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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. This chapter explores several techniques that are used to enable humans to interact with computers via natural human languages. 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,
Phonology. This is needed only if the computer is required to understand spoken language. Phonology is the study of the sounds that make up words and is used to identify words from sounds. We will explore this in a little more detail later, when we look at the ways in which computers can understand speech. Morphology. This is the first stage of analysis that is applied to words, once they have been identified from speech, or input into the system. Morphology looks at the ways in which words break down into components and how that affects their grammatical status. For example, the letter “s” on the end of a word can often either indicate that it is a plural noun or a third-person present-tense verb. Syntax. This stage involves applying the rules of the grammar from the language being used. Syntax determines the role of each word in a sentence and, thus, enables a computer system to convert sentences into a structure that can be more easily manipulated. Semantics. This involves the examination of the meaning of words and sentences. As we will see, it is possible for a sentence to be syntactically correct but to be semantically meaningless. Conversely, it is desirable that a computer system be able to understand sentences with incorrect syntax but that still convey useful information semantically.
Pragmatics. This is the application of human-like understanding to sentences and discourse to determine meanings that are not immediately clear from the semantics. For example, if someone says, “Can you tell me the time?”, most people know that “yes” is not a suitable answer. Pragmatics enables a computer system to give a sensible answer to questions like this. In addition to these levels of analysis, natural language processing systems must apply some kind of world knowledge. In most real-world systems, this world knowledge is limited to a specific domain (e.g., a system might have detailed knowledge about the Blocks World and be able to answer questions about this world). The ultimate goal of natural language processing would be to have a system with enough world knowledge to be able to engage a human in discussion on any subject. This goal is still a long way off.
