Timeline adaptation for text classification

Abstract

In this paper, we address the text classification problem that a period of time created test data is different from the training data, and present a method for text classification based on temporal adaptation. We first applied lexical chains for the training data to collect terms with semantic relatedness, and created sets (we call these Sem sets). Semantically related terms in the documents are replaced to their representative term. For the results, we identified short terms that are salient for a specific period of time. Finally, we trained SVM classifiers by applying a temporal weighting function to each selected short terms within the training data, and classified test data. Temporal weighting function is weighted each short term in the training data according to the temporal distance between training and test data. The results using MedLine data showed that the method was comparable to the current state-of-the-art biased-SVM method, especially the method is effective when testing on data far from the training data.