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        <title>Neural Sequence Learning Models for Word Sense Disambiguation</title>
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        <namePart type="given">Alessandro</namePart>
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            <namePart type="given">Martha</namePart>
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            <namePart type="given">Rebecca</namePart>
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    <abstract>Word Sense Disambiguation models exist in many flavors. Even though supervised ones tend to perform best in terms of accuracy, they often lose ground to more flexible knowledge-based solutions, which do not require training by a word expert for every disambiguation target. To bridge this gap we adopt a different perspective and rely on sequence learning to frame the disambiguation problem: we propose and study in depth a series of end-to-end neural architectures directly tailored to the task, from bidirectional Long Short-Term Memory to encoder-decoder models. Our extensive evaluation over standard benchmarks and in multiple languages shows that sequence learning enables more versatile all-words models that consistently lead to state-of-the-art results, even against word experts with engineered features.</abstract>
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%0 Conference Proceedings
%T Neural Sequence Learning Models for Word Sense Disambiguation
%A Raganato, Alessandro
%A Delli Bovi, Claudio
%A Navigli, Roberto
%Y Palmer, Martha
%Y Hwa, Rebecca
%Y Riedel, Sebastian
%S Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing
%D 2017
%8 September
%I Association for Computational Linguistics
%C Copenhagen, Denmark
%F raganato-etal-2017-neural
%X Word Sense Disambiguation models exist in many flavors. Even though supervised ones tend to perform best in terms of accuracy, they often lose ground to more flexible knowledge-based solutions, which do not require training by a word expert for every disambiguation target. To bridge this gap we adopt a different perspective and rely on sequence learning to frame the disambiguation problem: we propose and study in depth a series of end-to-end neural architectures directly tailored to the task, from bidirectional Long Short-Term Memory to encoder-decoder models. Our extensive evaluation over standard benchmarks and in multiple languages shows that sequence learning enables more versatile all-words models that consistently lead to state-of-the-art results, even against word experts with engineered features.
%R 10.18653/v1/D17-1120
%U https://aclanthology.org/D17-1120
%U https://doi.org/10.18653/v1/D17-1120
%P 1156-1167

Markdown (Informal)

[Neural Sequence Learning Models for Word Sense Disambiguation](https://aclanthology.org/D17-1120) (Raganato et al., EMNLP 2017)

• Neural Sequence Learning Models for Word Sense Disambiguation (Raganato et al., EMNLP 2017)

ACL

• Alessandro Raganato, Claudio Delli Bovi, and Roberto Navigli. 2017. Neural Sequence Learning Models for Word Sense Disambiguation. In Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, pages 1156–1167, Copenhagen, Denmark. Association for Computational Linguistics.