Domain-independent term extraction through domain modelling

Abstract

Extracting general or intermediate level terms is a relevant problem that has not received much attention in literature. Cur-rent approaches for term extraction rely on contrastive corpora to identify domain-specific terms, which makes them better suited for specialised terms, that are rarely used outside of the domain. In this work, we propose an alternative measure of domain specificity based on term coherence with an automatically constructed domain model. Although previous systems make use of domain-independent features, their performance varies across domains, while our approach displays a more stable be-haviour, with results comparable to, or better than, state-of-the-art methods.

Full text

Domain-independent term extraction through domain modelling
Georgeta Bordea
UNLP, DERI
National University
of Ireland, Galway
name.surname
at deri.org
Paul Buitelaar
UNLP, DERI
National University
of Ireland, Galway
name.surname
at deri.org
Tamara Polajnar
Computer Laboratory
University of Cambridge
name.surname
at cl.cam.ac.uk
Abstract
Extracting general or intermediate level
terms is a relevant problem that has not re-
ceived much attention in literature. Cur-
rent approaches for term extraction rely
on contrastive corpora to identify domain-
specific terms, which makes them better
suited for specialised terms, that are rarely
used outside of the domain. In this work,
we propose an alternative measure of do-
main specificity based on term coherence
with an automatically constructed domain
model. Although previous systems make
use of domain-independent features, their
performance varies across domains, while
our approach displays a more stable be-
haviour, with results comparable to, or bet-
ter than, state-of-the-art methods.
Term extraction plays an important role in a
wide range of applications including information
retrieval (Yang et al., 2005), keyphrase extrac-
tion (Lopez and Romary, 2010), information ex-
traction (Yangarber et al., 2000), domain ontol-
ogy construction (Kietz et al., 2000), text classi-
fication (Basili et al., 2002), and knowledge min-
ing (Mima et al., 2006). In many of these ap-
plications the specificity level of a term is a rel-
evant characteristic, but despite the large body of
work in term extraction there are few methods that
are able to identify general terms or intermediate
level terms. Take for example the following struc-
ture from the AGROVOC vocabulary1:resources
→natural resources →mineral resources →lig-
nite, where resources is an upper level term, natu-
ral resources and mineral resources are intermedi-
1AGROVOC: http://aims.fao.org/
standards/agrovoc/about
ate level terms, and lignite is a leaf. Intermediate
level terms are specific to a domain but are broad
enough to be usable for summarisation and clas-
sification. Methods that make use of contrastive
corpora to select domain specific terms favour the
leaves of the hierarchy, and are less sensitive to
generic terms that can be used in other domains.
Instead, we construct a domain model by iden-
tifying upper level terms from a domain corpus.
This domain model is further used to measure the
coherence of a candidate term within a domain.
The underlying assumption is that top level terms
(e.g., resource) can be used to extract intermedi-
ate level terms, in our example natural resources
and mineral resources. Our method for construct-
ing a domain model is evaluated directly through
an expert survey as well as indirectly based on its
contribution to intermediate level term extraction.
While domain modelling is tested and exemplified
with English, the ideas presented here are not lan-
guage dependent and can be applied to other lan-
guages, but this is outside the scope of this work.
We start by giving an overview of related work
in term extraction in Section 1. Then, an approach
to construct a domain model based on domain co-
herence is proposed in Section 2, followed by a
method to apply domain models for term extrac-
tion. The experimental part of the paper starts with
a direct evaluation of a domain model through a
user survey (Section 3). A first set of experiments
is carried in a standard setting for term evaluation,
while the second set of experiments is application-
driven, using corpora annotated for keyphrase ex-
traction, information extraction, and information
retrieval. We conclude this paper in Section 4, giv-
ing a few directions for future work.

