Composing Word Embeddings for Compound Words Using Linguistic Knowledge
Composing Word Embeddings for Compound Words Using Linguistic Knowledge
复制标题
DOI：
10.1145/3561299
复制
发表时间：
2022-09
影响因子：
2
通讯作者：
Kanako Komiya;Shinji Kouno;Takumi Seitou;Teruo Hirabayashi
中科院分区：
文献类型：
--
作者：
Kanako Komiya;Shinji Kouno;Takumi Seitou;Teruo Hirabayashi
In recent years, the use of distributed representations has been a fundamental technology for natural language processing. However, Japanese has multiple compound words, and often we must compare the meanings of a word and a compound word. Moreover, word boundaries in Japanese are unspecific because Japanese does not have delimiters between words, e.g., “ぶどう狩り” (grape picking) is one word according to one dictionary, whereas “ぶどう” and “狩り” are different words according to another dictionary. This study describes an attempt to compose word embeddings of a compound word from its constituent words in Japanese. We used “short unit” and “long unit,” both of which are the units of terms in UniDic—a Japanese dictionary compiled by the National Institute for Japanese Language and Linguistics—for constituent and compound words, respectively. Furthermore, we composed a word embedding of a compound word from the word embeddings of two constituent words using a neural network. The training data for the word embedding of compound words was created using a corpus generated by concatenating the corpora divided by constituent and compound words. We propose using linguistic knowledge for compositing word embedding to demonstrate how it improves the composition performance. We compared cosine similarity between composed and correct word embeddings of compound words to assess models with and without linguistic knowledge. Furthermore, we evaluated our methods by the ranking of synonyms using a thesaurus. We compared several frameworks and algorithms that use three types of linguistic knowledge—semantic patterns, parts of speech patterns, and compositionality score—and then investigated which linguistic knowledge improves the composition performance. The experiments demonstrated that the multitask models with the classification task of the parts of speech patterns and the estimation task of compositionality scores achieved high performances.