SP500215 Incorporating Semantics Within a Connectionist Model and a Vector Processing Model chapter R. Boyd J. Driscoll National Institute of Standards and Technology D. K. Harman Incorporating Semanfics Within a Connectionist Model and a Vector Processing Model Richard Boyd, James Driscoll, mien Syu Department of Computer Science University of Central Florida Orlando, Florida 32816 (407)823-2341 FAX: (407)823-5419 e-mail: driscoll@cs.ucf.edu Abstract Semantic information obtained from the public domain 1911 version of Roget's Thesaurus is combined with key- words to measure similarity between natural language topics and documents. Two approaches are explored. In one approach, a combination of keyword relevance and semantic relevance is achieved by using the vector processing model for calculating similarity, but extending the use of a keyword weight by using individual weights for each of its meanings. This approach is based on the database concept of semantic modeling and the linguistic concept of thematic roles. It is applicable to both routing and archival retrievaL The second approach is especially suited for routing. It is based on an Al connectionist model. In this approach, a probabilistic inference network is modified using semantic information to achieve a competitive activation mechanism that can be used for calculating similarity. Keywords: vector processing model, semantic data model, semantic lexicon, inference network, connectionist model. 1 . Introduction The experiments reported here use a relatively efficient method to detect the semantic representation of text. Our original method is based on semantic modeling and is described in [4,17,19). Semantic modeling was an object of considerable database research in the late 1970's and early 1980's. Abriefoverview can be found in [3]. Essentially, the semantic modeling approach identified concepts useful in talking informally about the real world. These concepts included the two notions of entities (objects in the real world) and relationships among entities (actions in the real world). Both entities and rela- tionships have properties. The properties of entities are often called attributes. There are basic or surface level attributes for entities in the real world. Examples of surface level entity attributes are General Dimensions, Color, and Position. These properties are prevalent in natural language. For example, consider the phrase "large, black book on the table" which indicates the General Dimensions, Color, and Position of the book. In linguistic research, the basic properties of relationships are discussed and called thematic roles. Thematic roles are also referred to in the literature as participant roles, semantic roles and case roles. Examples of thematic roles are Ben[OCRerr] ficiary and Time. Thematic roles are prevalent in natural language; they reveal how sentence phrases and clauses are semantically related to the verbs in a sentence. For example, consider the phrase "purchase for Mary on Wednesday" which indicates who benefited from a purchase(13eneficiary) and when a purchase occurred (Fime). A main goal of our research has been to detect thematic information along with attribute information contained in natural language queries and documents. In order to use this additional information, the concept of text relevance needs to be modified. In [17,19] the major modifications included the addition of a lexicon with thematic and attribute information, and a modified computation of a vector processing similarity coefficient. That research concerned a Question/Answer environment where queries were the length of a sentence and documents were either a sentence or at most a paragraph. At that time, our lexicon was based on 36 semantic categories, and in that environment, our semantic approach produced a significant improvement in retrieval performance. However, for TREC-1 [4], document and topic length presented a problem and caused our semantic approach based on 36 semantic categories to be of little value. However, as reported in [4], by breaking the TREC documents into paragraphs, a significant improvement was demonstrated. This work has been supported in part by NASA KSC Cooperative Agreement NCC 10[OCRerr]3 Project 2, Florida High Technol- ogy and Industry Council Grants 494011-28-721 and 4940-1 1-2[OCRerr]728. 291