Classifying Korean comparative sentences for comparison analysis

Comparisons sort objects based on their superiority or inferiority and they may have major effects on a variety of evaluation processes. The Web facilitates qualitative and quantitative comparisons via online debates, discussion forums, product comparison sites, etc., and comparison analysis is becoming increasingly useful in many application areas. This study develops a method for classifying sentences in Korean text documents into several different comparative types to facilitate their analysis. We divide our study into two tasks: 1) extracting comparative sentences from text documents, and 2) classifying comparative sentences into seven types. In the first task, we investigate many actual comparative sentences by referring to previous studies and construct a lexicon of comparisons. Sentences that contain elements from the lexicon are regarded as comparative sentence candidates. Next, we use machine learning techniques to eliminate non-comparative sentences from the candidates. In the second task, we roughly classify the comparative sentences using keywords and use a transformation-based learning method to correct initial classification errors. Experimental results show that our method could be suitable for practical use. We obtained an F1-score of 90.23% in the first task, an accuracy of 81.67% in the second task, and an overall accuracy of 88.59% for the integrated system with both tasks. 1 Introduction In many areas, comparisons are very important during decision making. For example, politicians may change their political strategies after monitoring how their policies compare with those of their competitors. Manufacturers can also change their marketing strategies after comparing their products with those of their competitors. A similar situation also applies to customers. If a customer is deciding whether to buy Car-A or Car-B, he/she will probably access the Web and type these two items into the search box. A search engine such as Google will then find relevant documents. Next, the customer will open and read each retrieved document until he/she obtains enough information. The customer " s decision may be dominated by sentences that compare these two items. It is clear that obtaining information from the Web is a good and easy solution. However, it is also clear that reading many documents until sufficient information has been acquired is still a time-consuming task. If the customer only has access to a small amount of data, he/she may form biased views. By contrast, reading large amounts of data demands an enormous amount of time and effort. Therefore, it would be very useful in many …