@inproceedings{jawahar-etal-2018-elmolex,
title = "{ELM}o{L}ex: Connecting {ELM}o and Lexicon Features for Dependency Parsing",
author = "Jawahar, Ganesh and
Muller, Benjamin and
Fethi, Amal and
Martin, Louis and
Villemonte de la Clergerie, {\'E}ric and
Sagot, Beno{\^\i}t and
Seddah, Djam{\'e}",
editor = "Zeman, Daniel and
Haji{\v{c}}, Jan",
booktitle = "Proceedings of the {C}o{NLL} 2018 Shared Task: Multilingual Parsing from Raw Text to Universal Dependencies",
month = oct,
year = "2018",
address = "Brussels, Belgium",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/K18-2023",
doi = "10.18653/v1/K18-2023",
pages = "223--237",
abstract = "In this paper, we present the details of the neural dependency parser and the neural tagger submitted by our team {`}ParisNLP{'} to the CoNLL 2018 Shared Task on parsing from raw text to Universal Dependencies. We augment the deep Biaffine (BiAF) parser (Dozat and Manning, 2016) with novel features to perform competitively: we utilize an indomain version of ELMo features (Peters et al., 2018) which provide context-dependent word representations; we utilize disambiguated, embedded, morphosyntactic features from lexicons (Sagot, 2018), which complements the existing feature set. Henceforth, we call our system {`}ELMoLex{'}. In addition to incorporating character embeddings, ELMoLex benefits from pre-trained word vectors, ELMo and morphosyntactic features (whenever available) to correctly handle rare or unknown words which are prevalent in languages with complex morphology. ELMoLex ranked 11th by Labeled Attachment Score metric (70.64{\%}), Morphology-aware LAS metric (55.74{\%}) and ranked 9th by Bilexical dependency metric (60.70{\%}).",
}
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<abstract>In this paper, we present the details of the neural dependency parser and the neural tagger submitted by our team ‘ParisNLP’ to the CoNLL 2018 Shared Task on parsing from raw text to Universal Dependencies. We augment the deep Biaffine (BiAF) parser (Dozat and Manning, 2016) with novel features to perform competitively: we utilize an indomain version of ELMo features (Peters et al., 2018) which provide context-dependent word representations; we utilize disambiguated, embedded, morphosyntactic features from lexicons (Sagot, 2018), which complements the existing feature set. Henceforth, we call our system ‘ELMoLex’. In addition to incorporating character embeddings, ELMoLex benefits from pre-trained word vectors, ELMo and morphosyntactic features (whenever available) to correctly handle rare or unknown words which are prevalent in languages with complex morphology. ELMoLex ranked 11th by Labeled Attachment Score metric (70.64%), Morphology-aware LAS metric (55.74%) and ranked 9th by Bilexical dependency metric (60.70%).</abstract>
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%0 Conference Proceedings
%T ELMoLex: Connecting ELMo and Lexicon Features for Dependency Parsing
%A Jawahar, Ganesh
%A Muller, Benjamin
%A Fethi, Amal
%A Martin, Louis
%A Villemonte de la Clergerie, Éric
%A Sagot, Benoît
%A Seddah, Djamé
%Y Zeman, Daniel
%Y Hajič, Jan
%S Proceedings of the CoNLL 2018 Shared Task: Multilingual Parsing from Raw Text to Universal Dependencies
%D 2018
%8 October
%I Association for Computational Linguistics
%C Brussels, Belgium
%F jawahar-etal-2018-elmolex
%X In this paper, we present the details of the neural dependency parser and the neural tagger submitted by our team ‘ParisNLP’ to the CoNLL 2018 Shared Task on parsing from raw text to Universal Dependencies. We augment the deep Biaffine (BiAF) parser (Dozat and Manning, 2016) with novel features to perform competitively: we utilize an indomain version of ELMo features (Peters et al., 2018) which provide context-dependent word representations; we utilize disambiguated, embedded, morphosyntactic features from lexicons (Sagot, 2018), which complements the existing feature set. Henceforth, we call our system ‘ELMoLex’. In addition to incorporating character embeddings, ELMoLex benefits from pre-trained word vectors, ELMo and morphosyntactic features (whenever available) to correctly handle rare or unknown words which are prevalent in languages with complex morphology. ELMoLex ranked 11th by Labeled Attachment Score metric (70.64%), Morphology-aware LAS metric (55.74%) and ranked 9th by Bilexical dependency metric (60.70%).
%R 10.18653/v1/K18-2023
%U https://aclanthology.org/K18-2023
%U https://doi.org/10.18653/v1/K18-2023
%P 223-237
Markdown (Informal)
[ELMoLex: Connecting ELMo and Lexicon Features for Dependency Parsing](https://aclanthology.org/K18-2023) (Jawahar et al., CoNLL 2018)
ACL
- Ganesh Jawahar, Benjamin Muller, Amal Fethi, Louis Martin, Éric Villemonte de la Clergerie, Benoît Sagot, and Djamé Seddah. 2018. ELMoLex: Connecting ELMo and Lexicon Features for Dependency Parsing. In Proceedings of the CoNLL 2018 Shared Task: Multilingual Parsing from Raw Text to Universal Dependencies, pages 223–237, Brussels, Belgium. Association for Computational Linguistics.