Abstract
The paper investigates several techniques for hypernymy extraction from a large collection of dictionary definitions in Russian. First, definitions from different dictionaries are clustered, then single words and multiwords are extracted as hypernym candidates. A classification-based approach on pre-trained word embeddings is implemented as a complementary technique. In total, we extracted about 40K unique hypernym candidates for 22K word entries. Evaluation showed that the proposed methods applied to a large collection of dictionary data are a viable option for automatic extraction of hyponym/hypernym pairs. The obtained data is available for research purposes.
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Notes
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A different, though less common approach is to define a word trough its synonyms:
car – a motorcar or automobile, or through cognate words: running – the action of the verb to run.
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See for example GermaNet, http://www.sfs.uni-tuebingen.de/GermaNet/.
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The list http://ruscorpora.ru/corpora-freq.html contains 6.8 million bigrams with frequency above 3. We also matched the extracted multiwords with Wikipedia titles, but there were fewer than 9% matches, and we did not use this as a selection criterion.
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The embeddings can be downloaded from http://rusvectores.org/models/, model ruwikiruscorpora_upos_skipgram_300_2_2018.
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Acknowledgments
MK was supported by RFBR grant #15-37-50912, PB and YK were supported by RFH grant #16-04-12019.
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Karyaeva, M., Braslavski, P., Kiselev, Y. (2018). Extraction of Hypernyms from Dictionaries with a Little Help from Word Embeddings. In: van der Aalst, W., et al. Analysis of Images, Social Networks and Texts. AIST 2018. Lecture Notes in Computer Science(), vol 11179. Springer, Cham. https://doi.org/10.1007/978-3-030-11027-7_8
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