex database is considerably smaller
than StimulStat, it contains 35,304 lemmas. If only 35,304
most frequent lemmas in StimulStat are taken into account,
the average lemma frequency equals 26.8 ipm (SD =352.9,
range: 135,801.8).
Now let us turn to the average form length in symbols.
The following values are reported in the CLEARPOND
project: 8.4 for Dutch, 7.3 for English, 7.9 for French, 8.3
for German, 7.9 for Spanish. The values in Table 1are
larger both for forms with frequency information and for
all forms. However, if only 27,751 most frequent forms are
taken, the average length is 7.6 (SD = 2.5, range: 124).
The average form length in the English Lexicon Project
(Balota et al., 2007) is 8.0. Thus, the popular belief that
words tend to be longer in Russian than in Germanic and
Romance languages is not supported.
The paper on the GreekLex database (Ktori, van Heuven,
& Pitchford, 2008) reports 9.0 and 5.7 as the average lemma
length (the second figure is corrected for frequency). The fig-
ures in Table 1are 9.1 and 5.5, but if we select a subcorpus of
the same size as GreekLex, they will be smaller: 8.7 (SD =3.0,
range: 131) and 5.4 (SD =3.3,range:131).
The average form length in syllables can be compared to
the data presented in the paper on the Malay Lexicon Project
Tabl e 1 The properties of lemmas and word forms in the StimulStat database
Lemmas Word forms that have frequency information All word forms
Mean SD Range Mean SD Range Mean SD Range
Length in letters 9.1 (5.5)
3.2 (3.3)
134 9.2 (5.4)
2.8 (3.2)
131 10.4 2.9 134
Length in syllables
3.5 (2.1)
1.4 (1.3)
015 3.9 (2.3)
1.3 (1.4)
013 4.5 1.5 015
Frequency 18.5 291.9 0.435,801.8 3.8 126.2 0.238,107.4 N/A N/A N/A
Uniqueness point
7.2 2.6 221 N/A N/A N/A 10.5 2.8 232
Stress position in letters
5.4 (3.2)
2.4 (2.0)
121 5.2 (3.6)
2.3 (2.0)
117 5.5 2.2 121
Stress position in syllables
2.4 (1.5)
1.1 (0.8)
1.0 (0.8)
If word frequencies are taken into account
We decided not to calculate uniqueness point values for the subset of forms that have frequency values
Some Russian words (prepositions and particles) consist of a single consonant and therefore contain no syllables. They were not taken into account
when stress position was calculated
Tabl e 2 The number of orthographic neighborhoods (N) and words that comprise them for lemmas and word forms included in the StimulStat
Neighbor- hood type Orthographically unique lemmas (51,688) Orthographically unique word forms (963,257)
Number of Ns Number of words
Number of Ns Number of words
sns 12,280 15,241 (29.5 %) 704,072 819,965 (85.1 %)
tns 642 1,130 (2.2 %) 17,657 30,718 (3.2 %)
ans 2,409 5,077 (9.8 %) 311,694 648,223 (67.3 %)
dns 3,346 5,077 (9.8 %) 510,193 648,223(67.3 %)
pns 44,592 47,380 (91.7 %) N/A
wns 14,175 47,380 (91.7 %) N/A
bins 6,636 51,645 (99.9 %) 8,950 963,227 (99.9 %)
trins 25,610 51,175 (99.0 %) 51,516 962,835 (99.9 %)
The percentage of words in the database that have neighbors of a certain type is indicated in parentheses
The information about bigram and trigram neighbors (bins and trins) for all word forms is not stored in the database, unlike in the other cases, such
neighbors are calculated online for every query about individual forms
Behav Res (2018) 50:23052315 2311
(Yap et al., 2010). In addition to Malay, this paper discusses
four other languages, but the statistics reported for them are
derived from the databases of different sizes:
3.0 for Malay
(corpus size: 9,592), 2.5 for French (corpus size: 38,335), 2.5
for English (corpus size: 38,477), 3.4 for German (corpus size:
50,658), and 3.5 for Dutch (corpus size: 117,867).
The figure in Table 1is larger (3.9), but if we select
subcorpora of the same sizes, the values will be 2.8, 3.2, 3.2,
3.3, and 3.5, respectively. Thus, the average form length in
syllables in Russian is similar to German and Dutch, while
English and French tend to have less syllables per form.
Presumably, this is due to the fact that in both languages,
diphthongs and letters that are not pronounced, i.e. do not
correspond to any phonemes, are quite frequent. The figure
in Malay is only slightly larger than in Russian, and it is
difficult to speculate about the reasons. Apparently, the aver-
age form length is slightly larger in Malay, and it also tends to
have open syllables.
Average values for the uniqueness point are not discussed
for other databases, so no cross-linguistic comparison is pos-
sible. As for the stress position, average values can be found
only for the GreekLex database (Ktori, van Heuven, &
Pitchford, 2008). However, they cannot be directly compared
to our data because the stress position is calculated from the
end of the word, not from the beginning this makes sense
because in Greek, the stress can fall only on one of the three
final syllables.
Now let us turn to the characteristics of orthographic neigh-
borhoods, which we analyzed in more detail because they
have not been discussed for Russian before. Table 2presents
the number of orthographic neighborhoods of different types
identified in StimulStat, as well as the number and the percent-
age of lemmas and forms in the database that are included in
these neighborhoods. Tables 3and 4show how many neigh-
bors of a certain type a lemma or a form has on average.
