Smoothing Temporal Difference for Text Categorization

This paper addresses text categorization problem that training data may be derived from a different time period than test data. We present a method for text categorization that minimizes the impact of temporal effects by using term smoothing and transfer learning techniques. We first used a technique called Temporal-based Term Smoothing (TTS) to replace those time sensitive features with representative terms, then applied boosting based transfer learning algorithm called TrAda-Boost for categorization. The results using a 21-year Japanese Mainichi Newspaper corpus showed that integrating term smoothing and transfer learning improves overall performance, especially it is effective when the creation time period of the test data differs greatly from the training data.