2018 SyntacticallyAwareNeuralArchite

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Subject Headings: Definition Extraction System.

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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.

References

BibTeX

@inproceedings{2018_SyntacticallyAwareNeuralArchite,
  author    = {Luis Espinosa Anke and
               Steven Schockaert},
  editor    = {Marilyn A. Walker and
               Heng Ji and
               Amanda Stent},
  title     = {Syntactically Aware Neural Architectures for Definition Extraction},
  booktitle = {Proceedings of the 2018 Conference of the North American Chapter of
               the Association for Computational Linguistics: Human Language Technologies,
               (NAACL-HLT 2018) Volume 2 (Short Papers)},
  pages     = {378--385},
  publisher = {Association for Computational Linguistics},
  year      = {2018},
  url       = {https://doi.org/10.18653/v1/n18-2061},
  doi       = {10.18653/v1/n18-2061},
}


 AuthorvolumeDate ValuetitletypejournaltitleUrldoinoteyear
2018 SyntacticallyAwareNeuralArchiteLuis Espinosa Anke
Steven Schockaert
Syntactically Aware Neural Architectures for Definition Extraction2018