Tab le 3provides the numbers for all orthographically
unique lemmas and forms included in StimulStat.Thisdem-
onstrates how widespread a certain type of neighbors is.
Table 4presents similar calculations, but only for lemmas
and forms that have neighbors of the relevant type: for exam-
ple, we calculated how many lemmas or forms are included in
every substitution neighborhood. This shows the average size
of neighborhoods of different types.
The data on substitution neighbors in Table 3can be com-
pared to the results obtained in the CLEARPOND project. As
before, to have a valid comparison, we recalculated the values
for a subcorpus including 27,751 most frequent word forms in
StimulStat. The average number of substitution neighbors per
form is 3.1 in Table 1, and for this subcorpus, it is 1.6 (SD =
1.9, range: 017). It is similar to 1.5 reported for Spanish,
higher than in Dutch, French, and German (all about 1 on
average), but smaller than in English (about 2 on average).
Notably, in the database of the English lexicon project
(Balota et al., 2007), which contains 40,481 forms, the
average number of substitution neighbors per form is 1.2.
In StimulStat, the average number of neighbors always de-
creased when a smaller subcorpus was taken. This points at
interesting cross-linguistic differences beyond easily com-
parable average numbers. Exploring them is beyond the
scope of this paper, so we can only provide an informal
observation. As far as we can judge, the absolute majority
of neighbors in English have different roots. Many neigh-
bors in Russian have different affixes, while the root is the
same. For example, prefixes are very widespread, and
many prefixes differ by one letter: za- and na-,do- and
po-,po- and pod-,v- and vy-etc.
Some cross-linguistic differences in neighborhood proper-
ties have already been explored, primarily for transposition
neighbors, and demonstrated to be psycholinguistically
relevant. For example, Frost (2012,2015) reviewed priming
effects across writing systems to conclude that they crucially
depend on the frequency of such neighbors, which can be very
different in different languages.
A new model of reading was
suggested based on these findings.
The average number of addition and deletion neighbors in
Tab le 1is 0.6. For the 27,751 form subcorpus, the numbers are
0.3 (SD =0.9,range:015) and 0.3 (SD =0.5,range:04)
respectively. These numbers are similar to the ones reported
for Dutch, German, and Spanish in the CLEARPOND project
(0.4 for both neighbor types). In English and French, the
Form length in symbols is also analyzed in this paper, but for this reason we
preferred to rely on the CLEARPOND project.
Tabl e 3 The number of orthographic neighbors of different types per
lemma or word form in StimulStat (all lemmas and forms in the database
are taken into account)
Lemmas Word forms
Mean SD Range Mean SD Range
sns 0.8 2.0 028 3.1 2.5 041
tns 0.02 0.2 02 0.03 0.2 04
ans 0.07 0.5 029 0.6 1.2 025
dns 0.07 0.3 030.60.7 05
pns 2.5 2.0 017 N/A
wns 2.5 32.4 03,463 N/A
bins 433.1 551.9 03,098 8,520.4 12,444.4 082,759
trins 102.6 197.6 01,178 1,974.1 4,018.9 030,395
The information is not available because the relevant parameters are
calculated online for every query and are not stored in the database
Every prefix has a variety of meanings, so it is difficult to provide transla-
tions. For example,za - can be inchoative, resultant, has a variety of spatial uses
and several other meanings.
Frost did not rely on large lexical databases, focusing on the comparison
between several European languages and Semitic languages.
2312 Behav Res (2018) 50:23052315
numbers are slightly higher (0.5 in English and 0.6 in French
for both neighbor types).
The results for transposition, addition, and deletion lemma
neighbors are available for the GreekLex database (Ktori, van
Heuven, & Pitchford, 2008). To have a valid comparison, we
calculated the relevant values for a subcorpus including
35,304 lemmas, as in the GreekLex project. The proportion
of lemmas that have at least one transposition neighbor is 2.0
% in this subcorpus, whereas in GreekLex it is only 0.6 %. For
addition and deletion neighbors, the figures are 4.6 % and 6.4
% for Russian and 8.0 % and 9.7 % for Greek, respectively.
Other results presented in Tables 3and 4cannot be sub-
jected to cross-linguistic comparisons because the relevant
data are not available for other languages.
The StimulStat database presented in this paper may be useful
for linguists, psychologists and other scientists conducting
experimental research on Russian. It is the first lexical data-
base of this type created for Russian. It contains more than
52,000 lemmas and more than 1.7 million word forms and
features more parameters than most databases created for oth-
er languages, including frequency, length, stress, syllabic
structure, phonemic transcription, uniqueness point, as well
as other parameters related to orthographic and phonological
representations, various grammatical properties, orthographic
and phonological neighborhood characteristics, homonymy,
polysemy and subjective parameters: subjective age of acqui-
sition and imageability. We took some parameters from vari-
ous sources and computed the others ourselves.
StimulStat is freely available as a web application, so users
do not need to buy and install any specialized software. In the
future, we plan to add ideal phonological transcription for all
forms included in the database, to recalculate all relevant sta-
tistics and to include the option to search for homophonous
forms, i.e. the forms that have the same phonological repre-
sentations, but different spellings. We also plan to develop a
tool for generating nonce words withcertain properties and for
computing required properties for the list of nonce words
uploaded by the user.
Acknowledgements This project is funded by the grant 14-04-12034
from the Russian Foundation for Humanities.
Acha, J., Laka, I., Landa, J., & Salaburu, P. (2014). EHME: A new word
database for research in Basque language. The Spanish Journal of
Psychology,17, E79.
