This paper deals with a discourse structure analysis of technical Japanese texts for developing a Japanese writing Computer Assisted Language Learning (CALL) system whose goal is to assist students in learning to write technical Japanese texts. To analyze discourse structures of technical Japanese texts, cohesive expressions are used as cue words. The rules for analyzing texts are based on micr...

Automated language identification of written text is a wellestablished research domain that has received considerable attention in the past. By now, efficient and effective algorithms based on character n-grams are in use, mainly with identification based on Markov models or on character n-gram profiles. In this paper we investigate the limitations of these approaches when applied to real-world...

This paper describes an approach to information retrieval based on a syntactic analysis of the document texts and user queries, and from that analysis, the construction of tree structures (TSAs) to encode and capture language ambiguities. TSAs are constructed at the clause level and thus each document can yield many TSAs and each query may be represented by several TSAs. The TSAs from documents...

Current web technology has brought us a scenario that information about a certain topic is widely dispersed in data from different domains and data modalities, such as texts and images from news and social media. Automatic extraction of the most informative and important multimedia summary (e.g. a ranked list of inter-connected texts and images) from massive amounts of cross-media and cross-gen...

Ontologies that have been built from texts can be associated with lexical information that is crucial for the semantic annotation of texts and all semantic search tasks. However, the entire pocess of building ontologies from texts cannot be fully automated and it is important to guide the knowledge engineer during the building process. This paper presents an enriched version of TERMINAE, which ...

In this paper, we consider the problem of inductively learning rules from specific facts extracted from texts. This problem is challenging due to two reasons. First, natural texts are radically incomplete since there are always too many facts to mention. Second, natural texts are systematically biased towards novelty and surprise, which presents an unrepresentative sample to the learner. Our so...

In this paper, we consider the problem of inductively learning rules from specific facts extracted from texts. This problem is challenging due to two reasons. First, natural texts are radically incomplete since there are always too many facts to mention. Second, natural texts are systematically biased towards novelty and surprise, which presents an unrepresentative sample to the learner. Our so...