@inproceedings{hou-2018-enhanced,
title = "Enhanced Word Representations for Bridging Anaphora Resolution",
author = "Hou, Yufang",
editor = "Walker, Marilyn and
Ji, Heng and
Stent, Amanda",
booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers)",
month = jun,
year = "2018",
address = "New Orleans, Louisiana",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/N18-2001",
doi = "10.18653/v1/N18-2001",
pages = "1--7",
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.",
}
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<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.</abstract>
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%0 Conference Proceedings
%T Enhanced Word Representations for Bridging Anaphora Resolution
%A Hou, Yufang
%Y Walker, Marilyn
%Y Ji, Heng
%Y Stent, Amanda
%S Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers)
%D 2018
%8 June
%I Association for Computational Linguistics
%C New Orleans, Louisiana
%F hou-2018-enhanced
%X 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.
%R 10.18653/v1/N18-2001
%U https://aclanthology.org/N18-2001
%U https://doi.org/10.18653/v1/N18-2001
%P 1-7
Markdown (Informal)
[Enhanced Word Representations for Bridging Anaphora Resolution](https://aclanthology.org/N18-2001) (Hou, NAACL 2018)
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.