Automatic Acquisition of Semantic-Based Question Reformulations for Question Answering

Abstract. In this paper, we present a method for the automatic acquisition of semantic-based reformulations from natural language questions. Our goal is to find useful and generic reformulation patterns, which can be used in our question answering system to find better candidate answers. We used 1343 examples of different types of questions and their corresponding answers from the TREC-8, TREC-9 and TREC-10 collection as training set. The system automatically extracts patterns from sentences retrieved from the Web based on syntactic tags and the semantic relations holding between the main arguments of the question and answer as defined in WordNet. Each extracted pattern is then assigned a weight according to its length, the distance between keywords, the answer sub-phrase score, and the level of semantic similarity between the extracted sentence and the question. The system differs from most other reformulation learning systems in its emphasis on semantic features. To evaluate the gene...

• Type: Conference
• Year: 2006
• Where: CICLING
• Authors: Jamileh Yousefi, Leila Kosseim
• Keywords: Better Candidate Answers, CICLING 2006, Generic Reformulation Patterns, Natural Language Processing, Natural Language Questions