Adelman, J. S., Marquis,S. J., Sabatos-DeVito, M. G., & Estes, Z. (2013).
The unexplained nature of reading. Journal of Experimental
Psychology: Learning, Memory, and Cognition,39(4), 1037.
Akinina, Y., Malyutina, S., Ivanova, M., Iskra, E., Mannova, E., &
Dragoy, O. (2014). Russian normative data for 375 action pictures
and verbs. Behavior Research Methods,47(3), 691707. https://doi.
Andrews, S. (1996). Lexical retrieval and selection processes: Effects of
transposed-letter confusability. Journal of Memory and Language,
35(6), 775800.
Andrews, S. (1997a). The effect of orthographic similarity on lexical
retrieval: Resolving neighborhood conflicts. Psychonomic Bulletin
& Review,4(4), 439461.
Andrews, S. (1997b). The effect of orthographic similarity on lexical
retrieval: Resolving neighborhood conflicts. Psychonomic Bulletin
& Review,4(4), 439461.
Arciuli, J., & Cupples, L. (2006). The processing of lexical stress during
visual word recognition: Typicality effects and orthographic corre-
lates. The Quarterly Journal of Experimental Psychology,59(05),
Armstrong, B. C., Tokowicz, N., & Plaut, D. C. (2012). eDom: Norming
software and relative meaning frequencies for 544 English hom-
onyms. Behavior Research Methods,44(4), 10151027. https://
Ashby, J., & Rayner, K. (2004). Representing syllable information during
silent reading: Evidence from eye movements. Language &
Cognitive Processes,19(3), 391426.
Baayen, R. H., Dijkstra, T., & Schreuder, R. (1997). Singulars and plurals
in Dutch: Evidence for a parallel dual-route model. Journal of
Memory and Language,37(1), 94117.
Balota, D. A., Yap, M. J., Hutchison, K. A., Cortese, M. J., Kessler, B.,
Loftis, B., & Treiman, R. (2007). The English Lexicon Project.
Behavior Research Methods,39(3), 445459.
Bates, E., DAmico, S., Jacobsen, T., Székely, A., Andonova, E.,
Devescovi, A., Tzeng, O. (2003). Timed picture naming in seven
languages. Psychonomic Bulletin & Review, 10,344380. https://
Beretta, A., Fiorentino, R., & Poeppel, D. (2005). The effects of homon-
ymy and polysemy on lexical access: An MEG study. Cognitive
Brain Research,24(1), 5765.
Tabl e 4 The size of orthographic neighborhoods of different types per
lemma or word form in StimulStat (for every neighborhood type, only
lemmas and forms that have neighbors of this type are taken into account)
Lemmas Word forms
Mean SD Range Mean SD Range
sns 3.9 3.2 229 4.8 2.4 242
tns 2.1 0.3 232.10.325
ans 2.5 1.7 230 2.9 1.3 226
dns 2.1 0.3 242.20.426
pns 3.9 3.2 218 N/A
wns 10.3 61.3 23,464 N/A
bins 62.9 153.2 23,099 1014.2 2759.5 282760
trins 13.7 36.0 21179 157.4 535.1 230,396
The information is not available because the relevant parameters are
calculated online for every query and are not stored in the database
Behav Res (2018) 50:23052315 2313
Bocharov, V. V., Alexeeva, S. V., Granovsky, D. V., Protopopova, E. V.,
Stepanova, M. E., & Surikov, A. V. (2013). Crowdsourcing mor-
phological annotation. In V. P. Selegey (Ed.), Computational
Linguistics and Intellectual Technologies. Vol. 12 (pp. 109114).
Moscow: RGGU. [In Russian].
Bondarko, L. V. (1977). Zvukovoj stroj sovremennogo russkogo jazyka
[Sound system of the modern Russian language]. Moscow:
Prosveschenie. [In Russian].
Boudelaa, S., & Marslen-Wilson, W. D. (2010). Aralex: A lexical data-
base for Modern Standard Arabic. Behavior Research Methods,
42(2), 481487.
Bowers, J. S., Davis, C. J., & Hanley, D. A. (2005). Automatic semantic
activation of embedded words: Is there a Bhat^in Bthat^?Journal of
Memory and Language,52(1), 131143.
Carreiras, M., & Grainger, J. (2004). Sublexical representations and
the Bfront end^of visual word recognition. Language &
Cognitive Processes,19(3), 321331.
Colombo, L. (1992). Lexical stress effect and its interaction with frequen-
cy in word pronunciation. Journal of Experimental Psychology:
Human Perception and Performance,18(4), 9871003. https://doi.
Coltheart, M. (1981). The MRC psycholinguistic database. The Quarterly
Journal of Experimental Psychology, Section A,33(4), 497505.
Coltheart, M., Davelaar, E., Jonasson, T., & Besner, D. (1977). Access to
the internal lexicon. In S. Dornic (Ed.), Attention and Performance
VI (pp. 535555). New York: Academic Press.
Davis, C. J. (2005). N-Watch: A program for deriving neighborhood size
and other psycholinguistic statistics. Behavior Research Methods,
37(1), 6570.
Davis, C. J., & Perea, M. (2005). BuscaPalabras: A program for deriving
orthographic and phonological neighborhood statistics and other
psycholinguistic indices in Spanish. Behavior Research Methods,
37(4), 665671.
Davis, C. J., Perea, M., & Acha, J. (2009). Re(de)fining the orthographic
neighborhood: The role of addition and deletion neighbors in lexical
decision and reading. Journal of Experimental Psychology: Human
Perception and Performance, 35(5), 15501570.
