Akira Terada

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Unknown words such as proper nouns, abbreviations, and acronyms are a major obstacle in text processing. Abbreviations, in particular, are difficult to read/process because they are often domain-specific. In this paper, we propose a method for automatic expansion of abbreviations by using context and character information. In previous studies dictionaries(More)
What kinds of lexical resources are helpful for extracting useful information from domain-specific documents? Although domain-specific documents contain much useful knowledge, it is not obvious how to extract such knowledge efficiently from the documents. We need to develop techniques for extracting hidden information from such domain-specific documents.(More)
As huge quantities of documents have become available, services using natural language processing technologies trained by huge corpora have emerged, such as information retrieval and information extraction. In this paper we verify the usefulness of resource-based, or corpus-based, translation in the aviation domain as a real business situation. This study(More)
Nominalization is a linguistic phenomenon in which events usually described in terms of clauses are expressed in the form of noun phrases. Extracting event structures is an important task in text mining applications. To achieve this goal, clauses are parsed and the argument structure of main verbs are extracted from the parsed results. This kind of(More)
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