Lexical Disambiguation in Natural Language Questions (NLQs)
This addresses lexical disambiguation for question answering systems, but it is incremental as it builds on existing shallow NLP methods.
The paper tackles lexical ambiguity in natural language questions by integrating context and domain-specific concepts into shallow NLP techniques, applied to a university QA system, but does not report concrete performance numbers.
Question processing is a fundamental step in a question answering (QA) application, and its quality impacts the performance of QA application. The major challenging issue in processing question is how to extract semantic of natural language questions (NLQs). A human language is ambiguous. Ambiguity may occur at two levels; lexical and syntactic. In this paper, we propose a new approach for resolving lexical ambiguity problem by integrating context knowledge and concepts knowledge of a domain, into shallow natural language processing (SNLP) techniques. Concepts knowledge is modeled using ontology, while context knowledge is obtained from WordNet, and it is determined based on neighborhood words in a question. The approach will be applied to a university QA system.