Using Global Constraints and Reranking to Improve Cognates Detection

Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics, pages 1983–1992 Vancouver, Canada, July 30 - August 4, 2017. c⃝2017 Association for Computational Linguistics https://doi.org/10.18653/v1/P17-1181 Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics, pages 1983–1992 Vancouver, Canada, July 30 - August 4, 2017. c⃝2017 Association for Computational Linguistics https://doi.org/10.18653/v1/P17-1181 Using Global Constraints and Reranking to Improve Cognates Detection Michael Bloodgood Department of Computer Science The College of New Jersey Ewing, NJ 08628 mbloodgood@tcnj.edu Benjamin Strauss Computer Science and Engineering Dept. The Ohio State University Columbus, OH 43210 strauss.105@osu.edu Abstract Global constraints and reranking have not been used in cognates detection research to date. We propose methods for using global constraints by performing rescoring of the score matrices produced by state of the art cognates detection systems. Using global constraints to perform rescoring is complementary to state of the art methods for performing cognates detection and re- sults in significant performance improve- ments beyond current state of the art per- formance on publicly available datasets with different language pairs and various conditions such as different levels of base- line state of the art performance and dif- ferent data size conditions, including with more realistic large data size conditions than have been evaluated with in the past. 1 Introduction This paper presents an effective method for us- ing global constraints to improve performance for cognates detection. Cognates detection is the task of identifying words across languages that have a common origin. Automatic cognates detection is important to linguists because cognates are needed to determine how languages evolved. Cognates are used for protolanguage reconstruction (Hall and Klein, 2011; Bouchard-Cˆot´e et al., 2013). Cognates are important for cross-language dictio- nary look-up and can also improve the quality of machine translation, word alignment, and bilin- gual lexicon induction (Simard et al., 1993; Kon- drak et al., 2003). A word is traditionally only considered cognate with another if both words proceed from the same ancestor. Nonetheless, in line with the conven- tions of previous research in computational lin- guistics, we set a broader definition. We use the word ‘cognate’ to denote, as in (Kondrak, 2001): “...words in different languages that are similar in form and meaning, without making a distinction between borrowed and genetically related words; for example, English ‘sprint’ and the Japanese bor- rowing ‘supurinto’ are considered cognate, even though these two languages are unrelated.” These broader criteria are motivated by the ways scien- tists develop and use cognate identification algo- rithms in natural language processing (NLP) sys- tems. For cross-lingual applications, the advan- tage of such technology is the ability to identify words for which similarity in meaning can be ac- curately inferred from similarity in form; it does not matter if the similarity in form is from strict genetic relationship or later borrowing (Mericli and Bloodgood, 2012). Cognates detection has received a lot of atten- tion in the literature. The research of the use of statistical learning methods to build systems that can automatically perform cognates …

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