Concept-Based Approach to Word-Sense Disambiguation

Ariel Raviv and Shaul Markovitch. Concept-Based Approach to Word-Sense Disambiguation. In Proceedings of the Twenty-Sixth AAAI Conference on Artificial Intelligence, 807-813 Toronto, Canada, 2012.

Abstract

The task of automatically determining the correct sense of a polysemous word has remained a challenge to this day. In our research, we introduce Concept-Based Disambiguation (CBD), a novel framework that utilizes recent semantic analysis techniques to represent both the context of the word and its senses in a high-dimensional space of natural concepts. The concepts are retrieved from a vast encyclopedic resource, thus enriching the disambiguation process with large amounts of domain-specific knowledge. In such concept-based spaces, more comprehensive measures can be applied in order to pick the right sense. Additionally, we introduce a novel representation scheme, denoted anchored representation, that builds a more specific text representation associated with an anchoring word. We evaluate our framework and show that the anchored representation is more suitable to the task of word- sense disambiguation (WSD). Additionally, we show that our system is superior to state-of-the-art methods when evaluated on domain-specific corpora, and competitive with recent methods when evaluated on a general corpus.

Keywords: Explicit Semantic Analysis, ESA, Word Sense Disambiguation, WSD

Secondary Keywords:

Online version:

Bibtex entry:

 @inproceedings{Raviv:2012:CBA,
  Author = {Ariel Raviv and Shaul Markovitch},
  Title = {Concept-Based Approach to Word-Sense Disambiguation},
  Year = {2012},
  Booktitle = {Proceedings of the Twenty-Sixth AAAI Conference on Artificial Intelligence},
  Pages = {807--813},
  Address = {Toronto, Canada},
  Url = {http://www.cs.technion.ac.il/~shaulm/papers/pdf/Raviv-Markovitch-AAAI2012.pdf},
  Keywords = {Explicit Semantic Analysis, ESA, Word Sense Disambiguation, WSD},
  Abstract = {
    The task of automatically determining the correct sense of a
    polysemous word has remained a challenge to this day. In our
    research, we introduce Concept-Based Disambiguation (CBD), a novel
    framework that utilizes recent semantic analysis techniques to
    represent both the context of the word and its senses in a high-
    dimensional space of natural concepts. The concepts are retrieved
    from a vast encyclopedic resource, thus enriching the
    disambiguation process with large amounts of domain-specific
    knowledge. In such concept-based spaces, more comprehensive
    measures can be applied in order to pick the right sense.
    Additionally, we introduce a novel representation scheme, denoted
    anchored representation, that builds a more specific text
    representation associated with an anchoring word. We evaluate our
    framework and show that the anchored representation is more
    suitable to the task of word- sense disambiguation (WSD).
    Additionally, we show that our system is superior to state-of-the-
    art methods when evaluated on domain-specific corpora, and
    competitive with recent methods when evaluated on a general
    corpus.
  }

  }