With the widely use of electronic products in our daily life, how to improve the quality of communication between people and electronic products becomes more and more important. Under this situation, the concept of Natural Language Processing comes into people's sight. There are two parts of NLP. One is Natural Language Generation System, aiming to convert computer data into natural language. The other one is natural language understanding system, which can transform natural language to a easier mode for computer to understand.
     Even though when we communicate with the computer in limited words, NLP performs well. However when this system is put into the environment with more uncertainty and ambiguity, what we get disappoints us. The reasons leading to the decline mainly include the difficulty to define the boundary between words, vocabulary polysemy, syntax blur and non-standard input. Here is an example. When we say "hehe", it maybe means a politely refusal, or it can also show a disdain to what we heard. In addition, the implied mean behind the sentences also brings some confusedness. If I say "would you please bring me the salt?", what I really mean is hoping you to bring the salt to me, not a simply "Yes".
     Depending on the problems existing in NLP nowadays, we need to do some targeted improvements. Firstly, taking more processing on real text instead of traditional analysis based on grammar. Secondly, updating the glossary in time. Finally, focus on both shallow and deep layer of understanding when analyzing. If we can find a suitable method to implement such measures, NLP would probably perform much better in near future.