Takaki Makino

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We explore the use of Support Vector Machines (SVMs) for biomedical named entity recognition. To make the SVM training with the available largest corpus – the GENIA corpus – tractable, we propose to split the nonentity class into sub-classes, using part-of-speech information. In addition , we explore new features such as word cache and the states of an HMM(More)
This paper presents the LiLFeS system, an efficient feature-structure description language for HPSG. The core engine of LiLFeS is an Abstract Machine for Attribute-Value Logics, proposed by Carpenter and Qu. Basic design policies, the current status, and performance evaluation of the LiLFeS system are described. The paper discusses two implementations of(More)
We propose a computational theory on estimating the internal states of others, which is the basis of information processing in human communication. To estimate internal states of peers, we have to deal with two considerable difficulties, restricted dimension of estimator parameters and conversion of objective information into subjective. To solve these(More)
We consider apprenticeship learning — i.e., having an agent learn a task by observing an expert demonstrating the task — in a partially observable environment when the model of the environment is uncertain. This setting is useful in applications where the explicit modeling of the environment is difficult , such as a dialogue system. We show that we can(More)
We propose a Deep Belief Net model for robust motion generation, which consists of two layers of Restricted Boltzmann Machines (RBMs). The lower layer has multiple RBMs for encoding real-valued spatial patterns of motion frames into compact representations. The upper layer has one conditional RBM for learning temporal constraints on transitions between(More)
According to theories of cultural neuroscience, Westerners and Easterners may have distinct styles of cognition (e.g., different allocation of attention). Previous research has shown that Westerners and Easterners tend to utilize analytical and holistic cognitive styles, respectively. On the other hand, little is known regarding the cultural differences in(More)
We have developed a new series of multi-agent reinforcement learning algorithms that choose a policy based on beliefs about co-players' policies. The algorithms are applicable to situations where a state is fully observable by the agents, but there is no limit on the number of players. Some of the algorithms employ embedded beliefs to handle the cases that(More)
Various language processing algorithms have been studied to find the algorithm used in the human language understanding, but no algorithm has proven its existence by physiological evidences. In such a situation, we should consider an approach to pursue implementational constraints and preferences from a computational theory of the language understanding(More)
This paper describes an indexing substrate for typed feature structures (ISTFS), which is an efficient retrieval engine for typed feature structures. Given a set of typed feature structures, the ISTFS efficiently retrieves its subset whose elements are unifiable or in a subsumption relation with a query feature structure. The efficiency of the ISTFS is(More)