Publication Type

Conference Proceeding Article

Publication Date

4-2017

Abstract

Aspect extraction is a task to abstract the common properties of objects from corpora discussing them, such as reviews of products. Recent work on aspect extraction is leveraging the hierarchical relationship between products and their categories. However, such effort focuses on the aspects of child categories but ignores those from parent categories. Hence, we propose an LDA-based generative topic model inducing the two-layer categorical information (CAT-LDA), to balance the aspects of both a parent category and its child categories. Our hypothesis is that child categories inherit aspects from parent categories, controlled by the hierarchy between them. Experimental results on 5 categories of Amazon.com products show that both common aspects of parent category and the individual aspects of subcategories can be extracted to align well with the common sense. We further evaluate the manually extracted aspects of 16 products, resulting in an average hit rate of 79.10%.

Keywords

Computational linguistics, Linguistics

Discipline

Computational Engineering | Databases and Information Systems

Publication

Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: EACL 2017, Valencia, Spain, 2017 April 3-7

First Page

675

Last Page

680

ISBN

9781510838604

Publisher

Association for Computational Linguistics (ACL)

City or Country

Valencia, Spain

Creative Commons License

Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.

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