Paper: Inferring Activity Time in News through Event Modeling

ACL ID P08-3003
Title Inferring Activity Time in News through Event Modeling
Venue Annual Meeting of the Association of Computational Linguistics
Session Main Conference
Year 2008

Many applications in NLP, such as question- answering and summarization, either require or would greatly benefit from the knowledge of when an event occurred. Creating an ef- fective algorithm for identifying the activ- ity time of an event in news is difficult in part because of the sparsity of explicit tem- poral expressions. This paper describes a domain-independent machine-learning based approach to assign activity times to events in news. We demonstrate that by applying topic models to text, we are able to cluster sentences that describe the same event, and utilize the temporal information within these eventclusterstoinferactivitytimesforallsen- tences. Experimental evidence suggests that thisisapromisingapproach,givenevaluations performed on three distinct news article sets against t...