2017 NeuralSequenceLearningModelsfor

• (Raganato et al., 2017) ⇒ 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.

Subject Headings: Natural Language Model; Neural Sequence Learning Task; Word Sense Disambiguation.

Cited By

• http://scholar.google.com/scholar?q=%222017%22+Neural+Sequence+Learning+Models+for+Word+Sense+Disambiguation

Quotes

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.

References

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	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2017 NeuralSequenceLearningModelsfor	Alessandro Raganato Claudio Delli Bovi			Neural Sequence Learning Models for Word Sense Disambiguation