Teruo Hirabayashi


2020

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Composing Word Vectors for Japanese Compound Words Using Bilingual Word Embeddings
Teruo Hirabayashi | Kanako Komiya | Masayuki Asahara | Hiroyuki Shinnou
Proceedings of the 34th Pacific Asia Conference on Language, Information and Computation

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Automatic Creation of Correspondence Table of Meaning Tags from Two Dictionaries in One Language Using Bilingual Word Embedding
Teruo Hirabayashi | Kanako Komiya | Masayuki Asahara | Hiroyuki Shinnou
Proceedings of the 13th Workshop on Building and Using Comparable Corpora

In this paper, we show how to use bilingual word embeddings (BWE) to automatically create a corresponding table of meaning tags from two dictionaries in one language and examine the effectiveness of the method. To do this, we had a problem: the meaning tags do not always correspond one-to-one because the granularities of the word senses and the concepts are different from each other. Therefore, we regarded the concept tag that corresponds to a word sense the most as the correct concept tag corresponding the word sense. We used two BWE methods, a linear transformation matrix and VecMap. We evaluated the most frequent sense (MFS) method and the corpus concatenation method for comparison. The accuracies of the proposed methods were higher than the accuracy of the random baseline but lower than those of the MFS and corpus concatenation methods. However, because our method utilized the embedding vectors of the word senses, the relations of the sense tags corresponding to concept tags could be examined by mapping the sense embeddings to the vector space of the concept tags. Also, our methods could be performed when we have only concept or word sense embeddings whereas the MFS method requires a parallel corpus and the corpus concatenation method needs two tagged corpora.