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.
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.
@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. } }