Name
Title | ||
|---|---|---|
Neural Modeling Of Multi-Predicate Interactions For Japanese Predicate Argument Structure Analysis |
Abstract | ||
|---|---|---|
The performance of Japanese predicate argument structure (PAS) analysis has improved in recent years thanks to the joint modeling of interactions between multiple predicates. However, this approach relies heavily on syntactic information predicted by parsers, and suffers from error propagation. To remedy this problem, we introduce a model that uses grid-type recurrent neural networks. The proposed model automatically induces features sensitive to multi-predicate interactions from the word sequence information of a sentence. Experiments on the NAIST Text Corpus demonstrate that without syntactic information, our model outperforms previous syntax-dependent models. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.18653/v1/P17-1146 | PROCEEDINGS OF THE 55TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS (ACL 2017), VOL 1 |
Field | DocType | Volume |
Structure analysis,Computer science,Natural language processing,Artificial intelligence,Predicate (grammar) | Conference | P17-1 |
Citations | PageRank | References |
2 | 0.37 | 15 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Hiroki Ouchi | 1 | 18 | 8.08 |
| Hiroyuki Shindo | 2 | 75 | 13.80 |
| Yuji Matsumoto | 3 | 27 | 12.98 |