  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.
