Name
Abstract | ||
|---|---|---|
Distinguishing between antonyms and synonyms is a key task to achieve high performance in NLP systems. While they are notoriously difficult to distinguish by distributional co-occurrence models, pattern-based methods have proven effective to differentiate between the relations. In this paper, we present a novel neural network model AntSynNET that exploits lexico-syntactic patterns from syntactic parse trees. In addition to the lexical and syntactic information, we successfully integrate the distance between the related words along the syntactic path as a new pattern feature. The results from classification experiments show that AntSynNET improves the performance over prior pattern-based methods. |
Year | Venue | DocType |
|---|---|---|
2017 | EACL | Conference |
Volume | ISSN | Citations |
abs/1701.02962 | EACL2017 | 2 |
PageRank | References | Authors |
0.37 | 14 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Kim Anh Nguyen | 1 | 15 | 2.97 |
| Sabine Schulte im Walde | 2 | 440 | 65.65 |
| Ngoc Thang Vu | 3 | 220 | 35.62 |