Answer Determination for Natural Language Questioning

Abstract

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.

We claim: 
     
         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.   
     
     
         2 . The method of  claim 1 , wherein the candidate answer is one of a plurality of candidate answers, and further comprising ranking each candidate answer in the plurality of candidate answers by a confidence level. 
     
     
         3 . The method of  claim 2 , wherein the candidate answer has a highest confidence level in the plurality of candidate answers. 
     
     
         4 . The method of  claim 1 , wherein identifying of the plurality of candidate sentences further comprises:
 scanning a plurality of documents for the search phrase.   
     
     
         5 . The method of  claim 1 , wherein a conjunction of a sub-phrase occurs in a final step of the cascaded approach and comprises determining whether a predicate and a search argument are found within the natural language question. 
     
     
         6 . The method of  claim 5 , wherein the search argument is classified as a location argument. 
     
     
         7 . The method of  claim 5 , wherein the search argument is classified as a temporal argument. 
     
     
         8 . A system comprising:
 a processor; and   a computer-readable storage medium having instructions stored which, when executed by the processor, cause the processor to perform operations 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. 
   
     
     
         9 . The system of  claim 8 , wherein the candidate answer is one of a plurality of candidate answers; and
 the computer-readable storage medium has additional instructions stored which, when executed by the processor, result in operations comprising ranking each candidate answer in the plurality of candidate answers by a confidence level.   
     
     
         10 . The system of  claim 9 , wherein the candidate answer has a highest confidence level in the plurality of candidate answers. 
     
     
         11 . The system of  claim 8 , wherein identifying of the plurality of candidate sentences further comprises:
 scanning a plurality of documents for the search phrase.   
     
     
         12 . The system of  claim 8 , wherein a conjunction of a sub-phrase occurs in a final step of the cascaded approach and comprises determining whether a predicate and a search argument are found within the natural language question. 
     
     
         13 . The system of  claim 12 , wherein the search argument is classified as a location argument. 
     
     
         14 . The system of  claim 12 , wherein the search argument is classified as a temporal argument. 
     
     
         15 . A computer-readable storage device having instructions stored which, when executed by a computing device, cause the computing device to perform operations 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.   
     
     
         16 . The computer-readable storage device of  claim 15 , wherein the candidate answer is one of a plurality of candidate answers; and
 having additional instructions stored which, when executed by the computing device, result in operations comprising ranking each candidate answer in the plurality of candidate answers by a confidence level.   
     
     
         17 . The computer-readable storage device of  claim 16 , wherein the candidate answer has a highest confidence level in the plurality of candidate answers. 
     
     
         18 . The computer-readable storage device of  claim 15 , wherein identifying of the plurality of candidate sentences further comprises:
 scanning a plurality of documents for the search phrase.   
     
     
         19 . The computer-readable storage device of  claim 15 , wherein a conjunction of a sub-phrase occurs in a final step of the cascaded approach and comprises determining whether a predicate and a search argument are found within the natural language question. 
     
     
         20 . The computer-readable storage device of  claim 19 , wherein the search argument is classified as a location argument.