Temporal Feature Space for Text Classification

摘要

In supervised learning algorithms for text classification the text content is usually represented using the frequencies of the words it contains, ignoring their semantic and their relationships. Words within temporal expressions such as "today" or "last February" are particularly affected by this simplification: the same expression can have a different semantic in documents with different timestamps, while different expressions could refer to the same time. After extracting temporal expressions in documents, we model a set of temporal features derived from the time mentioned in the document, showing the relation between these features and the belonging category. We test our temporal approach on a subset of the New York Times corpus showing a significant improvement over the text-only baseline.