TY - GEN
T1 - Accelerating and evaluation of syntactic parsing in natural language question answering systems
AU - Chen, Zhe
AU - Wen, Dunwei
PY - 2007
Y1 - 2007
N2 - With the development of Natural Language Processing (NLP), more and more systems want to adopt NLP in User Interface Module to process user input, in order to communicate with user in a natural way. However, this raises a speed problem. That is, if NLP module can not process sentences in durable time delay, users will never use the system. As a result, systems which are strict with processing time, such as dialogue systems, web search systems, automatic customer service systems, especially real-time systems, have to abandon NLP module in order to get a faster system response. This paper aims to solve the speed problem. In this paper, at first, the construction of a syntactic parser which is based on corpus machine learning and statistics model is introduced, and then a speed problem analysis is performed on the parser and its algorithms. Based on the analysis, two accelerating methods, Compressed POS Set and Syntactic Patterns Pruning, are proposed, which can effectively improve the time efficiency of parsing in NLP module. To evaluate different parameters in the accelerating algorithms, two new factors, PT and RT, are introduced and explained in detail. Experiments are also completed to prove and test these methods, which will surely contribute to the application of NLP.
AB - With the development of Natural Language Processing (NLP), more and more systems want to adopt NLP in User Interface Module to process user input, in order to communicate with user in a natural way. However, this raises a speed problem. That is, if NLP module can not process sentences in durable time delay, users will never use the system. As a result, systems which are strict with processing time, such as dialogue systems, web search systems, automatic customer service systems, especially real-time systems, have to abandon NLP module in order to get a faster system response. This paper aims to solve the speed problem. In this paper, at first, the construction of a syntactic parser which is based on corpus machine learning and statistics model is introduced, and then a speed problem analysis is performed on the parser and its algorithms. Based on the analysis, two accelerating methods, Compressed POS Set and Syntactic Patterns Pruning, are proposed, which can effectively improve the time efficiency of parsing in NLP module. To evaluate different parameters in the accelerating algorithms, two new factors, PT and RT, are introduced and explained in detail. Experiments are also completed to prove and test these methods, which will surely contribute to the application of NLP.
KW - Corpus learning
KW - Evaluation
KW - Natural Language Processing
KW - Parsing algorithm
KW - Question answering
UR - http://www.scopus.com/inward/record.url?scp=49149128611&partnerID=8YFLogxK
M3 - Published Conference contribution
AN - SCOPUS:49149128611
SN - 9781601320254
T3 - Proceedings of the 2007 International Conference on Artificial Intelligence, ICAI 2007
SP - 595
EP - 601
BT - Proceedings of the 2007 International Conference on Artificial Intelligence, ICAI 2007
T2 - 2007 International Conference on Artificial Intelligence, ICAI 2007
Y2 - 25 June 2007 through 28 June 2007
ER -