Name
Abstract | ||
|---|---|---|
Named entities are frequently used in a metonymic manner. They serve as references to related entities such as people and organisations. Accurate identification and interpretation of metonymy can be directly beneficial to various NLP applications, such as Named Entity Recognition and Geographical Parsing. Until now, metonymy resolution (MR) methods mainly relied on parsers, taggers, dictionaries, external word lists and other hand-crafted lexical resources. We show how a minimalist neural approach combined with a novel predicate window method can achieve competitive results on the SemEval 2007 task on Metonymy Resolution. Additionally, we contribute with a new Wikipedia-based MR dataset called RelocaR, which is tailored towards locations as well as improving previous deficiencies in annotation guidelines. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.18653/v1/P17-1115 | PROCEEDINGS OF THE 55TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS (ACL 2017), VOL 1 |
Field | DocType | Volume |
Computer science,Natural language processing,Artificial intelligence,Metonymy,Linguistics | Conference | P17-1 |
Citations | PageRank | References |
0 | 0.34 | 18 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Milan Gritta | 1 | 0 | 1.01 |
| Mohammad Taher Pilehvar | 2 | 376 | 25.70 |
| Nut Limsopatham | 3 | 172 | 14.86 |
| Nigel Collier | 4 | 1164 | 96.59 |