Concept-based approach toword-sense disambiguation
Ariel Raviv, Shaul Markovitch · National Conference on Artificial Intelligence · 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 wordsense 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.