1 Related work
Methods for term extraction that use corpus statis-
tics alone are faced with the challenge of distin-
guishing general language expressions (e.g., last
week) from terminological expressions. A solu-
tion to this problem is to use contrastive corpora
(Huizhong, 1986). Several contrastive measures
are proposed including domain relevance (Park
et al., 2002), domain consensus (Velardi et al.,
2001), and word impurity (Liu et al., 2005). In
this work we propose an approach to compute do-
main specificity based on a domain model, that is
less sensitive to leaf terms and is better suited for
intermediate level terms.
The domain model proposed in this work is de-
rived from the corpus itself, without the need for
external corpora. An automatic method for iden-
tifying the upper level terms of a domain has ap-
plications beyond the task of term extraction. Al-
though not named as such, upper level terms were
previously used for text summarisation (Teufel
and Moens, 2002). The authors manually identi-
fied a set of 37 nouns including theory,method,
prototype and algorithm, without considering a
principled approach to extract them. The work
presented here is similar to (Barri`
ere, 2007), but
instead of re-ranking terms based on their similar-
ity to each other we make use of domain model
terms, reducing data sparsity issues.
In our experiments we employ two state of the
art methods for term extraction, the NC-value ap-
proach (Frantzi et al., 2000) and TermExtractor2
(Velardi et al., 2001). The former is a hybrid
method that ranks terms using only corpus statis-
tics, while the latter exploits contrastive corpora.
NC-value is based on raw frequency counts and
considers nested multi-word terms by penalising
frequency counts of shorter embedded terms. Ad-
ditionally, it incorporates context information in
a re-ranking step using top ranked terms. Con-
text words (nouns, verbs and adjectives) are identi-
fied based on their occurrence with top candidates.
Our method is an extension of this approach that
uses domain models instead of selecting context
words based on frequency alone.
TermExtractor is a popular approach that com-
bines different term extraction techniques includ-
2TermExtractor demo: http://lcl.
uniroma1.it/sso/index.jsp?returnURL=
\%2Ftermextractor\%2F
ing domain relevance, domain consensus and lex-
ical cohesion. Domain Relevance (DR) compares
the probability of a term tin a given domain Di
with the maximum probability of the term in other
domains used for contrast Djand is measured as:
DRDi(t) = P(t/Di)
maxj(P(t/Dj)), j 6=i(1)
Domain Consensus (DC) identifies terms that
have an even probability distribution across the
corpus that represents a domain of interest, and is
estimated through entropy as follows:
DCDi(t) = −X
d∈Di
P(t/d)·log (P(t/d)) (2)
where dis a document in the domain Di. Fi-
nally, the degree of cohesion among the words wj
that compose the term tis computed through a
measure called Lexical Cohesion (LC). Let |t|be
the length of tin number of words, and f(t, Di)
the frequency of tin the domain Di, then Lexical
Cohesion is defined as:
LCDi(t) = |t| · f(t, Di)·log (f(t, Di))
Pwjf(wj, Di)(3)
The weight T E used for ranking terms by Ter-
mExtractor is a linear combination of the three
methods described above:
T E(t, Di) = α·DR +β·DC +γ·LC (4)
While general terms typically have a high do-
main consensus, the domain relevance measure
boosts narrow terms that have limited usage out-
side of the domain. For example the term system is
not identified as relevant for Computer Science be-
cause it is frequently used in general language and
in other specific domains as biology. In this work
we take a different approach to compute domain
specificity that can be applied for general terms by
using a domain coherence measure that does not
use external corpora. Two general purpose cor-
pora, the Open American National Corpus3and
a corpus of books from Project Gutenberg4, are
used as contrastive corpora for our implementa-
tion of TermExtractor. The books selected from
3Open American National Corpus: http://www.
americannationalcorpus.org/OANC/
4Project Gutenberg: http://www.gutenberg.
org/

