Incremental Disambiguation Model of Natural Language Analysis

Abstract

Semantic disambiguation is a difficult problem in natural language analysis. The meaning of words is ambiguous because it cannot be uniquely determined unless information of other words in the sentence is obtained. A possible strategy for semantic disambiguation is to determine the meaning of words at the end of the sentence. This strategy might cause combinatorial explosion of the number of possible meanings, if the sentence is long. So we think it is impracticable for a system to determine the meaning of words from a number of candidates after it has finished reading the whole sentence. A desirable strategy for semantic disambiguation is to accumurate information obtained during the analysis process of a sentence and disambiguate the meaning incrementally by using the information. In this paper, we propose such a computational model of natural language analysis, called incremental disambiguation model. In our model we introduce indeterminates which represent semantic ambiguity of words. Indeterminates enable underdetermined meaning of words to be incrementally determined by information obtained later. Primitive operation of the semantic disambiguation is realized by extended unification on indeterminates. Information used for disambiguation is considered to be constraints which restrict the meaning of words. Our model has the following features : analysing a sentence from left to right without any backtracking ; disambiguating the meaning incrementally by using constraints obtained during the analysis process. So it is clear that our computational model agree with constraint programming paradigm. Our computational model is regarded as an implementation of constraint programming by extending logic programming language Prolog.