Machine translation (MT) has been studied and developed since the advent of computers, and yet is rarely used in actual business. For business use, rule-based MT has been developed, but it requires rules and a domain-specific dictionary that have been created manually. On the other hand, as huge amounts of text data have become available, corpus-based MT has been actively studied, particularly corpus-based statistical machine translation (SMT). In this study, we tested and verified the usefulness of SMT for aviation manuals. Manuals tend to be similar and repetitive, so SMT is powerful even with a small amount of training data. Although our experiments with SMT are at the preliminary stage, the BLEU score is high. SMT appears to be a powerful and promising technique in this domain.

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. These techniques do not necessarily use state-of-the-art technologies and achieve deep and accurate language understanding, but are based on huge amounts of linguistic resources, such as domain-specific lexical databases. In this paper, we introduce two techniques for extracting informative expressions from documents: the extraction of related words that are not only taxonomically related but also thematically related, and the acquisition of salient terms and phrases. With these techniques we then attempt to automatically and statistically extract domain-specific informative expressions in aviation documents as an example and evaluate the results.

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 is important from both a business perspective and an academic perspective. Intuitively, manuals for similar products, or manuals for different versions of the same product, are likely to resemble each other. Therefore, even with only a small training data, a corpus-based MT system can output useful translations. The corpus-based approach is powerful when the target is repetitive. Manuals for similar products, or manuals for different versions of the same product, are real-world documents that are repetitive. Our experiments on translation of manual documents are still in a beginning stage. However, the BLEU score from very small number of training sentences is already rather high. We believe corpus-based machine translation is a player full of promise in this kind of actual business scene.

Aiming to compile a thesaurus of adjectives, we discuss how to extract abstract nouns categorizing adjectives, clarify the semantic and syntactic functions of these abstract nouns, and manually evaluate the capability to extract the “instance-category” relations. We focused on some Japanese syntactic structures and utilized possibility of omission of abstract noun to decide whether or not a semantic relation between an adjective and an abstract noun is an “instance-category” relation. For 63% of the adjectives (57 groups/90 groups) in our experiments, our extracted categories were found to be most suitable. For 22 % of the adjectives (20/90), the categories in the EDR lexicon were found to be most suitable. For 14% of the adjectives (13/90), neither our extracted categories nor those in EDR were found to be suitable, or examinees’ own categories were considered to be more suitable. From our experimental results, we found that the correspondence between a group of adjectives and their category name was more suitable in our method than in the EDR lexicon.

The EDR electronic dictionary is a machine-tractable dictionary developed for advanced computer-based processing of natural lan-guage. This dictionary comprises eleven sub-dictionaries, including a concept dictionary, word dictionaries, bilingual dictionaries, co-occurrence dictionaries, and a technical terminology dictionary. In this study, we focus on the concept dictionary and aim to revise the arrangement of concepts for improving the EDR electronic dictionary. We believe that unsuitable concepts in a class differ from other concepts in the same class from an abstract perspective. From this notion, we first try to automatically extract those concepts unsuited to the class. We then try semi-automatically to amend the concept explications used to explain the meanings to human users and rearrange them in suitable classes. In the experiment, we try to revise those concepts that are the lower-concepts of the concept “human” in the concept hierarchy and that are directly arranged under concepts with concept explications such as “person as defined by –” and “person viewed from –.” We analyze the result and evaluate our approach.