Name
Abstract | ||
|---|---|---|
Natural language text contains numerous event-based, and a large number of semantic relations exist between events. Event relations express the event rationality logic and reveal the evolution process of events, which is of great significance for machines to understand the text and the construction of event-based knowledge base. Event relation discovery includes extracting event relation from text and obtaining event relation by reasoning. Event relation extraction focuses on the recognition of explicit relations, while event relation reasoning can also discover implicit relations, which is more meaningful and more difficult. In this paper, we propose a model combining LSTM and attention mechanism for event relation reasoning, which uses the attention mechanism to dynamically generate event sequence representation according to the type of relation and predicts the event relation. The macro-F1 value in the experimental result reaches 63.71%, which shows that the model can effectively discover implicit event-event relation. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1007/978-3-030-82136-4_40 | KNOWLEDGE SCIENCE, ENGINEERING AND MANAGEMENT, PT I |
Keywords | DocType | Volume |
Event knowledge graph, Event relation, Bi-LSTM, Attention mechanism | Conference | 12815 |
ISSN | Citations | PageRank |
0302-9743 | 0 | 0.34 |
References | Authors | |
0 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Tingting Tang | 1 | 0 | 0.34 |
| Wei Liu | 2 | 11 | 4.26 |
| Weimin Li | 3 | 63 | 25.40 |
| Jinliang Wu | 4 | 0 | 0.68 |
| Haiyang Ren | 5 | 0 | 0.68 |