Duchon, A., Perea, M., Sebastián-Gallés, N., Martí, A., & Carreiras, M.
(2013). EsPal: One-stop shopping for Spanish word properties.
Behavior Research Methods,45(4), 12461258.
Duyck, W., Desmet, T., Verbeke, L. P. C., & Brysbaert, M. (2004).
WordGen: A tool for word selection and nonword generation in
Dutch, English, German, and French. Behavior Research Methods,
Instruments, & Computers,36(3), 488499.
Efremova, T. (2000). Novyj slovarrusskogo jazyka. Tolkovo-
slovoobrazovatelnyj [The new explanatory dictionary of Russian
language]. Moscow: Russkij jazyk. [In Russian].
Erten, B., Bozsahin, C., & Zeyrek, D. (2014). Turkish resources for visual
word recognition. In The LREC 2014 Proceedings (pp. 21062110).
Retrieved from
Estivalet, G. L., & Meunier, F. (2015). The Brazilian Portuguese Lexicon:
An instrument for psycholinguistic research. PLoS ONE,10(12),
Frost, R. (2012). Towards a universal model of reading. Behavioral and
Brain Sciences,35,263279.
Frost, R. (2015). Cross-linguistic perspectives on letter-order pro-
cessing: Empirical findings and theoretical considerations. In
The Oxford Handbook of Reading (pp. 8898). Oxford:
Oxford University Press.
Heister, J., Würzner, K. -M., Bubenzer, J., Pohl, E., Hanneforth, T.,
Geyken, A., & Kliegl, R. (2011). dlexDB eine lexikalische
Datenbank für die psychologische und linguistische Forschung.
[dlexDB a lexical database for psychological and linguistic re-
search]. Psychologische Rundschau,62(1), 1020. [In German].
Keuleers, E., Lacey, P., Rastle, K., & Brysbaert, M. (2012). The British
Lexicon Project: Lexical decision data for 28,730 monosyllabic and
disyllabic English words. Behavior Research Methods,44(1), 287
Kinoshita, S. (2015). Visual word recognition in the Bayesian reader
framework. In The Oxford Handbook of Reading (pp. 6375).
Oxford: Oxford University Press.
Kliegl, R., Grabner, E., Rolfs, M., & Engbert, R. (2004). Length, frequen-
cy, and predictability effects of words on eye movements in reading.
European Journal of Cognitive Psychology,16(12), 262284.
Kliegl, R., Nuthmann, A., & Engbert, R. (2006). Tracking the mind
during reading: The influence of past, present, and future words on
fixation durations. Journal of Experimental Psychology: General,
Korobov, M. (2015). Morphological analyzer and generator for Russian
and Ukrainian languages. In Khachay, M. Y., Konstantinova, N.,
Panchenko, A., Ignatov, D. I., & Labunets, V. G. (Eds.), Analysis
of images, social networks and texts (pp. 320332). Berlin: Springer.
Ktori, M., van Heuven, W. J., & Pitchford, N. J. (2008). GreekLex: A
lexical database of Modern Greek. Behavior Research Methods,
40(3), 773783.
Kučera, H., & Francis, W. N. (1967). Computational analysis of present-
day American English. Providence: Brown University Press.
Kwantes, P. J., & Mewhort, D. J. K. (1999). Evidence for sequential
processing in visual word recognition. Journal of Experimental
Psychology: Human Perception and Performance,25(2), 376
Kyparissiadis, A., van Heuven, W. J., Pitchford, N. J., & Ledgeway, T.
(2017). GreekLex 2: A comprehensive lexical database with part-of-
speech, syllabic, phonological, and stress information. PLoS ONE,
12(2), e0172493.
Lindell, A. K., Nicholls, M. E., & Castles, A. E. (2003). The effect of
orthographic uniqueness and deviation points on lexical decisions:
Evidence from unilateral and bilateral-redundant presentations. The
Quarterly Journal of Experimental Psychology: Section A,56(2),
Lyashevskaya, O. (2013). Chastotnyj leksiko-grammaticheskij slovar:
Prospect proekta [Lexico-grammatical frequency dictionary: A pre-
liminary design]. In V. P. Selegey (Ed.), Computational Linguistics
and Intellectual Technologies. Vol. 12 (pp. 478489). Moscow:
RGGU. [In Russian].
Lyashevskaya, O., & Sharov, S. (2009). Chastotnyj slovarsovremennogo
russkogo jazyka (na materialakh Nacionalnogo korpusa russkogo
jazyka) [The frequency dictionary of modern Russian language
based on Russian National Corpus]. Moscow: Azbukovnik. [In
Marian, V., Bartolotti, J., Chabal, S., & Shook, A. (2012). CLEARPOND:
Cross-Linguistic Easy-Access Resource for Phonological and
Orthographic Neighborhood Densities. PLoS ONE,7(8), e43230.
Marslen-Wilson, W., & Tyler, L. K. (1980). The temporal structure of
spoken language understanding. Cognition,8(1), 171. https://doi.
Mirman, D., Strauss, T. J., Dixon, J. A., & Magnuson, J. S. (2010). Effect
of representational distance between meanings on recognition of
ambiguous spoken words. Cognitive Science,34(1), 161173.
Monsell, S. (1991). The nature and locus of word frequency effects in
reading. In D. Besner & G. W. Humphreys (Eds.), Basic processes
2314 Behav Res (2018) 50:23052315
in reading: Visual word recognition (pp. 148197). Hillsdale:
Monsell, S., Doyle, M. C., & Haggard, P. N. (1989). Effects of frequency
on visual word recognition tasks: Where are they? Journal of
Experimental Psychology: General,118 (1), 4371.
