Improving explicit term matching with implicit topic matching for short text conversation

Short Text Conversation (STC) is the task of retrieving comments made to a post and reuse them as appropriate comments to a later post. Large vocabulary variations and very limited length of posts and comments pose great challenges to traditional retrieval techniques of matching terms. To address these problems, we propose a new technique of re-ranking comments initially retrieved through explicit term matching by using evidence of implicit topic matching gained from topic models of large collections of posts and comments. The key of our technique lies in (1) combining each post with the comments responding to it into a longer pseudo-document for topic modeling purposes, and (2) carrying out implicit topic matching by comparing the most probable topics contained in the topic composition of a comment and that of the query post. Experiments using the NTCIR-13 STC Chinese WeiBo collection showed that with properly tuned parameters, our technique can lead to improved retrieval effectiveness. The paper analyzes and discusses the results and outlines future work.