Name
Abstract | ||
|---|---|---|
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 effective algorithm for identifying the activity time of an event in news is difficult in part because of the sparsity of explicit temporal 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 event clusters to infer activity times for all sentences. Experimental evidence suggests that this is a promising approach, given evaluations performed on three distinct news article sets against the baseline of assigning the publication date. Our approach achieves 90%, 88.7%, and 68.7% accuracy, respectively, outperforming the baseline twice. |
Year | Venue | Keywords |
|---|---|---|
2008 | ACL (Student Research Workshop) | temporal information,distinct news article set,event modeling,explicit temporal expression,experimental evidence,cluster sentence,inferring activity time,activity time,promising approach,event cluster,publication date,effective algorithm,machine learning,question answering |
Field | DocType | Volume |
Automatic summarization,Event modeling,Computer science,Temporal expressions,Artificial intelligence,Natural language processing,Topic model,Machine learning | Conference | P08-3 |
Citations | PageRank | References |
4 | 0.62 | 5 |
Authors | ||
1 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Vladimir Eidelman | 1 | 323 | 17.61 |