New, B., Ferrand, L., Pallier, C., & Brysbaert, M. (2006). Reexamining
the word length effect in visual word recognition: New evidence
from the English Lexicon Project. Psychonomic Bulletin and
Review,13(1), 4552.
New, B., Pallier, C., Brysbaert, M., & Ferrand, L. (2004). Lexique 2: A
new French lexical database. Behavior Research Methods,
Instruments, & Computers,36(3), 516524.
Oldfield, R. C., & Wingfield, A. (1965). Response latencies in nam-
ing objects. Quarterly Journal of Experimental Psychology,
17(4), 273281.
Perea, M. (2015). Neighborhood effects in visual word recognition and
reading. In The Oxford Handbook of Reading (p. 76). Oxford:
Oxford University Press.
Perea,M.,&Lupker,S.J.(2003). Does judge activate COURT?
Transposed-letter similarity effects in masked associative priming.
Memory & Cognition,31(6), 829841.
Perea, M., Urkia, M., Davis, C. J., Agirre, A., Laseka, E., & Carreiras, M.
(2006). E-Hitz: A word frequency list and a program for deriving
psycholinguistic statistics in an agglutinative language (Basque).
Behavior Research Methods,38(4), 610615.
Rayner, K. (1998). Eye movements in reading and information process-
ing: 20 Years of Research. Psychological Bulletin,124(3), 372422.
Rodd, J. M., Gaskell, M. G., & Marslen-Wilson, W. D. (2004).
Modelling the effects of semantic ambiguity in word recogni-
tion. Cognitive Science,28(1), 89104.
Schiller, N. O., Fikkert, P., & Levelt, C. C. (2004). Stress priming in
picture naming: An SOA study. Brain and Language,90(1),
Segalovich, I. (2003). A fast morphological algorithm with unknown
word guessing induced by a dictionary for a web search engine. In
Proceedings of the MLMTA 2003 (pp. 273280). Las Vegas:
CSREA Press.
Skrelin, P., Volskaya, N., Kocharov, D., Evgrafova, K., Glotova, O., &
Evdokimova, V. (2010). A fully annotated corpus of Russian
speech. In The LREC 2010 Proceedings (pp. 109112). Ret rieved
Taft, M. (1979). Recognition of affixed words and the word frequency
effect. Memory & Cognition,7(4), 263272.
Taft, M., & Forster, K. I. (1976). Lexical storage and retrieval of
polymorphemic and polysyllabic words. Journal of Verbal
Learning and Verbal Behavior,15(6), 607620.
Yap, M. J., & Balota, D. A. (2015). Visual word recognition. In The
Oxford Handbook of Reading (pp. 2643). Oxford: Oxford
University Press.
Yap, M. J., Liow, S. J. R., Jalil, S.B., & Faizal, S. S. B. (2010). The Malay
Lexicon Project: A database of lexical statistics for 9,592 words.
Behavior Research Methods,42(4), 9921003.
Zaliznjak, A. A. (1987). Grammaticheskij slovarrusskogo jazyka
[Grammatical dictionary of the Russian Language]. 3
Moscow: Russkij jazyk. [In Russian].
Behav Res (2018) 50:23052315 2315
... on a separate predictability norming study using a cloze task (Laurinavichyute et al., 2019). Wordform frequencies were borrowed from the "Frequency grammar of Russian" project (Lyashevskaya, 2013) via the StimulStat database (Alexeeva et al., 2018). They were calculated after the morphological disambiguation of each word was completed (Laurinavichyute et al., 2019). ...
Full-text available
The current study aims to test the assumption that a specially designed Cyrillic font, LexiaD, can assist adolescents with persistent reading problems and facilitate their reading experience. LexiaD was compared with the widely used Arial font. Two groups of adolescents with dyslexia (N = 34) and without dyslexia (N = 28) silently read 144 sentences from the Russian Sentence Corpus (Laurinavichyute et al., 2019), some of which were presented in LexiaD, and others in Arial, while their eye movements were recorded. LexiaD did not show the desired effect for adolescents at the beginning of the experiment: Arial outperformed it in reading speed in both participant groups. By the end of the experiment, LexiaD outperformed Arial, and although the speed of the higher-level cognitive processing (e.g., lexical access) in both fonts did not differ significantly, the feature extraction was found to be better in LexiaD than in Arial. Thus, we found some positive effect of LexiaD when participants with and without dyslexia got accustomed to it. A follow-up study with an explicit exposure session is needed to confirm this conclusion.
... Adjective-noun phrases used as stimuli were created in the following way. At first, using the StimulStat database 31 , we selected pairs of nouns that differed only by one letter, as in vino 'wine' and kino 'movie' . All nouns were 4-5 letters long, their frequency ranged from 5 to 100 ipm (according to the frequency dictionary 32 ). ...