Project Gutenberg include the bible, the complete
works of William Shakespeare, James Joyce’s
Ulysses and Tolstoy’s War and Peace. We con-
sider only the default setting of TermExtractor as-
signing equal weights to each measure in Equation
4.
2 Constructing a domain model based on
domain coherence
We begin this section by describing an approach
for domain modelling based on domain coherence
in Section 2.1. Then, we discuss a modification
of the NC-value approach which makes it better
suited for intermediate level terms (Section 2.2).
We conclude this section by describing a novel
method for term extraction using a domain model
in Section 2.3.
2.1 Domain modelling
A domain model is represented as a vector of
words which contribute to determine the domain
of the whole corpus. Let ∆be the domain model,
and w1to wna set of generic words, specific to
the domain, then:
∆ = {w1, ..., wn}(5)
The number of words ncan be empirically set
according to a cutoff associated weight. Previous
work on using domain information for word sense
disambiguation (Magnini et al., 2002) has shown
that only about 21% of the words in a text actu-
ally carry information about the prevalent domain
of the whole text, and that nouns have the most
significant contribution (79.4%). Several assump-
tions are made to identify words that are used to
construct a domain model from a domain corpus:
1. Distribution: Generic words should appear
in at least one quarter of the documents in the
corpus;
2. Length: Only single-word candidates are
considered, as longer terms are more specific;
3. Content: Only content-bearing words are of
interest (i.e., nouns, verbs, adjectives);
4. Semantic Relatedness: A term is more gen-
eral if it is semantically related to many spe-
cific terms.
The distribution assumption implies that rare
terms are more specific, similar with the
frequency-based measure previously used for
measuring tag generality (Benz et al., 2011). This
might not always be the case, for example a sim-
ple search with a search engine shows that arte-
fact or silverware are more rarely used than the
term spoon, although the first two concepts are
more generic. However, in this work we are in-
terested in extracting basic-level categories as the-
orised in psychology (Hajibayova, 2013). A basic-
level category is the preferred level of naming, that
is the taxonomical level at which categories are
most cognitively efficient. A counter example can
be found for the length assumption as well, as the
longer term inorganic matter is more general than
the single word knife, but in this case we would
simply consider as a candidate the single word
matter which is more generic than the compound
term. Both length and frequency of occurrence are
proposed as general criteria for identifying basic-
level categories (Green, 2005).
The first three assumptions are used for can-
didate selection, while the fourth assumption is
used to filter the candidates. A possible solution
for building a domain model is to use a standard
termhood measure for single-word terms. Most
approaches for extracting single-word terms make
use of contrastive corpora, ranking higher specific
words that are rarely used outside of the domain.
But our domain model is further used for term
extraction, therefore it is important that we use
generic words to insure a high recall.
We interpret coherence as semantic relatedness
to quantify the coherence of a term in a do-
main. The measure used for semantic relatedness
is Pointwise Mutual Information (PMI). First, we
extract multi-word terms using a standard term
extraction technique, then we use the top ranked
terms to filter candidate words using the following
scoring function for domain coherence:
s(θ) = X
σ∈Ω
P M I(θ, σ ) = X
σ∈Ω
log P(θ, σ)
P(θ)·P(σ)(6)
where θis the domain model candidate, σis top
ranked multi-word term, Ωis the set of top ranked
multi-word terms and P(θ, σ)is the probability
that the word θappears in the context of the term
σ. In our implementation, the set Ωcontains the
best terms extracted by our baseline term extrac-
tion method described in Section 2.2, but any other
term extraction method can be applied in this step.
A small sample from domain models extracted us-

