@inproceedings{espinosa-anke-schockaert-2018-syntactically,
title = "Syntactically Aware Neural Architectures for Definition Extraction",
author = "Espinosa-Anke, Luis and
Schockaert, Steven",
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/N18-2061",
doi = "10.18653/v1/N18-2061",
pages = "378--385",
abstract = "Automatically identifying definitional knowledge in text corpora (Definition Extraction or DE) is an important task with direct applications in, among others, Automatic Glossary Generation, Taxonomy Learning, Question Answering and Semantic Search. It is generally cast as a binary classification problem between definitional and non-definitional sentences. In this paper we present a set of neural architectures combining Convolutional and Recurrent Neural Networks, which are further enriched by incorporating linguistic information via syntactic dependencies. Our experimental results in the task of sentence classification, on two benchmarking DE datasets (one generic, one domain-specific), show that these models obtain consistent state of the art results. Furthermore, we demonstrate that models trained on clean Wikipedia-like definitions can successfully be applied to more noisy domain-specific corpora.",
}
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%0 Conference Proceedings
%T Syntactically Aware Neural Architectures for Definition Extraction
%A Espinosa-Anke, Luis
%A Schockaert, Steven
%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 espinosa-anke-schockaert-2018-syntactically
%X Automatically identifying definitional knowledge in text corpora (Definition Extraction or DE) is an important task with direct applications in, among others, Automatic Glossary Generation, Taxonomy Learning, Question Answering and Semantic Search. It is generally cast as a binary classification problem between definitional and non-definitional sentences. In this paper we present a set of neural architectures combining Convolutional and Recurrent Neural Networks, which are further enriched by incorporating linguistic information via syntactic dependencies. Our experimental results in the task of sentence classification, on two benchmarking DE datasets (one generic, one domain-specific), show that these models obtain consistent state of the art results. Furthermore, we demonstrate that models trained on clean Wikipedia-like definitions can successfully be applied to more noisy domain-specific corpora.
%R 10.18653/v1/N18-2061
%U https://aclanthology.org/N18-2061
%U https://doi.org/10.18653/v1/N18-2061
%P 378-385
Markdown (Informal)
[Syntactically Aware Neural Architectures for Definition Extraction](https://aclanthology.org/N18-2061) (Espinosa-Anke & Schockaert, NAACL 2018)
ACL
- Luis Espinosa-Anke and Steven Schockaert. 2018. Syntactically Aware Neural Architectures for Definition Extraction. 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 378–385, New Orleans, Louisiana. Association for Computational Linguistics.