A Pattern-mining Driven Study on Differences of Newspapers in Expressing Temporal Information
This study addresses an under-researched topic of how newspapers express temporal information, which could be useful for computational linguistics and media analysis.
This paper investigates how different newspapers express temporal information by creating a temporally annotated corpus and applying pattern mining. By analyzing skip-gram patterns from sequences of temporal and part-of-speech tags, the study found that newspapers indeed differ in their temporal expression styles.
This paper studies the differences between different types of newspapers in expressing temporal information, which is a topic that has not received much attention. Techniques from the fields of temporal processing and pattern mining are employed to investigate this topic. First, a corpus annotated with temporal information is created by the author. Then, sequences of temporal information tags mixed with part-of-speech tags are extracted from the corpus. The TKS algorithm is used to mine skip-gram patterns from the sequences. With these patterns, the signatures of the four newspapers are obtained. In order to make the signatures uniquely characterize the newspapers, we revise the signatures by removing reference patterns. Through examining the number of patterns in the signatures and revised signatures, the proportion of patterns containing temporal information tags and the specific patterns containing temporal information tags, it is found that newspapers differ in ways of expressing temporal information.