Computer Science Biomed Food and Agriculture
development mechanism control
software evidence farm
framework antibody supply
information molecule food
system system forest
Table 1: Example words from domain models ex-
tracted for different domains
ing our domain coherence method for Computer
Science, Food and Agriculture, and the Biomedi-
cal Domain, is shown in Table 1.
2.2 Baseline term extraction method
Our baseline approach for intermediate level term
extraction is frequency-based, similar to the C-
value method (Ananiadou, 1994), but we mod-
ify its ranking function. The main difference
is the way we take into consideration embedded
terms. In previous work, this information is used
to decrease frequency counts, as shorter terms are
counted both when they appear by themselves and
when they are embedded in a longer term. We ar-
gue that the number of longer terms that embed a
term can be used as a termhood measure. In our
experiments, this measure only works for embed-
ded multi-word terms, as single-word terms are
too ambiguous. The baseline scoring method bis
defined as:
b(τ) = |τ|log f(τ) + αeτ(7)
where τis the candidate string, |τ|is the length
of τ,fis its frequency in the corpus, and eτis the
number of terms that embed the candidate string
τ. The parameter αis used to linearly combine
the embeddedness weight and is empirically set to
3.5 in our experiments.
2.3 Using domain coherence for term
extraction
Although we proposed a method to build a do-
main model in Section 2.1, the question of how
to use this domain model in a termhood measure
remains unanswered. Again, the solution is to
rely on the notion of domain coherence, which
is defined in this case as the semantic relatedness
between a candidate term and the domain model
described above. The assumption is that a cor-
rect term should have a high semantic relatedness
with representative words from the domain. This
method favours more generic candidates than con-
trastive corpora approaches, therefore it is better
suited for extracting intermediate level terms.
The same measure of semantic relatedness is
used as for the domain model, the PMI measure.
The domain coherence DC of a candidate string τ
is defined as follows:
DC(τ) = X
θ∈∆
P M I(τ , θ)(8)
where θis a word from the domain model, and
∆is the domain model constructed using Equation
6. Using generic terms to build the domain model
is crucial for ensuring a high recall as these words
are more frequently used across the corpus. In our
implementation context is defined as a window of
5 words.
3 Experiments and Results
Evaluating term extraction results across domains
is a challenge, because finding domain experts
is difficult for more than one domain. An al-
ternative is to reuse datasets annotated for appli-
cations where term extraction plays an important
role, for example, keyphrase extraction or index
term assignment. Three technical domain cor-
pora are used in our experiments: Krapivin, a cor-
pus of scientific publications in Computer Science
(Krapivin et al., 2009); GENIA, a corpus of ab-
stracts from the biomedical domain (Ohta et al.,
2001); and FAO, a corpus of reports about Food
and Agriculture (Medelyan and Witten, 2008) col-
lected from the website of the Food and Agricul-
ture Organization of the United Nations5. The
Krapivin corpus provides author and reviewer as-
signed keyphrases for each publication. The GE-
NIA corpus is exhaustively annotated with biomed
terms, with about 35% of all noun phrases anno-
tated as biomed terms. The FAO dataset provides
index terms assigned to each document by profes-
sional indexers. It is not only the document size
that varies considerably across these three cor-
pora, but also the number of annotations assigned
to each document as can be seen in Table 2.
We evaluate our measure for building a domain
model in Computer Science, by identifying a list
of general words with the help of a domain ex-
pert in Section 3.1. We envision two sets of ex-
periments: a standard term extraction evaluation
5Food and Agriculture Organization of the United States:
http://www.fao.org

Corpus Documents Tokens Avg. Annotations
Krapivin 2304 22 ·1065
GENIA 1999 0.5·10637
FAO 780 28 ·1068
Table 2: Corpora statistics
where the top ranked terms are evaluated against
the list of unique annotations provided in the eval-
uation datasets (Section 3.2.1), and a second set of
experiments where each term extraction approach
is used to assign candidates to documents in com-
bination with a document relevance measure in
Section 3.2.2.
3.1 Intrinsic evaluation of a domain model
A domain expert was asked to investigate nouns
used in the ACM Computing Classification Sys-
tem6. The expert was provided with the list of
nouns and their frequency in the taxonomy and
was required to identify nouns that refer to generic
concepts. A set of 80 nouns were selected in this
manner including system,information, and soft-
ware. Only one annotator was involved because of
the complexity of the task, that implies the analy-
sis and filtering of several hundred words. We esti-
mate the inter-annotator agreement by analysing a
subset of the selected words through a survey with
27 participants. A quarter of the selected words
are combined with the same number of randomly
selected rejected words and the resulting list is
sorted alphabetically. The Fleiss kappa statistic
for interrater agreement is 0.34, lying in the fair
agreement range. 80% of the words from our gold
standard domain model were selected by at least
half of the participants.
We compare our method (DC) with two other
benchmarks, the contrastive termhood measure
used in TermExtractor, and the frequency-based
method used by NC-value to select context words
(NCV weight). Again, context is defined as a
window of 5 words. A domain model has many
similarities with probabilistic topic modelling, al-
though it provides less structure. We compare our
approach with a popular approach to topic mod-
elling, Latent Dirichlet Allocation (LDA) (Blei et
al., 2003). We experimented with different num-
bers of topics but we report only the best results
6ACM Computing Classification System: http://
www.acm.org/about/class/1998/
Portion of ranked list (in number of candidates)
F−score
100 200 300 400
0
10
20
30
40 DC
T ermExtractor
NC V weight
LDA75
Figure 1: Methods for extracting a domain model
achieved for 75 topics (LDA75).
The results of this experiment are shown in Fig-
ure 1, in terms of F-score. Several conclusions
can be drawn from this experiment. First, the
methods that analyse the context of top ranked
terms (i.e., our domain coherence measure, DC,
and the weight used for context words in the NC-
value, wNC V ) perform better than the contrastive
measure used in TermExtractor, with statistically
significant gains. Also, our domain coherence
method outperforms the more simple frequency-
based weight used in NC-value, although this re-
sult is not statistically significant. As expected,
the words ranked high by TermExtractor are too
specific for a generic domain model. The topic
modelling approach identifies several words from
the gold standard but much less than our approach
and these are evenly distributed across latent top-
ics. These conclusions will be further investigated
across two other domains, using gold standard
terms annotated for three different applications in
Section 3.
3.2 Term extraction evaluation results
We implement and compare the baseline method
presented in Section 2.2 and the method based
on domain coherence described in Section 2.3,
against the NC-value and TermExtractor methods,
which are used as benchmarks. The same candi-
date selection method is used for all the evaluated
approaches. Candidate terms are selected through
syntactic analysis by defining a syntactic pattern
for noun phrases. To assure the results are compa-
rable, the same number of context words is used

