Browsing by Author "Katsova, Maria"
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Item A Comparative Study of Knowledge-Based Approaches for Cross-Language Information Retrieval(1998-10-15) Oard, Douglas W.; Dorr, Bonnie J.; Hackett, Paul G.; Katsova, MariaCross-language retrieval systems seek to use queries in one natural language to guide the retrieval of documents that might be written in another. Acquisition and representation of translation knowledge plays a central role in this process. This paper explores the utility of two sources of manually encoded translation knowledge, bilingual dictionaries and translation lexicons, for cross-language retrieval. We have implemented six query translation techniques that use bilingual dictionaries, one based on lexical-semantic analysis, and one based on direct use of the translation output from an existing machine translation system; these are compared with a document translation technique that uses output from the same existing translation system. Average precision measures on portions of the TREC collection suggest that arbitrarily selecting a single translation from a bilingual dictionary is typically no less effective than using every translation in the dictionary, that query translation using an existing machine translation system can achieve somewhat better effectiveness than simple dictionary-based techniques, and that performing document translation rather than query translation may result in further improvements in retrieval effectiveness under some conditions. (Also cross-referenced as UMIACS-TR-98-27)Item Lexical Selection for Cross-Language Applications: Combining LCS with WordNet(1998-10-15) Dorr, Bonnie J.; Katsova, MariaThis paper describes experiments for testing the power of large-scale resources for lexical selection in machine translation (MT) and cross-language information retrieval (CLIR). We adopt the view that verbs with similar argument structure share certain meaning components, but that those meaning components are more relevant to argument realization than to idiosyncratic verb meaning. We verify this by demonstrating that verbs with similar argument structure as encoded in Lexical Conceptual Structure (LCS) are rarely synonymous in WordNet. We then use the results of this work to guide our implementation of an algorithm for cross-language selection of lexical items, exploiting the strengths of each resource: LCS for semantic structure and WordNet for semantic content. We use the Parka Knowledge-Based System to encode LCS representations and WordNet synonym sets and we implement our lexical-selection algorithm as Parka-based queries into a knowledge base containing both information types. (Also cross-referenced as UMIACS-TR-98-49) (Also cross-referenced as LAMP-TR-021)