Kunihiko Sadamasa

Event relation acquisition using dependency patterns and confidence-weighted co-occurrence statistics

Event relation knowledge is important for deep language understanding and inference. Previous work has established automatic acquisition methods of event relations that focus on common sense knowledge acquisition from large-scale unlabeled corpus. However, in the case of domain-specific knowledge acquisition, such a method can not acquire much knowledge due to the limited amount of available knowledge sources. We propose an coverage-oriented acquisition method of event relations. The proposed method utilizes various patterns of dependency structures co-occurring with event relations than the existing method relying only on direct dependency relations between events. Experimental results show that the proposed method can acquire a larger amount of positive relation instances while keeping higher precision compared with the existing method and the proposed method also performs well for small sizes of corpora.

Cross-lingual Text Classification Using Topic-Dependent Word Probabilities.

Cross-lingual text classification is a major challenge in natural language processing, since often training data is available in only one language (target language), but not available for the language of the document we want to classify (source language). Here, we propose a method that only requires a bilingual dictionary to bridge the language gap. Our proposed probabilistic model allows us to estimate translation probabilities that are conditioned on the whole source document. The assumption of our probabilistic model is that each document can be characterized by a distribution over topics that help to solve the translation ambiguity of single words. Using the derived translation probabilities, we then calculate the expected word frequency of each word type in the target language. Finally, these expected word frequencies can be used to classify the source text with any classifier that was trained using only target language documents. Our experiments confirm the usefulness of our proposed method.