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.
I submitted my first blog at a wrong address, which is zhangmuren-5720iems.blogspot.hk. Here is a corrected version
回覆刪除I have learned a lot from your blog~ and the NLP thoughts are brilliant, I hope to talk with you about this method~thanks for your share!
回覆刪除Thanks for your introduction of NLP.I won't say ‘hehe' but 'haha' to you because of your share.
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回覆刪除I totally agrees with you,the sentiment analysis is more than complicated since the people feel differently even for the same word and pictures.As you mentioned in the blog 'hehe' does means a lot.To me,it is quite irony to use hehe to express thanks.So maybe the data should be colloected from a group of people with the similar personalities about there usage of the words.Then the usage of words in this group can be applied to analyse this special group of people
回覆刪除Thank you for your sharing. I have learned a lot. natural language processing is so hard. How to get the emotion behind the language is the hardest part form my view. And wish there has some opportunity we can do some experiment in that direction. Thanks again.
回覆刪除Computer is not robust and if computer has to process some complex words in a certain environment it can be a tough job for computer. Your article enlightened me a lot and thank you for your ideas.
回覆刪除Good introduction for NLP. The examples explain what NLP are doing intuitively. Good job.
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