In the best of all possible worlds (as far as most Natural Language Processing is concerned, anyway) every word would have one and only one meaning. But, as we all know, this is not the case. When a word has more than one meaning, it is said to be lexically ambiguous. When a phrase or sentence can have more than one structure it is said to be structurally ambiguous.
Ambiguity is a pervasive phenomenon in human languages. It is very hard to find words that are not at least two ways ambiguous, and sentences which are (out of context) several ways ambiguous are the rule, not the exception. This is not only problematic because some of the alternatives are unintended (i.e. represent wrong interpretations), but because ambiguities `multiply'. In the worst case, a sentence containing two words, each of which is two ways ambiguous may be four ways ambiguous (), one with three such words may be , ways ambiguous etc. One can, in this way, get very large numbers indeed. For example, a sentence consisting of ten words, each two ways ambiguous, and with just two possible structural analyses could have different analyses. The number of analyses can be problematic, since one may have to consider all of them, rejecting all but one.
Fortunately, however, things are not always so bad. In the rest of this section we will look at the problem in more detail, and consider some partial solutions.
Imagine that we are trying to translate these two sentences into French :
In the first sentence use is a verb, and in the second a noun, that is, we have a case of lexical ambiguity. An English-French dictionary will say that the verb can be translated by (inter alia) se servir de and employer, whereas the noun is translated as emploi or utilisation. One way a reader or an automatic parser can find out whether the noun or verb form of use is being employed in a sentence is by working out whether it is grammatically possible to have a noun or a verb in the place where it occurs. For example, in English, there is no grammatical sequence of words which consists of the + V + PP --- so of the two possible parts of speech to which use can belong, only the noun is possible in the second sentence ( b).
As we have noted in Chapter , we can give translation engines such information about grammar, in the form of grammar rules. This is useful in that it allows them to filter out some wrong analyses. However, giving our system knowledge about syntax will not allow us to determine the meaning of all ambiguous words. This is because words can have several meanings even within the same part of speech. Take for example the word button. Like the word use, it can be either a verb or a noun. As a noun, it can mean both the familiar small round object used to fasten clothes, as well as a knob on a piece of apparatus. To get the machine to pick out the right interpretation we have to give it information about meaning.
In fact, arming a computer with knowledge about syntax, without at the same time telling it something about meaning can be a dangerous thing. This is because applying a grammar to a sentence can produce a number of different analyses, depending on how the rules have applied, and we may end up with a large number of alternative analyses for a single sentence. Now syntactic ambiguity may coincide with genuine meaning ambiguity, but very often it does not, and it is the cases where it does not that we want to eliminate by applying knowledge about meaning.
We can illustrate this with some examples. First, let us show how grammar rules, differently applied, can produce more than one syntactic analysis for a sentence. One way this can occur is where a word is assigned to more than one category in the grammar. For example, assume that the word cleaning is both an adjective and a verb in our grammar. This will allow us to assign two different analyses to the following sentence.
One of these analyses will have cleaning as a verb, and one will have it as an adjective. In the former (less plausible) case the sense is `to clean a fluid may be dangerous', i.e. it is about an activity being dangerous. In the latter case the sense is that fluids used for cleaning can be dangerous. Choosing between these alternative syntactic analyses requires knowledge about meaning.
It may be worth noting, in passing, that this ambiguity disappears when can is replaced by a verb which shows number agreement by having different forms for third person singular and plural. For example, the following are not ambiguous in this way: ( a) has only the sense that the action is dangerous, ( b) has only the sense that the fluids are dangerous.
We have seen that syntactic analysis is useful in ruling out some wrong analyses, and this is another such case, since, by checking for agreement of subject and object, it is possible to find the correct interpretations. A system which ignored such syntactic facts would have to consider all these examples ambiguous, and would have to find some other way of working out which sense was intended, running the risk of making the wrong choice. For a system with proper syntactic analysis, this problem would arise only in the case of verbs like can which do not show number agreement.
Another source of syntactic ambiguity is where whole phrases, typically prepositional phrases, can attach to more than one position in a sentence. For example, in the following example, the prepositional phrase with a Postscript interface can attach either to the NP the word processor package, meaning ``the word-processor which is fitted or supplied with a Postscript interface'', or to the verb connect, in which case the sense is that the Postscript interface is to be used to make the connection.
Notice, however, that this example is not genuinely ambiguous at all, knowledge of what a Postscript interface is (in particular, the fact that it is a piece of software, not a piece of hardware that could be used for making a physical connection between a printer to an office computer) serves to disambiguate. Similar problems arise with ( ), which could mean that the printer and the word processor both need Postscript interfaces, or that only the word processor needs them.
This kind of real world knowledge is also an essential component in disambiguating the pronoun it in examples such as the following
In order to work out that it is the printer that is to be switched on, rather than the paper, one needs to use the knowledge of the world that printers (and not paper) are the sort of thing one is likely to switch on.
There are other cases where real world knowledge , though necessary, does not seem to be sufficient. The following, where two people are re-assembling a printer, seems to be such an example:
It is not clear that any kind of real world knowledge will be enough to work out that it in the last sentence refers to the cartridge, rather than the new paper, or toner. All are probably equally reasonable candidates for fixing. What strongly suggests that it should be interpreted as the cartridge is the structure of the conversation --- the discussion of the toner and new paper occurs in a digression, which has ended by the time it occurs. Here what one needs is knowledge of the way language is used. This is knowledge which is usually thought of as pragmatic in nature. Analysing the meaning of texts like the above example is important in dialogue translation, which is a long term goal for MT research, but similar problems occur in other sorts of text.
Another sort of pragmatic knowledge is involved in cases where the translation of a sentence depends on the communicative intention of the speaker --- on the sort of action (the speech act) that the speaker intends to perform with the sentence. For example, ( ) could be a request for action, or a request for information, and this might make a difference to the translation.
In some cases, working out which is intended will depend on the non-linguistic situation, but it could also depend on the kind of discourse that is going on --- for example, is it a discourse where requests for action are expected, and is the speaker in a position to make such a request of the hearer? In dialogues, such pragmatic information about the discourse can be important for translating the simplest expressions. For example, the right translation of Thank you into French depends on what sort of speech act it follows. Normally, one would expect the translation to be merci. However, if it is uttered in response to an offer, the right translation would be s'il vous plaît (`please').