Analysis of Similarity Measures between Short Text for the NTCIR-12 Short Text Conversation Task

According to rise of social networking services, short text like micro-blogs has become a valuable resource for practical applications. When using text data in applications, similarity estimation between text is an important process. Conventional methods have assumed that an input text is sufficiently long such that we can rely on statistical approaches, e.g., counting word occurrences. However, micro-blogs are much shorter; for example, tweets posted to Twitter are restricted to have only 140 character long. This is critical for the conventional methods since they suffer from lack of reliable statistics from the text. In this study, we compare the state-of-the-art methods for estimating text similarities to investigate their performance in handling short text, specially, under the scenario of short text conversation. We implement a conversation system using a million tweets crawled from Twitter. Our system also employs supervised learning approach to decide if a tweet can be a reply to an input, which has been revealed effective as a result of the NTCIR-12 Short Text Conversation Task.