Tetsuya Nasukawa

Yang Zhao, Tetsuya Nasukawa, Masayasu Muraoka, Bishwaranjan Bhattacharjee. Findings of the Association for Computational Linguistics: ACL 2023. 2023.

Cross-Cultural Comparisons of Review Aspect Importance

Topic models have been successfully applied in lexicon extraction. However, most previous methods are limited to document-aligned data. In this paper, we try to address two challenges of applying topic models to lexicon extraction in non-parallel data: 1) hard to model the word relationship and 2) noisy seed dictionary. To solve these two challenges, we propose two new bilingual topic models to better capture the semantic information of each word while discriminating the multiple translations in a noisy seed dictionary. We extend the scope of topic models by inverting the roles of "word" and "document". In addition, to solve the problem of noise in seed dictionary, we incorporate the probability of translation selection in our models. Moreover, we also propose an effective measure to evaluate the similarity of words in different languages and select the optimal translation pairs. Experimental results using real world data demonstrate the utility and efficacy of the proposed models.

This article proposesclause-level evaluation detection, which is a fine-grained type of opinion mining, and describes an unsupervised lexicon building method for capturing domain-specific knowledge by leveraging the similar polarities of sentiments between adjacent clauses. The lexical entries to be acquired are calledpolar atoms, the minimum human-understandable syntactic structures that specify the polarity of clauses. As a hint to obtain candidate polar atoms, we usecontext coherency, the tendency for the same polarity to appear successively in a context. Using the overall density and precision of coherency in the corpus, the statistical estimation picks up appropriate polar atoms from among the candidates, without any manual tuning of the threshold values. The experimental results show that the precision of polarity assignment with the automatically acquired lexicon was 83 per cent on average, and our method is robust for corpora in diverse domains and for the size of the initial lexicon.

This paper proposes an unsupervised lexicon building method for the detection of polar clauses, which convey positive or negative aspects in a specific domain. The lexical entries to be acquired are called polar atoms, the minimum human-understandable syntactic structures that specify the polarity of clauses. As a clue to obtain candidate polar atoms, we use context coherency, the tendency for same polarities to appear successively in contexts. Using the overall density and precision of coherency in the corpus, the statistical estimation picks up appropriate polar atoms among candidates, without any manual tuning of the threshold values. The experimental results show that the precision of polarity assignment with the automatically acquired lexicon was 94% on average, and our method is robust for corpora in diverse domains and for the size of the initial lexicon.