Session 4: Natural Language

Collectively, the papers in this session are mainly concerned with parsing, semantic interpretation, and inference. Interest in these processes can be motivated if we recognize that the overall goal of the field of NLP is the manipulation of natural language in ways that depend on meaning. Parsing, the recovery of the linguistic or grammatical structure of a natural-language utterance, is of concern to NLP because, in general, the meaning of a natural-language utterance depends on its structure. An example that illustrates this point is the sentence The man with the umbrella opened the door. If we tried to process this sentence without paying attention to its grammatical structure, we could easily be misled by the fact that it contains the substring ~he umbrella opened. But this sentence has nothing to do with umbrellas opening. Because of the structure of the sentence, the umbrella must be grouped with with and not with opened. If the central concern of NLP is the manipulation of natural language in ways that depend on meaning, then semantic interpretation would naturally be expected to play a central role. In practice, semantic interpretation in NLP usually means recovering a representation of the meaning of an utterance that encodes that meaning in a more transparent way than does the utterance itself. IIow does this contribute to the goal of manipulating language in meaning-dependent ways? We want to have algorithms that manipulate language according to meaning, but meaning is ultimately an abstraction that algorithms can have no direct access to. Algorithms can directly manipulate expressions only according to their structure. Thus we need expressions whose structure corresponds in a very direct way to their meaning. While, as we have argued above, the meaning of a natural-language expression is dependent on its structure , this dependence can be very indirect. By recovering an expression that encodes the meaning of an utterance more directly, we can create modular algorithms that consist of interacting pieces that each look only at a small piece of the structure of the meaning representation. If the pieces of the meaning representation fit together in a natural way that reflects the overall meaning of the utterance , then the algorithms that manipulate them will also be able to fit together in a natural way that reflects the overall meaning of the utterance. Finally, inference is the pay-off for the previous phases of parsing and semantic interpretation, being …