University of Manitoba: description of the NUBA system as used for MUC-5

Abduction is the inference to the best explanation . Many tasks in natural language understanding such as word-sense disambiguity [1], local pragmatics [4], metaphor interpretation [3], and plan recognition [5, 8] , can be viewed as abduction . NUBA (Natural-language Understanding By Abduction) is a natural language understanding system , where syntactic, semantic, and discourse analysis are performed by abductive inference . The task of understanding is viewed as finding the structural relationships between unstructured inputs . That is, to understand is to seek the best explanation of how the inputs are coherently related . From this abductive perspective , the goal of the parser is to explain how the words in a sentence are structured according to syntactic relationships; the goal of the semantic interpreter is to find the semantic relationships among the content word s in a sentence ; the goal discourse analyzer is to show how the events mentioned in the sentences fit togethe r to form a coherent plan . Although the abductive formulation of natural language understanding tasks results in significant simpli fications [4], the computational complexity of abductive inference presents a serious problem . Our solution to this problem is obvious abduction [6], a model of abductive inference that covers the kinds of abductiv e inferences people perform without apparent effort, such as parsing, plan recognition, and diagnosis . Obvious abduction uses a network to represent the domain knowledge . Observations correspond to nodes in th e network annotated with a set of attribute-value pairs . An explanation of the observations is a generalize d subtree of the network that connects all the observations . This connection is a coherent set of relationships between the observations, therefore, explains how they are related .