Full-text available
Brain systems dealing with multiple meanings of ambiguous stimuli are relatively well studied, while the processing of non-selected meanings is less investigated in the neurophysiological literature and provokes controversy between existing theories. It is debated whether these meanings are actively suppressed and, if yes, whether suppression characterizes any task that involves alternative solutions or only those tasks that emphasize semantic processing or the existence of alternatives. The current functional MRI event-related study used a modified version of the word fragment completion task to reveal brain mechanisms involved in implicit processing of the non-selected solutions of ambiguous fragments. The stimuli were pairs of fragmented adjectives and nouns. Noun fragments could have one or two solutions (resulting in two words with unrelated meanings). Adjective fragments had one solution and created contexts strongly suggesting one solution for ambiguous noun fragments. All fragmented nouns were presented twice during the experiment (with two different adjectives). We revealed that ambiguity resolution was associated with a reduced BOLD signal within several regions related to language processing, including the anterior hippocampi and amygdala and posterior lateral temporal cortex. Obtained findings were interpreted as resulting from brain activity inhibition, which underlies a hypothesized mechanism of suppression of non-selected solutions.
... For each of them, we chose 20 five-letter words, so that each target letter would appear in all five possible positions four times. These words were chosen from Chastotnyj slovar' sovremennogo russkogo jazyka (Frequency dictionary of the modern Russian language) (Lyashevskaya & Sharov, 2009), using the StimulStat database (Alexeeva et al., 2018). This dictionary is based on the subcorpus of the Russian National Corpus ( of 92 million words and contains 52,139 lemmas or base forms of Russian words. ...
Full-text available
Prior research has suggested that the identification and encoding of letter positions within letter strings might be influenced by orthography. Letters in transparent languages (e.g., Greek) with regular grapheme-to-phoneme correspondences are processed sequentially, whereas letters in deep languages (e.g., English) are processed in parallel. In three experiments, we used a visual search paradigm to test this hypothesis on Russian—a relatively transparent language. In Experiment 1, we measured the identification speed of Cyrillic letters at each position in the five-element real words or pronounceable pseudowords. In Experiment 2, the performance was compared to random letter strings, and in Experiment 3, to non-linguistic symbol strings. Our results reveal a search pattern similar to English, excluding strictly serial letter computation, which is inconsistent with the orthography hypothesis. Moreover, we showed that the lexical status and the nature of the string (linguistic/non-linguistic) affect response times for Russian and therefore must be accounted for in models of visual word recognition.
... Adjective-noun phrases used as stimuli were created in the following way. At first, using the StimulStat database 15 , we selected pairs of nouns that differed only by one letter, as in vino 'wine' and kino 'movie'. All nouns were 4-5 letters long, their frequency ranged from 5 to 100 ipm (according to the frequency dictionary 16 . ...
Full-text available
Dealing with ambiguity, one usually selects one meaning unconsciously and remains unaware of the alternative meanings. The brain systems dealing with multiple meanings of ambiguous stimuli are relatively well studied, while the brain processing of their non-selected meanings is relatively less investigated. The current functional MRI event-related study used a modified version of the word fragment completion task to reveal possible brain mechanisms involved in processing the non-selected meaning of ambiguous stimuli. Some noun stimuli were ambiguous, and the others were not. Adjectives created contexts strongly biasing the choice of the noun meaning in one or the other way. All ambiguous and unambiguous noun stimuli were presented twice during the experiment. It was revealed that ambiguity resolution was associated with a decrement in the BOLD signal within the right and left hippocampi. This finding supported one of the tested hypotheses assumed that non-selected meanings are actively suppressed. The similarity between this result and BOLD signal changes observed for suppression-induced forgetting for purging unwanted memories from awareness allows suggesting the general neurophysiological basis for voluntary and automatic inhibitory awareness control.
... Неидентичные праймы были отобраны при помощи базы данных StimulStat, позволяющей искать слова по различным лингвистическим характеристикам, включая длину, частотность, первую и последнюю буквы, часть речи и др. [Alexeeva et al., 2017]. ...
Full-text available
The book addresses the study of reading mechanisms and visual perception using the method of eye-tracking. The authors conduct their research in the Laboratory for Cognitive Studies of Saint Petersburg State University that was the first in Russia to utilize eye-tracking in experimental studies of language more than 15 years ago. Psycholinguistic experiments aimed at analyzing reading in Russian are described; a systematic description of factors which influence reading process at different stages and linguistic levels from a single letter to a coherent text is provided. The mechanisms of separate graphemes recognition, activation and competition processes in word recognition, contextual integration processes, syntactic parsing, and anaphora resolution in eye-tracking experiments are described. The influence of the text type on its processing is demonstrated, the advantages and disadvantages of the infographics compared to verbal text are described, the relationship between visual perception of images and their verbalization is discussed. The significant role of context is highlighted: on the one hand, it helps the reader to anticipate new information, and on the other hand, it enables different multiple interpretations of a sentence or text. In the book a virtual assistant — Schroedinger’s cat — is used as a model of a subject who processes and transmits information. The book will appeal to linguists and cognitive psychologists who use eye-tracking in their research, as well as for the wide range of those who are interested in objective methods of studying language processing and human behavior.
... Затем, пользуясь процедурой, описанной в работе [Ratcliff 1993], мы рассчитали для каждого 2 Для подбора пары использовалась база данных StimulStat (http://stimul.cognitivestudies. ru/ru_stimul/, [Alexeeva et al. 2018]), данные о частотности лемм здесь и далее даны по словарю [Lyashevskaya, Sharov 2009]. экспериментального условия пороговые значения времени реакции (среднее время реакции плюс или минус 2,5 стандартных отклонения) и удалили все данные за пределами этих пороговых значений, а именно, 40 случаев, что составляет 1,7 % от общего количества. ...
