Introduction

In this chapter we will introduce some of the techniques that can be used to represent the kind of information that is needed for translation in such a way that it can be processed automatically. This will provide some necessary background for Chapter , where we describe how MT systems actually work.

Human Translators actually deploy at least five distinct kinds of knowledge:

• Knowledge of the source language.
• Knowledge of the target language. This allows them to produce texts that are acceptable in the target language.
• Knowledge of various correspondences between source language and target language (at the simplest level, this is knowledge of how individual words can be translated).
• Knowledge of the subject matter, including ordinary general knowledge  and `common sense' . This, along with knowledge of the source language, allows them to understand what the text to be translated means.
• Knowledge of the culture, social conventions, customs, and expectations, etc. of the speakers of the source and target languages.

This last kind of knowledge is what allows translators to act as genuine mediators, ensuring that the target text genuinely communicates the same sort of message, and has the same sort of impact on the reader, as the source text. Since no one has the remotest idea how to represent or manipulate this sort of knowledge, we will not pursue it here --- except to note that it is the lack of this sort of knowledge that makes us think that the proper role of MT is the production of draft or `literal' translations.

Knowledge of the target language is important because without it, what a human or automatic translator produces will be ungrammatical, or otherwise unacceptable. Knowledge of the source language is important because the first task of the human translator is to figure out what the words of the source text mean (without knowing what they mean it is not generally possible to find their equivalent in the target language).

It is usual to distinguish several kinds of linguistic knowledge:

• Phonological knowledge : knowledge about the sound system of a language, knowledge which, for example, allows one to work out the likely pronunciation of novel words. When dealing with written texts, such knowledge is not particularly useful. However, there is related knowledge about orthography  which can be useful. Knowledge about spelling is an obvious example.
• Morphological knowledge : knowledge about how words can be constructed: that printer is made up of print + er.
• Syntactic knowledge : knowledge about how sentences, and other sorts of phrases can be made up out of words.
• Semantic knowledge : knowledge about what words and phrases mean, about how the meaning of a phrase is related to the meaning of its component words.

However, some of the knowledge is about whole classes or categories of word. In this chapter, we will focus on this sort of knowledge about syntax  and semantics . Sections , and discuss syntax, issues relating to semantics are considered in Section . We will look first on how syntactic knowledge  of the source and target languages can be expressed so that a machine can use it. In the second part of the chapter, we will look at how this knowledge can be used in automatic processing of human language.