Towards incremental disambiguation with a generalized discrimination network
Manabu Okumura, Hozumi Tanaka · National Conference on Artificial Intelligence · 1990
Semantic disambiguation is a difficult problem in natural language analysis. A better strategy for semantic disambiguation is to accumulate constraints obtained during the analytical process of a sentence, and disambiguate as early as possible the meaning incrementally using the constraints. We propose such a computational model of natural language analysis, and call it the 'incremental disambiguation model.' The semantic disambiguation process can be equated with the downward traversal of a discrimination network. However, the discrimination network has a problem in that it cannot be traversed unless constraints are entered in an a priori-fixed order. In general, the order in which constraints are obtained cannot be a priori fixed, so it is not always possible to traverse the network downward during the analytical process. In this paper, we propose a method which can traverse the discrimination network according to the order in which constraints are obtained incrementally during the analytical process. This order is independent of the a priori-fixed order of the network.