Multilingual lexical resources to detect cognates in non-aligned texts

28573

Authors

nan

Abstract

The identification of cognates between two distinct languages has recently start- ed to attract the attention of NLP re- search, but there has been little research into using semantic evidence to detect cognates. The approach presented in this paper aims to detect English-French cog- nates within monolingual texts (texts that are not accompanied by aligned translat- ed equivalents), by integrating word shape similarity approaches with word sense disambiguation techniques in order to account for context. Our implementa- tion is based on BabelNet, a semantic network that incorporates a multilingual encyclopedic dictionary. Our approach is evaluated on two manually annotated da- tasets. The first one shows that across different types of natural text, our method can identify the cognates with an overall accuracy of 80%. The second one, con- sisting of control sentences with semi- cognates acting as either true cognates or false friends, shows that our method can identify 80% of semi-cognates acting as cognates but also identifies 75% of the semi-cognates acting as false friends. 1

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nan

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Multilingual lexical resources to detect cognates in non-aligned texts Haoxing Wang Queensland University of Technology 2 George St, Brisbane, 4000 haoxing.wang@hdr.qut.edu.au Laurianne Sitbon Queensland University of Technology 2 George St, Brisbane, 4000 l.sitbon@qut.edu.au Abstract The identification of cognates between two distinct languages has recently start- ed to attract the attention of NLP re- search, but there has been little research into using semantic evidence to detect cognates. The approach presented in this paper aims to detect English-French cog- nates within monolingual texts (texts that are not accompanied by aligned translat- ed equivalents), by integrating word shape similarity approaches with word sense disambiguation techniques in order to account for context. Our implementa- tion is based on BabelNet, a semantic network that incorporates a multilingual encyclopedic dictionary. Our approach is evaluated on two manually annotated da- tasets. The first one shows that across different types of natural text, our method can identify the cognates with an overall accuracy of 80%. The second one, con- sisting of control sentences with semi- cognates acting as either true cognates or false friends, shows that our method can identify 80% of semi-cognates acting as cognates but also identifies 75% of the semi-cognates acting as false friends. 1 Introduction Estimating the difficulty of a text for non-native speakers, or learners of a second language, is gaining interest in natural language processing, information retrieval and education communities. So far measures and models to predict readability in a cross-lingual context have used mainly text features used in monolingual readability contexts (such as word shapes, grammatical features and individual word frequency features). These fea- tures have been tested when estimating readabil- ity levels for K-12 (primary to high school) read- ers. As word frequency can be partially estimated by word length, it remains the principal feature for estimation second language learners with the assumption that less frequent words are less like- ly to have been encountered and therefore acces- sible in the memory of the learnt language by readers. However such features are not entirely adapted to existing reading abilities of learners with a different language background. In particu- lar, for a number of reasons, many languages have identical words in their vocabulary, which opens a secondary access to the meaning of such words as an alternative to memory in the learnt language. For example, English and French lan- guages belong to different branches of the Indo- European family of languages, and additionally European history of mixed cultures has led their vocabularies to share a great number of similar and identical words. These words are called cog- nates. Cognates have often slightly changed their or- thography (especially in derived forms), and quite often meaning as well in the years and cen- turies following the transfer. Because of these changes, cognates are generally of one of three different types. First of all, true cognates are English-French word pairs that are viewed as similar and are mutual translations. The spelling could be identical or not, e.g., prison and prison, ceramic and céramique. …

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Labeled Text

The identification of cognates between two distinct languages has recently started to attract the attention of NLP research, but there has been little research into using semantic evidence to detect cognates. The approach presented in this paper aims to detect English-French cognates within monolingual texts (texts that are not accompanied by aligned translated equivalents), by integrating word shape similarity approaches with word sense disambiguation techniques in order to account for context. Our implementation is based on BabelNet, a semantic network that incorporates a multilingual encyclopedic dictionary. Our approach is evaluated on two manually annotated datasets. The first one shows that across different types of natural text, our method can identify the cognates with an overall accuracy of 80%. The second one, consisting of control sentences with semicognates acting as either true cognates or false friends, shows that our method can identify 80% of semi-cognates acting as cognates but also identifies 75% of the semi-cognates acting as false friends. 1

Publication Details

Paper ID:
28573
Year:
2014
Publication:
TIPSTER
Publication Type:
conf
DOI:
nan