Answer Determination for Natural Language Questioning

摘要

Open-domain question answering is the task of finding a concise answer to a natural language question using a large domain, such as the Internet. The use of a semantic role labeling approach to the extraction of the answers to an open domain factoid (Who/When/What/Where) natural language question that contains a predicate is described. Semantic role labeling identities predicates and semantic argument phrases in the natural language question and the candidate sentences. When searching for an answer to a natural language question, the missing argument in the question is matched using semantic parses of the candidate answers. Such a technique may improve the accuracy of a question answering system and may decrease the length of answers for enabling voice interface to a question answering system.

主权项

1. A method comprising:
receiving a natural language question from a user; generating a search phrase based on the natural language question, the search phrase comprising a plurality of words in a non-stop order; identifying a plurality of candidate sentences based on the search phrase, wherein each candidate sentence in the plurality of candidate sentences contains the plurality of words in the non-stop order; evaluating the plurality of candidate sentences using a cascaded approach comprising a baseline approach, a semantic role labeler approach, and a combination of the baseline approach and the semantic role labeler approach, to yield a level of precision for each candidate sentence; when the level of precision of a candidate sentence meets a threshold, generating a candidate answer by using a phrase sentence extraction approach to extract a portion from a side of the search phrase within the candidate sentence; and providing the candidate answer in response to the natural language question.