Conference Paper
Full-text available
Even if we know how to spell, we often see words misspelled by other people — especially nowadays when we constantly read unedited texts on social media and in personal messages. In this paper, we present two experiments showing that the incidence of orthographic errors reduces the quality of lexical representations in the mental lexicon—even if one knows how to spell a word, repeated exposure to incorrect spellings blurs its orthographical representation and weakens the connection between form and meaning. As a result, it is more difficult to judge whether the word is spelled correctly, and — more surprisingly — it takes more time to read the word even when there are no errors. We show that when all other factors are balanced the effect of misspellings is more pronounced for the words with lower frequency. We compare our results with the only previous study addressing the problem of misspellings’ influence on the processing of correctly spelled words — it was conducted on the English data. It may be interesting to explore this issue in a cross-linguistic perspective. In this study, we turn to Russian, which differs from English by a more transparent orthography. Much larger corpora of unedited texts are available for English than for Russian, but, using a different way to estimate the incidence of misspellings, we obtained similar results and could also make some novel generalizations. In Experiment 1 we selected 44 words that are frequently misspelled and presented in two conditions (with or without spelling errors) and were distributed across two experimental lists. For every word, participants were asked to determine whether it is spelled correctly or not. The frequency of the word and the relative frequency of its misspelled occurrences significantly influenced the number of incorrect responses: not only it takes longer to read frequently misspelled words, it is also more difficult to decide whether they are spelled correctly. In Experiment 2 we selected 30 words from the materials of Experiment 1 and for every selected word, we found a pair that is matched for length and frequency, but is rarely misspelled due to its orthographic transparency. We used a lexical decision task, presenting these 60 words in the correct spelling, as well as 60 nonwords. We used LMMs for statistics. Firstly, the word type factor was significant: it takes more time to recognize a frequently misspelled word, which replicates the results obtained for English. Secondly, the interaction between the word type factor and the frequency factor was significant: the effect of misspellings was more pronounced for the words of lower frequency. We can conclude that high frequency words have more robust representations that resist blurring more efficiently than low frequency ones. Finally, we conducted a separate analysis showing that the number of incorrect responses in Experiment 1 correlates with RTs in Experiment 2. Thus, whether we consciously try to find an error or simply read words orthographic representations blurred due to exposure to frequent misspellings make the task more difficult.
... We also checked if grammatically adapted texts contain less frequent words than lexically adapted (see Fig. 1). The word frequency was checked via the Russian linguistic database "StimulStat Project" [2]. ...
Full-text available
This article presents the results of an eye-tracking experiment on Russian language material, exploring the reading process in secondary school children with general speech underdevelopment. The objective of the study is to reveal what type of a text is better to use to make the reading and comprehension easier: lexically adapted text or grammatically adapted text? The data from Russian-speaking participants from the compulsory school (experimental group) and 28 secondary school children with normal speech development (control group) indicate that both types of adaptation proved to be efficient for recalling the information from the text. Though, we revealed that in teenagers with language disorders in anamnesis lower perceptual processes are partially compensated (parameters of eye movements), but higher comprehension processes remain affected.
Full-text available
Spelling errors are ubiquitous in all writing systems. Most studies exploring spelling errors focused on the phonological plausibility of errors. However, unlike typical pseudohomophones, spelling errors occur in naturally produced written language. We investigated the time course of recognition of the most frequent orthographic errors in Russian (error in an unstressed vowel in the root) and the effect of word frequency on this process. During event-related potentials (ERP) recording, 26 native Russian speakers silently read high-frequency correctly spelled words, low-frequency correctly spelled words, high-frequency words with errors, and low-frequency words with errors. The amplitude of P200 was more positive for correctly spelled words than for misspelled words and did not depend on the frequency of the words. In addition, in the 350–500-ms time window, we found a more negative response for misspelled words than for correctly spelled words in parietal–temporal-occipital regions regardless of word frequency. Considering our results in the context of a dual-route model, we concluded that recognizing misspelled high-frequency and low-frequency words involves common orthographic and phonological processes associated with P200 and N400 components such as whole word orthography processing and activation of phonological representations correspondingly. However, at the 500–700 ms stage (associated with lexical-semantic access in our study), error recognition depends on the word frequency. One possible explanation for these differences could be that at the 500–700 ms stage recognition of high-frequency misspelled and correctly spelled words shifts from phonological to orthographic processes, while low-frequency misspelled words are accompanied by more prolonged phonological activation. We believe these processes may be associated with different ERP components P300 and N400, reflecting a temporal overlap between categorization processes based on orthographic properties for high-frequency words and phonological processes for low-frequency words. Therefore, our results complement existing reading models and demonstrate that the neuronal underpinnings of spelling error recognition during reading may depend on word frequency.
