Hitoshi Nishikawa

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This paper presents grammar error correction for Japanese particles that uses discriminative sequence conversion, which corrects erroneous particles by substitution, insertion, and deletion. The error correction task is hindered by the difficulty of collecting large error corpora. We tackle this problem by using pseudoerror sentences generated(More)
In this paper we propose a novel algorithm for opinion summarization that takes account of content and coherence, simultaneously. We consider a summary as a sequence of sentences and directly acquire the optimum sequence from multiple review documents by extracting and ordering the sentences. We achieve this with a novel Integer Linear Programming (ILP)(More)
We propose a novel algorithm for sentiment summarization that takes account of informativeness and readability, simultaneously. Our algorithm generates a summary by selecting and ordering sentences taken from multiple review texts according to two scores that represent the informativeness and readability of the sentence order. The informativeness score is(More)
It has been suggested that occlusal interference results in masticatory muscle dysfunction. In our previous study, occlusal interference reduced the rat masseter energy level during masticatory movements. The purpose of this study was to investigate the histological alterations of rat masseter muscles following experimental occlusal alteration with(More)
This paper proposes a novel extractive summarization method for contact center dialogues. We use a particular type of hidden Markov model (HMM) called Class Speaker HMM (CSHMM), which processes operator/caller utterance sequences of multiple domains simultaneously to model domain-specific utterance sequences and common (domainwide) sequences at the same(More)
In this paper we introduce a novel single-document summarization method based on a hidden semi-Markov model. This model can naturally model single-document summarization as the optimization problem of selecting the best sequence from among the sentences in the input document under the given objective function and knapsack constraint. This advantage makes it(More)
This paper reports the improvements we made to our previously proposed hidden Markov model (HMM) based summarization method for multi-domain contact center dialogues. Since the method relied on Viterbi decoding for selecting utterances to include in a summary, it had the inability to control compression rates. We enhance our method by using the(More)
Anterior mediastinal hemangiomas are very rare neoplasms in mediastinal tumors. A 58-year-old woman was revealed to have a mass measuring 4 x 3 cm in size in the anterior mediastinum with calcification on computed tomography. It was initially suspected to be a thymoma. We performed tumor extirpation in November 1998. The tumor was close to the thymus and(More)
In this paper, a novel type of radial basis function network is proposed for multitask pattern recognition. We assume that recognition tasks are switched sequentially without notice to a learner and they have relatedness to some extent. We further assume that training data are given to learn one by one and they are discarded after learning. To learn a(More)