Natural Language Processing
Natural language processing is a field of artificial intelligence and linguistics. The main task of natural language processing is to deal with the interactions between computers and human languages. This is one entire separate field in computer science, the filed it related to is human based interaction. The main problems of natural language processing is that how to ensure computers can proceed and understand human beings’ intention and order under natural language, that is, enabling computers to derive meaning from human or natural input.
Nowadays, machine learning is the foundation for natural language processing because machine learning is quite different from the technique we used before to deal with natural language processing. Compared to ask natural language processing to run directly in the hand-coding of large set of rules, machine learning uses general learning algorithms automatically learn the rules from real world and projects. Machine learning is also a branch of artificial intelligence and it concerns the construction and study of systems that can learn from data. The most common topics in natural language processing involved: machine translation, which involves translating the in put text from one human language to another. This is one of the most difficult task for natural language processing because it not only requires a large set of knowledge, but also need to know how to deal with the rules contained in different human language. For example, Chinese, Japanese and Arabic, all of them have totally different language system. It is something even could not be finished by human yet. Another common topic in natural language processing is automatic summarization, which means computers need to read from the text and then gives out a readable summary of the text to human being. This is an often used application of natural language processing. There is also a topic named natural language understanding. It is somewhat like automatic summarization, but it is much more complicated than it. It requires computers to read and understand more than one text and create another text could be used for a more formal situation. It is to say that natural language processing is going to do something empirical work. Other commonly known applications are: conference resolution, name entity recognition, natural language generation, natural language understanding, optical character recognition, question answering, speech recognition and sentiment analysis.
One of the most difficult natural language processing is facing now is if can solve the central intelligence problem. That means computers have to be as intelligent as human being. It is still a very large unexplored area in natural language processing. I think it is a very good topic for every computer scientists to explore.
1.What are the untapped applications of natural language processing?
2.How far has machine translation gone?
3.How accurate is the statement that solving the natural language processing is equivalent to solving the AI problem?