Kernel approaches for genic interaction extraction
Kernel approaches for genic interaction extraction
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DOI：
10.1093/bioinformatics/btm544
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发表时间：
2008-01-01
期刊：
影响因子：
5.8
通讯作者：
Yang, Jihoon
中科院分区：
文献类型：
--
作者：
Kim, Seonho;Yoon, Juntae;Yang, Jihoon
动机：自动的知识发现和高效的信息访问，如命名实体识别和实体之间的关系提取，是近年来生物医学文献中的关键问题。然而，由于自然语言的多样性，关系提取任务的固有困难在生物医学领域进一步加剧，因为生物医学领域的句子通常很长很复杂。此外，关系提取通常涉及对长距离依赖关系、不连续的单词模式和语义关系进行建模，而基于模式的方法并不直接适用于这些关系。结果：本文将生物医学关系提取的重点从模式提取问题转移到核构建问题。我们建议使用四个核：谓词核、遍历核、依赖核和混合核，以充分封装基于两个实体中涉及的句子结构的关系预测所需的信息。为此，我们将句子的依赖关系结构看作是一个图，它允许系统通过寻找实体之间的最短路径，从复杂的句法结构中处理最重要的一个。我们提出的核从平面特征描述逐渐扩展到最短路径的结构描述。结果，我们得到了一个非常有希望的结果，在逻辑语言学习（LLL） 05基因交互共享任务上，walk kernel获得了77.5的f分。
Motivation: Automatic knowledge discovery and efficient information access such as named entity recognition and relation extraction between entities have recently become critical issues in the biomedical literature. However, the inherent difficulty of the relation extraction task, mainly caused by the diversity of natural language, is further compounded in the biomedical domain because biomedical sentences are commonly long and complex. In addition, relation extraction often involves modeling long range dependencies, discontiguous word patterns and semantic relations for which the pattern-based methodology is not directly applicable.Results: In this article, we shift the focus of biomedical relation extraction from the problem of pattern extraction to the problem of kernel construction. We suggest four kernels: predicate, walk, dependency and hybrid kernels to adequately encapsulate information required for a relation prediction based on the sentential structures involved in two entities. For this purpose, we view the dependency structure of a sentence as a graph, which allows the system to deal with an essential one from the complex syntactic structure by finding the shortest path between entities. The kernels we suggest are augmented gradually from the flat features descriptions to the structural descriptions of the shortest paths. As a result, we obtain a very promising result, a 77.5 F-score with the walk kernel on the Language Learning in Logic (LLL) 05 genic interaction shared task.