Extracting Comparative Entities and Predicates from Texts Using Comparative Type Classification

Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics, pages 1636–1644, Portland, Oregon, June 19-24, 2011. c⃝2011 Association for Computational Linguistics Extracting Comparative Entities and Predicates from Texts Using Comparative Type Classification Seon Yang Youngjoong Ko Department of Computer Engineering, Department of Computer Engineering, Dong-A University, Dong-A University, Busan, Korea Busan, Korea seony.yang@gmail.com yjko@dau.ac.kr Abstract The automatic extraction of comparative in- formation is an important text mining problem and an area of increasing interest. In this paper, we study how to build a Korean comparison mining system. Our work is composed of two consecutive tasks: 1) classifying comparative sentences into different types and 2) mining comparative entities and predicates. We perform various experiments to find relevant features and learning techniques. As a result, we achieve outstanding performance enough for practical use. 1 Introduction Almost every day, people are faced with a situation that they must decide upon one thing or the other. To make better decisions, they probably attempt to compare entities that they are interesting in. These days, many web search engines are helping people look for their interesting entities. It is clear that getting information from a large amount of web data retrieved by the search engines is a much better and easier way than the traditional survey methods. However, it is also clear that directly reading each document is not a perfect solution. If people only have access to a small amount of data, they may get a biased point of view. On the other hand, investigating large amounts of data is a time- consuming job. Therefore, a comparison mining system, which can automatically provide a summary of comparisons between two (or more) entities from a large quantity of web documents, would be very useful in many areas such as marketing. We divide our work into two tasks to effectively build a comparison mining system. The first task is related to a sentence classification problem and the second is related to an information extraction problem. Task 1. Classifying comparative sentences into one non-comparative class and seven comparative classes (or types); 1) Equality, 2) Similarity, 3) Difference, 4) Greater or lesser, 5) Superlative, 6) Pseudo, and 7) Implicit comparisons. The purpose of this task is to efficiently perform the following task. Task 2. Mining comparative entities and predicates taking into account the characteristics of each type. For example, from the sentence “Stock-X is worth more than stock-Y.” belonging to “4) Greater or lesser” type, we extract “stock- X” as a subject entity (SE), “stock-Y” as an object entity (OE), and “worth” as a comparative predicate (PR). These tasks are not easy or simple problems as described below. Classifying comparative sentences (Task 1): For the first task, we extract comparative sentences from text documents and then classify the extracted comparative sentences into seven 1636 comparative types. Our basic idea is a keyword search. Since Ha (1999a) categorized dozens of Korean comparative keywords, we easily build an initial keyword set as follows: ▪ Кling = {“같 ([gat]: …

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