Full-text available
Databases containing lexical properties on any given orthography are crucial for psycholinguistic research. In the last ten years, a number of lexical databases have been developed for Greek. However, these lack important part-of-speech information. Furthermore, the need for alternative procedures for calculating syllabic measurements and stress information, as well as combination of several metrics to investigate linguistic properties of the Greek language are highlighted. To address these issues, we present a new extensive lexical database of Modern Greek (GreekLex 2) with part-of-speech information for each word and accurate syllabification and orthographic information predictive of stress, as well as several measurements of word similarity and phonetic information. The addition of detailed statistical information about Greek part-of-speech, syllabification, and stress neighbourhood allowed novel analyses of stress distribution within different grammatical categories and syllabic lengths to be carried out. Results showed that the statistical preponderance of stress position on the pre-final syllable that is reported for Greek language is dependent upon grammatical category. Additionally, analyses showed that a proportion higher than 90% of the tokens in the database would be stressed correctly solely by relying on stress neighbourhood information. The database and the scripts for orthographic and phonological syllabification as well as phonetic transcription are available at
Full-text available
In this article, we present the Brazilian Portuguese Lexicon, a new word-based corpus for psycholinguistic and computational linguistic research in Brazilian Portuguese. We describe the corpus development, the specific characteristics on the internet site and database for user access. We also perform distributional analyses of the corpus and comparisons to other current databases. Our main objective was to provide a large, reliable, and useful word-based corpus with a dynamic, easy-to-use, and intuitive interface with free internet access for word and word-criteria searches. We used the Núcleo Interinstitucional de Linguística Computacional's corpus as the basic data source and developed the Brazilian Portuguese Lexicon by deriving and adding metalinguistic and psycholinguistic information about Brazilian Portuguese words. We obtained a final corpus with more than 30 million word tokens, 215 thousand word types and 25 categories of information about each word. This corpus was made available on the internet via a free-access site with two search engines: a simple search and a complex search. The simple engine basically searches for a list of words, while the complex engine accepts all types of criteria in the corpus categories. The output result presents all entries found in the corpus with the criteria specified in the input search and can be downloaded as a.csv file. We created a module in the results that delivers basic statistics about each search. The Brazilian Portuguese Lexicon also provides a pseudoword engine and specific tools for linguistic and statistical analysis. Therefore, the Brazilian Portuguese Lexicon is a convenient instrument for stimulus search, selection, control, and manipulation in psycholinguistic experiments, as also it is a powerful database for computational linguistics research and language modeling related to lexicon distribution, functioning, and behavior.
Conference Paper
Full-text available
pymorphy2 is a morphological analyzer and generator for Russian and Ukrainian languages. It uses large efficiently encoded lexi- cons built from OpenCorpora and LanguageTool data. A set of linguistically motivated rules is developed to enable morphological analysis and generation of out-of-vocabulary words observed in real-world documents. For Russian pymorphy2 provides state-of-the-arts morphological analysis quality. The analyzer is implemented in Python programming language with optional C++ extensions. Emphasis is put on ease of use, documentation and extensibility. The package is distributed under a permissive open-source license, encouraging its use in both academic and commercial setting.
Full-text available
The present article introduces a Russian-language database of 375 action pictures and associated verbs with normative data. The pictures were normed for name agreement, conceptual familiarity, and subjective visual complexity, and measures of age of acquisition, imageability, and image agreement were collected for the verbs. Values of objective visual complexity, as well as information about verb frequency, length, argument structure, instrumentality, and name relation, are also provided. Correlations between these parameters are presented, along with a comparative analysis of the Russian name agreement norms and those collected in other languages. The full set of pictorial stimuli and the obtained norms may be freely downloaded from for use in research and for clinical purposes.
The orthographic uniqueness point (OUP) of a word is introduced as the position of the Ist letter, reading from left to right, that distinguishes a word from all other printed words. In 3 experiments, observers named words that had early versus late OUPs. With unlimited viewing time, early-OUP words were named faster than late-OUP words. The effect disappeared in a delayed-naming task; hence, it was not associated with response production. The effect remained when exposure duration was reduced to limit eye movements. Results indicate that observers process; the letters of a word in left-to-right order, contrary to strictly parallel accounts of word identification.
Visual word recognition is an integral aspect of reading. Although readers are able to recognize visually presented words with apparent ease, the processes that map orthography onto phonology and semantics are far from straightforward. In the present chapter, we discuss the cognitive processes that skilled readers use in order to recognize and pronounce individual words. After a historical overview of the broad theoretical developments in this rich field, we provide a description of methods and a selective review of the empirical literature, with an emphasis on how the recognition of an isolated word is modulated by its lexical- and semantic-level properties and by its context. Finally, we briefly consider some recent approaches and analytic tools in visual word recognition research, including megastudies, analysis of response time distributions, and the important role of individual differences.
Mit der lexikalischen Datenbank dlexDB stellen wir der psychologischen und linguistischen Forschung im World Wide Web online statistische Kennwerte für eine Vielzahl von verarbeitungsrelevanten Merkmalen von Wörtern zur Verfügung. Diese Kennwerte umfassen die durch CELEX (Baayen, Piepenbrock und Gulikers, 1995) bekannten Variablen der Häufigkeiten von Wortformen und Lemmata in Texten geschriebener Sprache. Darüber hinaus berechnen wir eine Reihe neuer Kennwerte wie die Häufigkeiten von Silben, Morphemen, Zeichenfolgen und Mehrwortverbindungen sowie Wortähnlichkeitsmaße. Die Datengrundlage bildet das Kernkorpus des Digitalen Wörterbuchs der deutschen Sprache (DWDS) mit über 100 Millionen laufenden Wörtern. Wir illustrieren die Validität dieser Kennwerte mit neuen Ergebnissen zu ihrem Einfluss auf Fixationsdauern beim Lesen von Sätzen.
This article presents EHME, the frequency dictionary of Basque structure, an online program that enables researchers in psycholinguistics to extract word and nonword stimuli, based on a broad range of statistics concerning the properties of Basque words. The database consists of 22.7 million tokens, and properties available include morphological structure frequency and word-similarity measures, apart from classical indexes: word frequency, orthographic structure, orthographic similarity, bigram and biphone frequency, and syllable-based measures. Measures are indexed at the lemma, morpheme and word level. We include reliability and validation analysis. The application is freely available, and enables the user to extract words based on concrete statistical criteria, as well as to obtain statistical characteristics from a list of words.