Daisuke Okanohara

Learn More
This paper presents techniques to apply semi-CRFs to Named Entity Recognition tasks with a tractable computational cost. Our framework can handle an NER task that has long named entities and many labels which increase the computational cost. To reduce the computational cost, we propose two techniques: the first is the use of feature forests, which enables(More)
Estimating the conditional mean of an input-output relation is the goal of regression. However, regression analysis is not sufficiently informative if the conditional distribution has multi-modality, is highly asymmetric, or contains heteroscedastic noise. In such scenarios, estimating the conditional distribution itself would be more useful. In this paper,(More)
We propose a perceptron-style algorithm for fast discriminative training of structured latent variable model, and analyzed its convergence properties. Our method extends the perceptron algorithm for the learning task with latent dependencies, which may not be captured by traditional models. It relies on Viterbi decoding over latent variables, combined with(More)
Shallow parsing is one of many NLP tasks that can be reduced to a sequence labeling problem. In this paper we show that the latent-dynamics (i.e., hidden substructure of shallow phrases) constitutes a problem in shallow parsing, and we show that modeling this intermediate structure is useful. By analyzing the automatically learned hidden states, we show how(More)
Associating literature with pathways poses new challenges to the Text Mining (TM) community. There are three main challenges to this task: (1) the identification of the mapping position of a specific entity or reaction in a given pathway, (2) the recognition of the causal relationships among multiple reactions, and (3) the formulation and implementation of(More)