Redundancy: Helping Semantic Disambiguation

Redundancy: helping semantic disambiguation Caroline Barri~re School of Information Technology and Engineering University of Ottawa Ottawa, Canada, KIN 7Z3 barriere@site.uottawa.ca Abstract Redundancy is a good thing, at least in a learn- ing process. To be a good teacher you must say what you are going to say, say it, then say what you have just said. Well, three times is better than one. To acquire and learn knowl- edge from text for building a lexical knowledge base, we need to find a source of information that states facts, and repeats them a few times using slightly different sentence structures. A technique is needed for gathering information from that source and identify the redundant in- formation. The extraction of the commonality is an active learning of the knowledge expressed. The proposed research is based on a clustering method developed by Barri~re and Popowich (1996) which performs a gathering of related information about a particular topic. Individ- ual pieces of information are represented via the Conceptual Graph (CG) formalism and the re- sult of the clustering is a large CG embedding all individual graphs. In the present paper, we suggest that the identification of the redundant information within the resulting graph is very useful for disambiguation of the original infor- mation at the semantic level. 1 Introduction The construction of a Lexical Knowledge Base (LKB), if performed automatically (or semi- automatically), attempts at extracting knowl- edge from text. The extraction can be viewed as a learning process. Simplicity, clarity and re- dundancy of the information given in the source text are key features for a successful acquisition of knowledge. We assume success is attained when a sentence from the source text expressed in natural language can be transformed into an unambiguous internal representation. Using a conceptual graph (CG) representation (Sowa, 1984) of sentences means that a successful ac- quisition of knowledge corresponds to trans- forming each sentence from the source text into a set of unambiguous concepts (correct word senses found) and unambiguous relations (cor- rect semantic relations between concepts). This paper will look at the idea of making good use of the redundancy found in a text to help the knowledge acquisition task. Things are not always understood when they are first en- countered. A sentence expressing new knowl- edge might be ambiguous (at the level of the concepts it introduces and/or at the level of the semantic relations between those concepts). A search through previously acquired knowledge might help disambiguate the new sentence or it might not. A repetition of the exact same sen- tence would be of no help, but a slightly differ- ent format of expression might reveal necessary aspects for the comprehension. This is the av- enue explored in this paper which will unfold as follows. Section 2 will present briefly a pos- sible good source of knowledge and a gather- ing/clustering technique. Section 3 will present how the redundancy resulting from the cluster- ing process can be used in solving some types of semantic ambiguity. Section …

Content truncated to 500 words