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    Natural Language Processing :

    Developing programs to understand natural language is important in AI because a natural form of communication with systems is essential for user acceptance. One of the most critical tests for intelligent behavior is the ability to communicate effectively. This was the test proposed by Alan Turing. AI programs must be able to communicate with their human counterparts in a natural way, and natural language is one of the most important mediums for that purpose. A program understands a natural language if it behaves by taking a correct or acceptable action in response to the input. For example, we say a child demonstrates understanding if it responds with the correct answer to a question. The action taken need not be the external response. It may be the creation of some internal data structures. The structures created should be meaningful and correctly interact with the world model representation held by the program. In this chapter we explore many of the important issues related to natural language understanding and language generation.

    Natural languages are the languages used by humans for communication (among other functions). They are distinctly different from formal languages, such as C++, Java, and PROLOG. One of the main differences, which we will examine in some detail in this chapter, is that natural languages are ambiguous, meaning that a given sentence can have more than one possible meaning, and in some cases the correct meaning can be very hard to determine. Formal languages are almost always designed to ensure that ambiguity cannot occur. Hence, a given program written in C++ can have only one interpretation. This is clearly desirable because otherwise the computer would have to make an arbitrary decision as to which interpretation to work with. It is becoming increasingly important for computers to be able to understand natural languages. Telephone systems are now widespread that are able to understand a narrow range of commands and questions to assist callers to large call centers, without needing to use human resources. Additionally, the quantity of unstructured textual data that exists in the world (and in particular, on the Internet) has reached unmanageable proportions. For humans to search through these data using traditional techniques such as Boolean queries or the database query language SQL is impractical. The idea that people should be able to pose questions in their own language, or something similar to it, is an increasingly popular one.

    Of course, English is not the only natural language. A great deal of research in natural language processing and information retrieval is carried out in English, but many human languages differ enormously from English. Languages such as Chinese, Finnish, and Navajo have almost nothing in common with English (although of course Finnish uses the same alphabet). Hence, a system that can work with one human language cannot necessarily deal with any other human language. In this section we will explore two main topics. First, we will examine natural language processing, which is a collection of techniques used to enable computers to “understand” human language. In general, they are concerned with extracting grammatical information as well as meaning from human utterances but they are also concerned with understanding those utterances, and performing useful tasks as a result. Two of the earliest goals of natural language processing were automated translation (which is explored in this chapter) and database access. The idea here was that if a user wanted to find some information from a database, it would make much more sense if he or she could query the database in her language, rather than needing to learn a new formal language such as SQL. Information retrieval is a collection of techniques used to try to match a query (or a command) to a set of documents from an existing corpus of documents. Systems such as the search engines that we use to find data on the Internet use information retrieval (albeit of a fairly simple nature).

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