Supervised Word Sense Disambiguation Algorithm

A Supervised Word Sense Disambiguation Algorithm is a data-driven WSD algorithm that makes use of a supervised learning algorithm.

• AKA: Supervised WSD Algorithm.
• Context:
  • It can make use of a Lexical Semantic Similarity Function.
  • It can be implemented by a Supervised WSD System (that solves a supervised WSD task).
• Example(s):
  • a Neural-based WSD Algorithm.
  • an SVM-based WSD Algorithm.
  • …
• Counter-Example(s):
  • an Unsupervised Word Sense Disambiguation Algorithm.
  • a Supervised Anaphora Resolution Algorithm.
  • a Supervised Coreference Resolution Algorithm.
• See: TF-IDF.

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References

2016

• (Iacobacci et al., 2016) ⇒ Ignacio Iacobacci, Mohammad Taher Pilehvar, and Roberto Navigli. (2016). “Embeddings for Word Sense Disambiguation: An Evaluation Study.” In: Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (ACL-2016).

2006

• (Preiss, 2006) ⇒ Judita Preiss (2006). “Probabilistic Word Sense Disambiguation: Analysis and Techniques for Combining Knowledge Sources." Technical Report 673 at the University of Cambridge.

2004

• (Mihalcea, 2004) ⇒ Rada Mihalcea. (2004). “Co-training and Self-training for Word Sense Disambiguation.” In: Proceedings of NAACL Conference (NAACL 2004).
  • QUOTE: This paper investigated the application of co-training and self-training to supervised word sense disambiguation.

2003

• (Patwardhan et al., 2003) ⇒ Siddharth Patwardhan, Satanjeev Banerjee, and Ted Pedersen. (2003). “Using Measures of Semantic Relatedness for Word Sense Disambiguation.” In: Proceedings of the Fourth International Conference on Intelligent Text Processing and Computational Linguistics (CICLing 2003).
  • QUOTE: ... Supervised learning algorithms also assign meanings to words from a sense inventory, but take a very different approach. A human manually annotates examples of a word with tags that indicates the intended sense in each context. These examples become training data for a learning algorithm that induces rules that are then used to assign meanings to other occurrences of the word. In supervised methods, the human uses the information in the dictionary to decide which sense tag should be assigned to an example, and then a learning algorithm finds clues from the context of that word that allow it to generalize rules of disambiguation. Note that the learning algorithm simply views the sense inventory as a set of categories and that the human has absorbed the information from the dictionary and combined it with their own knowledge of words to manually sense–tag the training examples. The objective of a dictionary–based approach is to provide a disambiguation algorithm with the contents of a dictionary and attempt to make inferences about the meanings of words in context based on that information. Here we extract information about semantic relatedness from the lexical database WordNet (sometimes augmented by corpus statistics) in order to make such inferences. ...

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