Inference and Interpretation

Inference and interpretation are logic processes that require examining the input language, comparing it with knowledge that has already been accumulated, and drawing a conclusion. To get to this stage, the analysis techniques already discussed need to be run, and an internal representation of the discourse needs to exist. (see diagram from Luger & Stubblefield). To get to this stage, the analysis techniques already discussed need to be run, and an internal representation of the discourse needs to exist. (see diagram from Luger & Stubblefield).

This discourse processing uses functions of predicate logic to draw general conclusions. For a machine to draw sensible conclusions it is necessary to interpret the incoming data correctly, and thoroughly so that there is as much data as possible from which to draw a conclusion. There are two methods that are especially useful for extracting information from the natural language source: reference and context.

Reference

By considering the references of certain objects in a sentences, a machine can determine the linguistically expressible interdependencies between sentences in a discourse. Reference is the most important means of linking sentences in a discourse (Popov, 1982). The procedure for analysing reference is:

  1. establish where in the context we should seek the entity that is denoted by the given reference
  2. establish how to determine that a given referent and the given reference correspond to reach other (Popov, 1982)

Using reference, we are able to determine know which pronouns are referring to which previously described object. For example: "He lent Jan some money. She was very grateful". A machine would be able to to determine that she referred to Jan by calculating reference (Popov, 1982).

Context

By considering context while processing natural language, we are able to interpret the meaning of a sentence from a connected text by placing that individual sentence in context. If an individual sentence being processed is not related to context, that sentence may have several different meanings, or it might be totally incomprehensible.

There are many levels of context that need to be considered when processing natural language (Popov, 1982):

textual context is the meaning derived from the sentences preceding the current sentence

situational context is the meaning from the current sentence, and is usually only given implicitly.

global context is like the topic of the conversations and allows an algorithm to choose between several meanings (such as "bark" would be chosen differently if we were talking about dogs, as opposed to talking about trees)

local context is the meaning derived from only the few preceding sentences, this is useful because the topic of the conversation may progress. Local context provides the most recent topic.

A simple algorithm for processing context and reference is not really possible since a form of 'fuzzy' processing is required. Thus researchers are experimenting with neural networks to train a computer to recognise certain common situations (called frames) and also to generalise about new situations.

Now that the machine has a basis for accurately determining which objects are being referred to and how, when and where those objects are interacting we have reached a stage that some level of understanding is possible. This gives us the opportunity for an accurate translation to be possible from natural language to a machine readable form, and then to a different natural language.