Enhanced Word Representations for Bridging Anaphora Resolution

Yufang Hou


Abstract
Most current models of word representations (e.g., GloVe) have successfully captured fine-grained semantics. However, semantic similarity exhibited in these word embeddings is not suitable for resolving bridging anaphora, which requires the knowledge of associative similarity (i.e., relatedness) instead of semantic similarity information between synonyms or hypernyms. We create word embeddings (embeddings_PP) to capture such relatedness by exploring the syntactic structure of noun phrases. We demonstrate that using embeddings _PP alone achieves around 30% of accuracy for bridging anaphora resolution on the ISNotes corpus. Furthermore, we achieve a substantial gain over the state-of-the-art system (Hou et al., 2013b) for bridging antecedent selection.
Anthology ID:
N18-2001
Volume:
Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers)
Month:
June
Year:
2018
Address:
New Orleans, Louisiana
Editors:
Marilyn Walker, Heng Ji, Amanda Stent
Venue:
NAACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
1–7
Language:
URL:
https://aclanthology.org/N18-2001
DOI:
10.18653/v1/N18-2001
Bibkey:
Cite (ACL):
Yufang Hou. 2018. Enhanced Word Representations for Bridging Anaphora Resolution. In Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers), pages 1–7, New Orleans, Louisiana. Association for Computational Linguistics.
Cite (Informal):
Enhanced Word Representations for Bridging Anaphora Resolution (Hou, NAACL 2018)
Copy Citation:
PDF:
https://aclanthology.org/N18-2001.pdf
Data
ISNotes