Portion of ranked list (in %)
P recision
20 40 60 80 100
0
0.1
0.2
0.3
0.4
0.5
Baseline
Baseline +DC
NC value
T ermExtractor
Figure 2: Precision for top 10k
terms from the Krapivin corpus
Portion of ranked list (in %)
P recision
20 40 60 80 100
0
0.1
0.2
0.3Baseline
Baseline +DC
NC V alue
T ermExtractor
Figure 3: Precision for top 10k
terms from the FAO corpus
Portion of ranked list (in %)
P recision
20 40 60 80 100
0
0.2
0.4
0.6
0.8Baseline
Baseline +DC
NC V alue
T ermExtractor
Figure 4: Precision for top 10k
terms from the GENIA corpus
in our implementation of the NC-value approach
as the size of the domain model. Two general pur-
pose corpora, the Open American National Cor-
pus7and a corpus of books from Project Guten-
berg8, are used as contrastive corpora for our im-
plementation of TermExtractor. We considered
only the default setting for TermExtractor, assign-
ing equal weights to each measure.
3.2.1 Standard term extraction evaluation
While keyphrases and index terms suit well our
purposes, as they are terms of an intermediate
level of specificity, meant to summarise or clas-
sify documents, many of the terms annotated in
GENIA are too specific. We discard the annotated
terms that are mentioned in less than 1% of the
documents from corpus, based on our distribution
assumption. For each of the three datasets, the top
ten thousand ranked terms were evaluated. We in-
crementally analysed portions of the ranked lists
computed using the baseline approach (Baseline),
the baseline approach linearly combined with the
domain coherence measure (Baseline+DC), and
the two benchmarks, NC-value and TermExtrac-
tor. The precision value for a portion of the list
is scaled against the overall number of candidates
considered. First, we observe that all methods
perform better on the GENIA (Figure 4) and the
Krapivin corpus (Figure 2), with the best methods
achieving a maximum precision close to 60% at
the top of the ranked list.
The Food and Agriculture use case is more chal-
lenging, as the best method achieves a precision
of less than 20%, as can be seen in Figure 3.
7Open American National Corpus: http://www.
americannationalcorpus.org/OANC/
8Project Gutenberg: http://www.gutenberg.
org/
Also, the contrastive corpora measure employed in
TermExtractor yields considerably worse results
on all three domains, because the extracted terms
are too specific. The baseline method, that re-
wards embedded terms, outperforms the NC-value
method on the Computer Science domain, and in
the biomedical domain, but it performs slightly
worse on the Agriculture domain. The combina-
tion of our baseline method with the domain co-
herence measure (referred to as Baseline + DC in
the legend) yields the most stable behaviour, out-
performing all other measures across the three do-
mains, considerably so in the biomedical domain
(Figure 4) and at the top of the ranked list in Com-
puter Science (Figure 2). In Computer Science,
domain coherence significantly outperforms the
best performing state-of-the-art method, NC-value
(Figure 2). In Biomedicine, the improvement is
statistically significant, with a gain of 106% at top
20% of the list (Figure 4).
3.2.2 Application-based evaluation
An important reason for developing termhood
measures is that they are needed in specific ap-
plications, for example keyphrase extraction and
index term extraction. Typically, a termhood mea-
sure is combined with different measures of docu-
ment relevance in such applications, as the candi-
dates are assigned at the document level. We make
use of the standard information retrieval measure
TF-IDF in combination with the considered term
extraction scoring functions to assign terms to
documents. The best results are obtained by us-
ing domain coherence as a post-processing step.
In this experiment, the PostRankDC approach was
computed by re-ranking the top 30 candidates se-
lected using our baseline approach described in

