In games like chess, the node-expansion strategy significantly affects the performance of a gameplaying program. In this article we propose a new game-tree search algorithm that uses the realization probabilities of nodes for deciding upon the range of the search. The realization probability of a node represents the probability that the moves leading to the node will actually be played. Our algorithm expands nodes as long as the realization probability of a node is greater than the threshold. Therefore, it spends little computational resource on unrealistic moves, resulting in a more effective search. We have implemented this algorithm in a Shogi-playing program. Experimental results show that the proposed algorithm achieves state-of-the-art performance on a standard test suite for computer Shogi. Moreover, its performance gain is equivalent to a speed-up of more than two.
BackgroundRelation extraction is a fundamental technology in biomedical text mining. Most of the previous studies on relation extraction from biomedical literature have focused on specific or predefined types of relations, which inherently limits the types of the extracted relations. With the aim of fully leveraging the knowledge described in the literature, we address much broader types of semantic relations using a single extraction framework.ResultsOur system, which we name PASMED, extracts diverse types of binary relations from biomedical literature using deep syntactic patterns. Our experimental results demonstrate that it achieves a level of recall considerably higher than the state of the art, while maintaining reasonable precision. We have then applied PASMED to the whole MEDLINE corpus and extracted more than 137 million semantic relations. The extracted relations provide a quantitative understanding of what kinds of semantic relations are actually described in MEDLINE and can be ultimately extracted by (possibly type-specific) relation extraction systems.ConclusionPASMED extracts a large number of relations that have previously been missed by existing text mining systems. The entire collection of the relations extracted from MEDLINE is publicly available in machine-readable form, so that it can serve as a potential knowledge base for high-level text-mining applications.Electronic supplementary materialThe online version of this article (doi:10.1186/s12859-015-0538-8) contains supplementary material, which is available to authorized users.
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