Hyperbolic Relevance Matching for Neural Keyphrase Extraction

Mingyang Song, Yi Feng, Liping Jing


Abstract
Keyphrase extraction is a fundamental task in natural language processing that aims to extract a set of phrases with important information from a source document. Identifying important keyphrases is the central component of keyphrase extraction, and its main challenge is learning to represent information comprehensively and discriminate importance accurately. In this paper, to address the above issues, we design a new hyperbolic matching model (HyperMatch) to explore keyphrase extraction in hyperbolic space. Concretely, to represent information comprehensively, HyperMatch first takes advantage of the hidden representations in the middle layers of RoBERTa and integrates them as the word embeddings via an adaptive mixing layer to capture the hierarchical syntactic and semantic structures. Then, considering the latent structure information hidden in natural languages, HyperMatch embeds candidate phrases and documents in the same hyperbolic space via a hyperbolic phrase encoder and a hyperbolic document encoder. To discriminate importance accurately, HyperMatch estimates the importance of each candidate phrase by explicitly modeling the phrase-document relevance via the Poincaré distance and optimizes the whole model by minimizing the hyperbolic margin-based triplet loss. Extensive experiments are conducted on six benchmark datasets and demonstrate that HyperMatch outperforms the recent state-of-the-art baselines.
Anthology ID:
2022.naacl-main.419
Volume:
Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
Month:
July
Year:
2022
Address:
Seattle, United States
Venue:
NAACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
5710–5720
Language:
URL:
https://aclanthology.org/2022.naacl-main.419
DOI:
10.18653/v1/2022.naacl-main.419
Bibkey:
Cite (ACL):
Mingyang Song, Yi Feng, and Liping Jing. 2022. Hyperbolic Relevance Matching for Neural Keyphrase Extraction. In Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 5710–5720, Seattle, United States. Association for Computational Linguistics.
Cite (Informal):
Hyperbolic Relevance Matching for Neural Keyphrase Extraction (Song et al., NAACL 2022)
Copy Citation:
PDF:
https://aclanthology.org/2022.naacl-main.419.pdf
Code
 mysong7nlper/hypermatch
Data
KP20k