Word Sense Disambiguation using Human Computation

Abstract

Words have different meanings based on the context of the word usage in a sentence. Word sense is one of the meanings of a word. Human language is ambiguous, so that many words can be interpreted in multiple ways depending on the context in which they occur. Word sense disambiguation (WSD) is the ability to identify the meaning of words in context in a computational manner. WSD is considered an AI complete problem, that is, a task whose solution is at least as hard as the most difficult problems in artificial intelligence. WSD can be viewed as a classification task: word senses are the classes, and an automatic classification method is used to assign each occurrence of a word to one or more classes based on the evidence from the context and from external knowledge sources. WSD heavily relies on knowledge. Knowledge sources provide data which are essential to associate senses with words. The assessment of WSD systems is discussed in the context of the Senseval/Semeval campaigns, aiming at the objective evaluation of systems participating in several different disambiguation tasks. Here, some of the knowledge sources used in WSD, different approaches for WSD (supervised, unsupervised and Knowledge-based ) and evaluation of WSD systems are discussed. The applications of WSD are also seen. In our project, we shall use Human Computation (HCOMP) to achieve the desired disambiguation for a word. Using HCOMP will decrease the processing overhead in our project as compared to that of WSD using AI algorithms. Also, the HCOMP component of our project will be achieved by a fun multiplayer game which will urge players to choose alternate words for certain words in a phrase or a sentence.