Word Sense Embedding System

A Word Sense Embedding System is a Word Sense Disambiguation System that is a Natural Language Sequence Embedding System.

• Context:
  • It can (typically) implement a Word Sense Embedding Algorithm (to solve a word sense embedding task).
  • …
• Example(s):
  • ARES System,
  • SensEmBERT System.
  • …
• Counter-Example(s):
  • Word Embedding System,
  • Morpheme Embedding System,
  • Character Embedding System,
  • Subword Embedding System.
• See: Word Similarity Task, Word Analogy Task, Distributional Co-Occurrence Word Vector, Term Vector Space, Sentiment Analysis, Natural Language Processing, Language Model, Sequence Tagging, Deep Contextualized Word Representation System, Contextual Word Vector.

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References

2020a

• (Scarlini et al., 2020) ⇒ Bianca Scarlini, Tommaso Pasini, and Roberto Navigli. (2020). “SensEmBERT: Context-Enhanced Sense Embeddings for Multilingual Word Sense Disambiguation.” In: Proceedings of the Thirty-Fourth AAAI Conference on Artificial Intelligence (AAAI 2020); Proceedings of the Thirty-Second Innovative Applications of Artificial Intelligence Conference (IAAI 2020); Proceedings of the Tenth AAAI Symposium on Educational Advances in Artificial Intelligence (EAAI 2020).

2020b

• (Scarlini et al., 2020) ⇒ Bianca Scarlini, Tommaso Pasini, and Roberto Navigli. (2002). “With More Contexts Comes Better Performance: Contextualized Sense Embeddings for All-Round Word Sense Disambiguation.” In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP 2020).

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