Name
Abstract | ||
|---|---|---|
Identifying event instance in texts plays a critical role in the field of Information Extraction (IE). The currently proposed methods that employ neural networks have successfully solve the problem to some extent, by encoding a series of linguistic features, such as lexicon, part-of-speech and entity. However, so far, the entity relation hasn't yet been taken into consideration. In this paper, we propose a novel event extraction method to exploit relation information for event detection (ED), due to the potential relevance between entity relation and event type. Methodologically, we combine relation and those widely used features in an attention-based network with Bidirectional Long Short-term Memory (Bi-LSTM) units. In particular, we systematically investigate the effect of relation representation between entities. In addition, we also use different attention strategies in the model. Experimental results show that our approach outperforms other state-of-the-art methods. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1007/978-3-319-99495-6_15 | Lecture Notes in Artificial Intelligence |
Keywords | Field | DocType |
Event detection,Attention mechanisms,Entity relation | Event type,Computer science,Exploit,Lexicon,Information extraction,Natural language processing,Artificial intelligence,Artificial neural network,Encoding (memory) | Conference |
Volume | ISSN | Citations |
11108 | 0302-9743 | 0 |
PageRank | References | Authors |
0.34 | 17 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jingli Zhang | 1 | 0 | 0.34 |
| Wenxuan Zhou | 2 | 81 | 6.51 |
| Yu Hong | 3 | 246 | 35.44 |
| Jianmin Yao | 4 | 131 | 16.96 |
| Min Zhang | 5 | 1849 | 157.00 |