Two Languages Are More Informative Than One

Two Languages Are More Informative Than One * Ido Dagan Computer Science Department Technion, Haifa, Israel and IBM Scientific Center Haifa, Israel dagan@cs.technion.ac.il Alon Itai Computer Science Department Technion, Haifa, Israel itai~cs.technion.ac.il Ulrike Schwall IBM Scientific Center Institute for Knowledge Based Systems Heidelberg, Germany schwall@dhdibml Abstract This paper presents a new approach for resolving lex- ical ambiguities in one language using statistical data on lexical relations in another language. This ap- proach exploits the differences between mappings of words to senses in different languages. We concen- trate on the problem of target word selection in ma- chine translation, for which the approach is directly applicable, and employ a statistical model for the se- lection mechanism. The model was evaluated using two sets of Hebrew and German examples and was found to be very useful for disambiguation. 1 Introduction The resolution of hxical ambiguities in non-restricted text is one of the most difficult tasks of natural lan- guage processing. A related task in machine trans- lation is target word selection - the task of deciding which target language word is the most appropriate equivalent of a source language word in context. In addition to the alternatives introduced from the dif- ferent word senses of the source language word, the target language may specify additional alternatives that differ mainly in their usages. Traditionally various linguistic levels were used to deal with this problem: syntactic, semantic and pragmatic. Computationally the syntactic methods are the easiest, but are of no avail in the frequent situation when the different senses of the word show *This research was partially supported by grant number 120-741 of the Iarael Council for Research and Development the same syntactic behavior, having the same part of speech and even the same subcategorization frame. Substantial application of semantic or pragmatic knowledge about the word and its context for broad domains requires compiling huge amounts of knowl- edge, whose usefulness for practical applications has not yet been proven (Lenat et al., 1990; Nirenburg et al., 1988; Chodorow et al., 1985). Moreover, such methods fail to reflect word usages. It is known for many years that the use of a word in the language provides information about its meaning (Wittgenstein, 1953). Also, statistical approaches which were popular few decades ago have recently reawakened and were found useful for computational linguistics. Consequently, a possible (though partial) alternative to using manually constructed knowledge can be found in the use of statistical data on the oc- currence of lexical relations in large corpora. The use of such relations (mainly relations between verbs or nouns and their arguments and modifiers) for var- ious purposes has received growing attention in re- cent research (Church and Hanks, 1990; Zernik and Jacobs, 1990; Hindle, 1990). More specifically, two recent works have suggested to use statistical data on lexical relations for resolving ambiguity cases of PP-attachment (Hindle and Rooth, 1990) and pro- noun references (Dagan and Itai, 1990a; Dagan and Itai, 1990b). Clearly, statistical methods can be useful also for target word …

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