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Automated English Mnemonic Keyword Suggestion for Learning Japanese Vocabulary
Abstract
This paper proposes a new methodology that automatically generates English mnemonic keywords to support the learning of basic Japanese vocabulary. A new phonetic algorithm, called JemSoundex, is also introduced for phonetically transliterating the Japanese and English languages for phonetic matching. The effective mnemonic keywords are selected and ranked by considering their phonetic, orthographic and semantic similarities, as well as psycholinguistic power. A system-oriented evaluation is conducted to evaluate the proposed methodology, and in particular an approach on the basis of the JemSoundex algorithm. The experimental results show that the JemSoundex outperforms other comparative approaches, i.e., IPA, the original Soundex and Metaphone.
... A combination of acoustic and orthographic links were used in the studies by Wyra et al. (2007) and . In non-alphabetic languages only the acoustic aspect was used (e.g., Anonthanasap et al., 2015;Wyra, 2019). For the current study, although Persian is alphabetic, as the alphabet is different to English, only acoustic links were used (e.g., Atkinson & Raugh, 1974). ...
Learning new vocabulary is one of the challenges in language learning yet crucial for mastering another language. It is also essential in any discipline or professional field where mastering new terminology is indispensable. VLASTWA is a Vocabulary Learning and Strategy Teaching Web Application designed to help Persian native speakers improve their English vocabulary knowledge. In this study, I examined the effects of VLASTWA’s use on improving Persian learners’ vocabulary. The conducted research presents an evaluation of efficacy and usability of this custom-built (for this project), targeted and learnable web application for teaching an extensively researched vocabulary learning strategy, the keyword method, and for facilitating learning of new vocabulary with the aid of this method. In this longitudinal study (n = 240, age 18+), effectiveness of the use of the keyword method taught within the designed web application (app) or traditional pen and paper (P&P) was compared between four experimental (two P&P and two app groups – with differences in receiving the encoding or encoding and recall keyword method training) and two control groups (one app and one P&P). In the experimental groups, participants learned to use the keyword method, applied it in learning 22 new (English) words, and tested the recall of this newly learned vocabulary within the app and P&P methods on four different occasions (times) while in the control groups – the only difference was not being given any keyword method instruction.
The popularity of the Japan M- learning app to read and write only focuses on improving vocabulary. It is essential to improve vocabulary and readability, but it is also essential to foster Japan's communication. To provide new interactions for the learner as an alternative to the Japan of learning tools, speaking to the input of the concept of learning the application using M. Its primary purpose is to investigate how to speak to support mobile learning implementation. The embedded intelligent system analyzes the collected data, embedding can communicate with other systems; it is a computer with an Internet connection. Besides, these systems, experience, security, and connection can adjust itself based on the ability to learn from the remote monitoring and management and the current data. Including smart devices and embedded smart systems, it also contains such a complex artificial intelligence-based software system, a chat robot, interconnected collection of these devices as an expert system. The biggest challenge in advanced Japanese learning is a way of evaluating the accuracy of pronunciation and pronunciation. Data from the Japan International Exchange Foundation in Indonesia shows an unbalanced gap ratio between the availability of learning needs and the formal Japanese educational institutions. As replaces the Japanese learning tool, it will learn to input to speak to provide a new interactive concept to the user. Before checking whether the data has been entered correctly, the user, the mobile learning system, talks about some of the words and sentences in Japanese smartphones can speak.
This paper presents an overview of the phonetic encoding algorithms designed to determine the similarity of words in sound (pronunciation). Phonetic encoding algorithms are divided into the algorithms for comparing words and the algorithms for determining the distance between words. Word comparison algorithms, such as SoundEx, NYSIIS, Daitch–Mokotoff, Metaphone, and Polyphone, as well as algorithms for determining the distance between words, such as Levenshtein, Jaro, and N-grams, are described. For each algorithm, the advantages and shortcomings are discussed, and an analog for the Russian language is given. For eliminating the common shortcomings of phonetic encoding algorithms, the idea suggested in this paper is to use not the letter sequences of words, but the sequences of their elementary sounds. In this case, word recognition, record linkage, and word indexing by sounds are expected to improve.
