Translingual Information Management by Natural Language Processing

Noun AND Quantifier Preposition Abstract-Noun] in the conjunct scope of the "and" in (4). Some patterns are represented by semantic features such as [Instrument AND Instrument] in the "and 1 " of (5). (4) The container need not be large; if it is 10 cm in diameter and 12 cm in depth, that is enough. (5) Inspect the cockpit indicators and 1 levers for cracked glass and missing control knobs. ffl Morphological symmetric patterns: Morphological symmetric patterns are recognized by the sorts of letters, uppercase or lowercase letters, as in (6) and (7), as well as the exact same morphological pattern [CIC ... hatches AND CIC ..., hatches] in (8). (6) An atomic bomb is a device for producing an explosively rapid neutron chain reaction in uranium-235 or plutonium-239 which is called a fissile material. (7) Technical orders described in AFR 8-2 and PFR 7-2 are registered in the on-line file in the form of inspection workcards. (8) There are CIC 1 ditching 2 hatches 3 and CIC 4 escape 5 hatc..

Building Japanese-English Dictionary based on Ontology for Machine Translation

This paper describes a semi-automatic method for associating a Japanese lexicon with a semantic concept taxonomy called an ontology, using a Japanese-English bilingual dictionary as a "bridge". The ontology supports semantic processing in a knowledge-based machine translation system by providing a set of language-neutral symbols and semantic information. To put the ontology to practical use, lexical items of each language of interest must be linked to appropriate ontology items. The association of ontology items with lexical items of various languages is a process fraught with difficulty: since much of this work depends on the subjective decisions of hu-man workers, large MT dictionaries tend to be subject to some dispersion and inconsistency. The problem we focus on here is how to associate concepts in the ontology with Japanese lexical entities by automatic methods, since it is too difficult to define adequately many concepts manually. We have designed three algorithms to associate a Japanese lexicon with the concepts of the ontology automatically: the equivalent-word match, the argument match, and the exam-ple match. We simulated these algorithms for 980 nouns, 860 verbs and 520 adjectives as preliminary experiments. The algorithms axe found to be effective for more than 80 % of the words

Information Retrieval System for TREC3

Introduction This is our first participation with TREC. Our team researches natural language processing, and we have developed English-Japanese and Japanese-English machine translation system. (The code name of machine translation system is VENUS.) We are now researching a new natural language processing environment, including information retrieval and text understanding. (The environment name is VIRTUE: VENUS for Information Retrieval and Text Understanding.) Last year, our team participated with MUC5, and we got promising results[1]. This year, Information Retrieval Text Understanding Machine Translation VENUS MUC5 System TREC3 System Natural Language Processing Environment:VIRTUE Figure 1. Our Goal our team has participated with the TREC3 for researching and developing and information retrieval system. Figure 1 shows our goal of researching natural language environment. Because of our past development of machine translation systems, we have on hand transla