Top F@5 F@10 F@15 F@20
Baseline 12.24 12.81 12.14 11.32
PostRankDC 13.42 14.55 13.72 12.51
NC-value 6.77 7.32 7.18 6.75
TermExtractor 1.41 1.77 1.95 1.97
Table 3: Keyphrase extraction evaluation on the
Krapivin corpus
Equation 7, based on their domain coherence.
The application-based evaluation proposed in
this work allows us to evaluate both precision and
recall, and consequently F-score can be used as
an evaluation metric. The results for keyphrase
extraction in Computer Science are presented in
Table 3, while the results for index term extrac-
tion in the Agriculture domain are shown in Ta-
ble 4. The results for document level term extrac-
tion from the Biomed corpus appear in Table 5.
All three methods yield a higher performance on
the GENIA corpus. The results on the Agricul-
ture corpus are again the lowest, because a larger
number of candidates has to be analysed.
Our Baseline method outperforms the NC-
value approach on the Krapivin corpus and on the
GENIA corpus, but not on the FAO corpus. We
can observe that the domain coherence approach
(P ostRankDC ) improves over our baseline ap-
proach (Baseline) on all three domains. The im-
provement is statistically significant compared to
the best state-of-the-art method in Computer Sci-
ence, NC-value. NC-value outperforms TermEx-
tractor in Computer Science and Agriculture, but
TermExtractor performs better in Biomedicine.
Although both NC-value and TermExtractor make
use of domain-independent features for ranking,
their performance varies across domains and ap-
plications. At the same time, combining our
domain coherence approach (PostRankDC) with
our baseline method in a post-ranking step dis-
plays a more stable behaviour, achieving the best
performance on the Computer Science domain
(Krapivin) and similar results with the results of
the best method in Biomedicine (GENIA) and
Agriculture (FAO).
4 Conclusions
In this study, we proposed an approach to iden-
tify intermediate level terms through domain mod-
elling and a novel domain coherence measure, ar-
Top F@5 F@10 F@15 F@20
Baseline 3.17 3.76 4.03 4.20
PostRankDC 55.8 5.62 5.29
NC-value 4.65 5.88 6.09 5.94
TermExtractor 0.2 0.31 0.34 0.35
Table 4: Index term evaluation on the FAO corpus
Top F@5 F@10 F@15 F@20
Baseline 9.67 15.71 20.17 23.19
PostRankDC 11.36 17.63 21.52 23.55
NC-value 7.79 11.97 14.01 14.6
TermExtractor 10.77 17.75 22.14 24.63
Table 5: Term extraction at the document level on the
GENIA corpus
guing that approaches that make use of contrastive
corpora are only suitable for updating existing ter-
minology resources with more specific terms and
not for summarisation or classification tasks. The
contributions described in this work are three-fold:
i) A method for extracting top level terms from a
domain corpus
ii) A novel domain coherence metric based on se-
mantic relatedness with a domain model
iii) A novel application-based evaluation for term ex-
traction systems
Experiments discussed in this paper show that
term extraction performance depends on the do-
main, although systems make use of domain-
independent features. Our domain coherence ap-
proach based on a domain model performs well
across domains, while the performance of the
NC-value and TermExtractor benchmarks is more
domain-dependent. The results lead to the conclu-
sion that using a domain model is more appropri-
ate than using statistical approaches based on con-
trastive corpora, for extracting intermediate level
terms. Future work will include an unsupervised
learning-to-rank approach for term extraction, that
will allow a more principled integration of domain
coherence measures with standard term extraction
features. The method proposed here can be used
as a specificity measure, and we currently investi-
gate this in the context of constructing generalisa-
tion hierarchies of concepts.
Acknowledgments
This work has been funded in part by the European
Union under Grant No. 258191 for the PROMISE

project, as well as by a research grant from Sci-
ence Foundation Ireland (SFI) under Grant Num-
ber SFI/12/RC/2289.
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