| Download | - View final version: Syntax encoding with application in Authorship Attribution (PDF, 478 KiB)
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| DOI | Resolve DOI: https://doi.org/10.18653/v1/D18-1294 |
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| Author | Search for: Zhang, Richong; Search for: Hu, Zhiyuan; Search for: Guo, Hongyu1; Search for: Mao, Yongyi |
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| Affiliation | - National Research Council of Canada. Digital Technologies
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| Format | Text, Article |
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| Conference | Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, Oct. 31 - Nov. 4, 2018, Brussels, Belgium |
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| Abstract | 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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| Publication date | 2018-11-04 |
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| Publisher | Association for Computational Linguistics |
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| Licence | |
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| In | |
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| Language | English |
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| Peer reviewed | Yes |
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| Export citation | Export as RIS |
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| Report a correction | Report a correction (opens in a new tab) |
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| Record identifier | 8f70be2b-f362-4e2e-a318-a7ef5f93e3cd |
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| Record created | 2021-06-17 |
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| Record modified | 2021-06-18 |
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