| Téléchargement | - Voir la version finale : Syntax encoding with application in Authorship Attribution (PDF, 478 Kio)
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| DOI | Trouver le DOI : https://doi.org/10.18653/v1/D18-1294 |
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| Auteur | Rechercher : Zhang, Richong; Rechercher : Hu, Zhiyuan; Rechercher : Guo, Hongyu1; Rechercher : Mao, Yongyi |
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| Affiliation | - Conseil national de recherches du Canada. Technologies numériques
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| Format | Texte, Article |
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| Conférence | Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, Oct. 31 - Nov. 4, 2018, Brussels, Belgium |
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| Résumé | We propose a novel strategy to encode the syntax parse tree of sentence into a learnable distributed representation. The proposed syntax encoding scheme is provably information-lossless. In specific, an embedding vector is constructed for each word in the sentence, encoding the path in the syntax tree corresponding to the word. The one-to-one correspondence between these “syntax-embedding” vectors and the words (hence their embedding vectors) in the sentence makes it easy to integrate such a representation with all word-level NLP models. We empirically show the benefits of the syntax embeddings on the Authorship Attribution domain, where our approach improves upon the prior art and achieves new performance records on five benchmarking data sets. |
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| Date de publication | 2018-11-04 |
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| Maison d’édition | Association for Computational Linguistics |
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| Licence | |
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| Dans | |
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| Langue | anglais |
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| Publications évaluées par des pairs | Oui |
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| Exporter la notice | Exporter en format RIS |
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| Signaler une correction | Signaler une correction (s'ouvre dans un nouvel onglet) |
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| Identificateur de l’enregistrement | 8f70be2b-f362-4e2e-a318-a7ef5f93e3cd |
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| Enregistrement créé | 2021-06-17 |
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| Enregistrement modifié | 2021-06-18 |
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