Syntactic word embedding based on dependency syntax and polysemous analysis

Most word embedding models have the following problems:(1)In the models based on bag-of-words contexts,the structural relations of sentences are completely neglected;(2)Each word uses a single embedding,which makes the model indiscriminative for polysemous words;(3)Word embedding easily tends to contextual structure similarity of sentences.To solve these problems,we propose an easy-to-use representation algorithm of syntactic word embedding(SWE).The main procedures are:(1)A polysemous tagging algorithm is used for polysemous representation by the latent Dirichlet allocation(LDA)algorithm;(2)Symbols‘+’and‘-’are adopted to indicate the directions of the dependency syntax;(3)Stopwords and their dependencies are deleted;(4)Dependency skip is applied to connect indirect dependencies;(5)Dependency-based contexts are inputted to a word2vec model.Experimental results show that our model generates desirable word embedding in similarity evaluation tasks.Besides,semantic and syntactic features can be captured from dependency-based syntactic contexts,exhibiting less topical and more syntactic similarity.We conclude that SWE outperforms single embedding learning models.

Semantic Distribution of Polysemous Verbs in Junior High School English Textbooks in Japan

This paper reports how the polysemous word TAKE is treated in the junior high school textbooks in Japan and its semantic distributions. Also, it looks into the ways in which Japanese junior high school students actually use TAKE in their compositions and analyses its semantic distributions in their writings. This paper uses the BBC 500 Word essay compositions(BBC 500 words) to compare these compositions with the ones written by English native speakers. Comparing the different semantic distributions of the word TAKE in Japanese junior high school textbooks, Japanese students' compositions and BBC 500 words, it is clear that compositions in BBC 500 words have a better balance. This result suggests that the English native speaking children use TAKE to convey various meanings. Also the core meaning of TAKE is the highest among the three contexts under investigation. This paper suggests the textbooks should include many more various objects to enable students to learn to use them in various situations and the core meaning of TAKE should be presented in the textbooks more often. Furthermore, the semantic features of the polysemous words and their semiotic value in English should be introduced in the textbooks and taught in intentional ways.

A Study of Image-Schema-Based Metaphorical Extension of Verbal Polysemy

The researcher analyzes the distribution and types of image-schema-based metaphorical extension(or IS-based meta-extension) in the semantic extension of 203 polysemous verbs,which can be named as PVs and are randomly selected from Longman Dictionary of Contemporary English(2nd Edition,1993).She finds that IS-based meta-extension is pervasive in the semantic extension of verbal polysemy.Specif ically,she f inds 77.83% of the samples can be analyzed in terms of IS-based Meta-extension,and PVs that can be analyzed in terms of path-IS-based,force-IS-based,containerIS-based,path+force-IS-based,and path+container-IS-based metaphorical extension,together making up 70.44% of the samples.

On Translation of Polysemic Words in News English

Polysemic words appear in news English with high-frequency.So,how to determine the exact meaning of a polysemic word in the news and put it into proper Chinese causes much trouble and frustration to readers.This paper summarizes some effective translating strategies of polysemic words from English to Chinese.They include:translating based on collocation,translating based on grammar,translating based on background knowledge,translating based on logical relation,and translating based on register.