To support language learning by using the principle of a Mnemonic technique, this paper proposes to automatically generate suggested mnemonic words by using “phonetic algorithms”, i.e., Soundex and Metaphone. Levenshtein edit distance is employed to compare the phonetic similarity of foreign words and that of words in a known language using the sound transcriptions transformed by the proposed algorithms. Our new interactive cross-lingual system, called iMnem, is also introduced to support the task of searching for mnemonic words with images for better imagination of word association. To the best of our knowledge, this work is the first to apply phonetic algorithms for mnemonic word generation. In this study, we focus on suggesting mnemonic keywords in English for supporting learning of words in Japanese.
In this paper, we propose a new educational system for second-language vocabulary learning based on a mnemonic technique. The system is equipped with the dynamic and interactive interface that allows vocabulary learners to seamlessly browse a collection of foreign words while suggesting phonetically related words of a known lan-guage for helping the memorization of unfamiliar languages. The phonetic algorithm is employed to encode pronunciation of words. The phonetic codes of words are then applied to homonyms of different languages (i.e., known and learning languages). The Levenshtein distance is used to quantify the similarity of phonetic codes or of words' pro-nunciation. The mnemonic words with their associated images are presented surrounding the learning words according to the edit distance or phonetic similarity. With visual ef-fects based on user's input, the dynamic and interactive interface will help users browse a collection of vocabulary in source and destination languages as well as images related to their word meanings.
We present age-of-acquisition (AoA) ratings for 30,121 English content words (nouns, verbs, and adjectives). For data collection, this megastudy used the Web-based crowdsourcing technology offered by the Amazon Mechanical Turk. Our data indicate that the ratings collected in this way are as valid and reliable as those collected in laboratory conditions (the correlation between our ratings and those collected in the lab from U.S. students reached .93 for a subsample of 2,500 monosyllabic words). We also show that our AoA ratings explain a substantial percentage of the variance in the lexical-decision data of the English Lexicon Project, over and above the effects of log frequency, word length, and similarity to other words. This is true not only for the lemmas used in our rating study, but also for their inflected forms. We further discuss the relationships of AoA with other predictors of word recognition and illustrate the utility of AoA ratings for research on vocabulary growth.
This study aims to develop a phonetic similarity measurement method across Asian languages. The method, cross-language similarity algorithm aggregates the transcription of language-specific Romanization, the International Phonetic Alphabet, the Soundex algorithm, and Levenshtein distance. To evaluate the proposed algorithm, this study involves an experiment using ninety-two chemical element names in nine different languages. The scores of the similarity of names were calculated between a source language and each target language. We could draw a line of threshold between the scores of similarities in each language into two groups (phonetic and semantic adoption groups). After evaluating the ratios of precision, recall, and F-measure, the results show that the proposed methodology successfully differentiates between phonetic and semantic groups by allocating the thresholds in all Asian languages, with the exception of Chinese. The results reported here prove that the proposed method has the potential to be applied to cross-language information retrieval and various linguistic studies.
The MRC machine usable dictionary contains 150837 words with up to 26 linguistic and psycholinguistic attributes for each. The attributes are from sources that are publicly available, but difficult to obtain and structure into a single dictionary. Three utility programs are described which permit the selection of words defined by a set of specified attribute values, and the attribute values for a set of specified words. These programs permit the construction of word sets for psycholinguistic experiments which control for the attributes specified in the dictionary. The dictionary may also be of use to researchers in artificial intelligence and computer science who require psychological and linguistic descriptions of words. - 2 - Those wishing to construct word sets as stimuli for psycholinguistic experiments must take into account a large number of characteristics of the words (see Cutler, 1981; Whaley, 1978). The Medical Research Council (MRC) Psycholinguistic Database version 1, was...
We present TransPhoner: a system that generates keywords for a variety of scenarios including vocabulary learning, phonetic transliteration, and creative word plays. We select effective keywords by considering phonetic, orthographic and semantic word similarity, and word concept imageability. We show that keywords provided by TransPhoner improve learner performance in an online vocabulary learning study, with the improvement being more pronounced for harder words. Participants rated TransPhoner keywords as more helpful than a random keyword baseline, and almost as helpful as manually selected keywords. Comments also indicated higher engagement in the learning task, and more desire to continue learning. We demonstrate additional applications to tasks such as pure phonetic transliteration, generation of mnemonics for complex vocabulary, and topic-based transformation of song lyrics.
