Beyond pattern recognition of individual alphanumeric characters, whether they be of fixed font or handwritten, lies the problem of understanding strings of characters that form words, sentences, or larger assemblages of text in a “natural” language, such as English. To distinguish languages such as English from the languages used by computers, the former are usually called “natural languages.” In artificial intelligence, “understanding” natural language input usually means either converting it to some kind of memory model (such as the one used by Raphael in his SIR system or the semantic network used by Quillian) or the evocation of some action appropriate to the input.

Natural languages are spoken as well as written. And, because speech sounds are not as well segmented as are the characters printed on a page, speech understanding presents additional difficulties, which I'll describe in a later chapter.

The inverse of understanding natural language input is generating natural language output – both written and spoken. Translating from one language to another involves both understanding and generation. So does carrying on a conversation. All of these problems – understanding, generation, translation, and conversing – fall under the general heading of “natural language processing” (sometimes abbreviated as NLP).

Linguistic Levels

Linguists and others who study language recognize several levels at which language can be analyzed. These levels can be arranged in a sort of hierarchy, starting with those dealing with the most basic components of language (sounds and word parts) and proceeding upward to levels dealing with sequences of sentences. If speech is being dealt with, there are the levels of phonetics (language sounds) and phonology (organization of sounds into words).