A Non-Entity Approach for Intent-Based Classification: A Case Study of Thai News

Abstract

Conjunction and stop words are normally ignored in text classification task that is content-based, such as classifying news into entertainment or sports. However, they are useful in this study, since the content and the intention of the document are independent. This paper studies intent-based classification that specifically desires to classify the author's intention of Thai news article into three intents, 'inform', 'explain', and 'give solution'. These three intents subtly co-exist with the content of the article and thus is our classification challenge. Our experiments confirm that intent-based classification needs a different approach from those techniques used for content-based classification. Accordingly, we propose a new pipeline for Thai intent-based classification such that conjunction and others can play a significant role above entity. Our contributions include (1) proving the need for a new methodology to handle intent-based classification and (2) proposing the Non-Entity data processing approach to managing intent-based classification problems. The proposed methodology shows partially promising results. Nonetheless, flags for enhancement are also discussed in the conclusion for future works.