We propose a supervised method of extracting event causalities like conduct slash-and-burn
agriculture→ exacerbate desertification from the web using semantic relation (between …

We propose a semi-supervised learning method for improving why-question answering (why-
QA). The key of our method is to generate training data (question-answer pairs) from causal …

This paper proposes a method for intrasentential subject zero anaphora resolution in
Japanese. Our proposed method utilizes a Multi-column Convolutional Neural Network …

A self-adaptive classifier for efficient text-stream processing is proposed. The proposed
classifier adaptively speeds up its classification while processing a given text stream for …

Event causality knowledge is indispensable for intelligent natural language understanding.
The problem is that any method for extracting event causalities from text is insufficient; it is …

Accurate neural models are much less efficient than non-neural models and are useless for
processing billions of social media posts or handling user queries in real time with a limited …

In this work, we improve the performance of intra-sentential zero anaphora resolution in
Japanese using a novel method of recognizing subject sharing relations. In Japanese, a …

Chunks (or phrases) once played a pivotal role in machine translation. By using a chunk
rather than a word as the basic translation unit, local (intra-chunk) and global (inter-chunk) …

Background Despite being a pandemic, the impact of the spread of COVID-19 extends
beyond public health, influencing areas such as the economy, education, work style, and …

This paper proposes an efficient online method that trains a classifier with many conjunctive
features. We employ kernel computation called kernel slicing, which explicitly considers …