Evaluated the effectiveness of a mnemonic procedure, the keyword method, for learning a foreign language vocabulary. The method used divides the study of a vocabulary item into 2 stages. The 1st stage requires the S to associate the spoken foreign word with an English word, the keyword, that sounds like some part of the foreign word; the 2nd stage requires him to form a mental image of the keyword interacting with the English translation. The experiment compared the keyword method with an unconstrained control procedure using Russian vocabulary. Ss were 52 undergraduates. On all measures the keyword method proved to be highly effective, yielding for the most critical test a score of 72% correct for the keyword group compared to 46% for the control group. (PsycINFO Database Record (c) 2012 APA, all rights reserved)
The method to automatically extract translational Japanese-KATAKANA and English word pairs from bilingual corpora is proposed. The method applies all the existing transliteration rules to each mora unit in a KATAKANA word, and extract English word which matched or partially-matched to one of these transliteration candidates as translation. For instance, if there is a word 'ƒ Oƒ ‰ ƒ t ' (graph) in Japanese part of bilingual corpora, we generate such transliteration candidates as , , ,... and extract similar words from English part of corpora. The method worked fairly well, achieving 83-100% precision at 75% recall against eight corpora we used for evaluation.
GROUPS OF SS, 17-46 YR. OLD COLLEGE STUDENTS, WERE USED TO SCALE 925 NOUNS ON ABSTRACTNESS-CONCRETENESS (C), IMAGERY (I), AND MEANINGFULNESS (M). CONCRETENESS WAS DEFINED IN TERMS OF DIRECTNESS OF REFERENCE TO SENSE EXPERIENCE, AND I, IN TERMS OF WORD'S CAPACITY TO AROUSE NONVERBAL IMAGES; C AND I WERE RATED ON 7-POINT SCALES. MEANINGFULNESS WAS DEFINED IN TERMS OF THE MEAN NUMBER OF WRITTEN ASSOCIATIONS IN 30 SEC. THE MEAN SCALE VALUES FOR THESE VARIABLES ARE PRESENTED FOR EACH OF THE 925 NOUNS. ALSO REPORTED ARE THE INTERCORRELATIONS OF THE VARIABLES, TOGETHER WITH AN EXAMINATION OF THE WORDS FOR WHICH C, I, AND M VALUES ARE MOST CLEARLY DIFFERENTIATED; AND RELIABILITY DATA, INCLUDING COMPARISONS WITH SCALE VALUES FOR THE VARIABLES FROM OTHER STUDIES. (45 REF.)
Similarity is an important and widely used concept. Previous definitions of similarity are tied to a particular application or a form of knowledge representation. We present an informationtheoretic definition of similarity that is applicable as long as there is a probabilistic model. We demonstrate how our definition can be used to measure the similarity in a number of different domains.
This paper presents a new measure of semantic similarity in an is-a taxonomy, based on the notion of information content. Experimental evaluation suggests that the measure performs encouragingly well (a correlation of r = 0:79 with a benchmark set of human similarity judgments, with an upper bound of r = 0:90 for human subjects performing the same task), and significantly better than the traditional edge counting approach (r = 0:66). 1 Introduction Evaluating semantic relatedness using network representations is a problem with a long history in artificial intelligence and psychology, dating back to the spreading activation approach of Quillian [ 1968 ] and Collins and Loftus [ 1975 ] . Semantic similarity represents a special case of semantic relatedness: for example, cars and gasoline would seem to be more closely related than, say, cars and bicycles, but the latter pair are certainly more similar. Rada et al. [ 1989 ] suggest that the assessment of similarity in semantic n...
This paper presents a new approach for measuring semantic similarity/distance between words and concepts. It combines a lexical taxonomy structure with corpus statistical information so that the semantic distance between nodes in the semantic space constructed by the taxonomy can be better quantified with the computational evidence derived from a distributional analysis of corpus data. Specifically, the proposed measure is a combined approach that inherits the edge-based approach of the edge counting scheme, which is then enhanced by the node-based approach of the information content calculation. When tested on a common data set of word pair similarity ratings, the proposed approach outperforms other computational models. It gives the highest correlation value (r = 0.828) with a benchmark based on human similarity judgements, whereas an upper bound (r = 0.885) is observed when human subjects replicate the same task.
Phonetic Matching: A Better Soundex
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- S P Morse
Transliterated Word Encoding and Retrieval Algorithms for Thai-English Cross-Language Retrieval
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Phonetic Matching in Japanese
- M Yasukawa
- S J Culpeppery
- F Scholery
Search Engines: Information Retrieval in Practice
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- D Metzler
- T Strohman
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