Open Relation Extraction (ORE) overcomes the limitations of traditional IE techniques, which train individual extractors for every single relation type. Systems such as ReVerb, PATTY, OLLIE, and Exemplar have attracted much attention on English ORE. However, few studies have been reported on ORE for languages beyond English. This paper presents a syntax-based Chinese (Zh) ORE system, ZORE, for extracting relations and semantic patterns from Chinese text. ZORE identifies relation candidates from automatically parsed dependency trees, and then extracts relations with their semantic patterns iteratively through a novel double propagation algorithm. Empirical results on two data sets show the effectiveness of the proposed system.

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ZORE: A Syntax-based System for Chinese Open Relation Extraction

Relation extraction is the task of finding semantic relations between two entities from text. In this paper, we propose a novel feature-based Chinese relation extraction approach that explicitly defines and explores nine positional structures between two entities. We also suggest some correction and inference mechanisms based on relation hierarchy and co-reference information etc. The approach is effective when evaluated on the ACE 2005 Chinese data set.

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A Novel Feature-based Approach to Chinese Entity Relation Extraction

Implementation of a Kernel-Based Chinese Relation Extraction System

Named entity relation extraction is an important subject in the field of information extraction. Although many English extractors have achieved reasonable performance, an effective system for Chinese relation extraction remains undeveloped due to the lack of Chinese annotation corpora and the specificity of Chinese linguistics. Here, we summarize three kinds of unique but common phenomena in Chinese linguistics. In this article, we investigate unsupervised linguistics-based Chinese open relation extraction (ORE), which can automatically discover arbitrary relations without any manually labeled datasets, and research the establishment of a large-scale corpus. By mapping the entity relations into dependency-trees and considering the unique Chinese linguistic characteristics, we propose a novel unsupervised Chinese ORE model based on Dependency Semantic Normal Forms (DSNFs). This model imposes no restrictions on the relative positions among entities and relationships and achieves a high yield by extracting relations mediated by verbs or nouns and processing the parallel clauses. Empirical results from our model demonstrate the effectiveness of this method, which obtains stable performance on four heterogeneous datasets and achieves better precision and recall in comparison with several Chinese ORE systems. Furthermore, a large-scale knowledge base of entity and relation, called COER, is established and published by applying our method to web text, which conquers the trouble of lack of Chinese corpora.

Chinese Open Relation Extraction and Knowledge Base Establishment

Feature assembly method for extracting relations in Chinese

In this paper, a novel kernel-based method is presented for the problem of relation extraction between named entities from Chinese texts The kernel is defined over the original Chinese string representations around particular entities As a kernel function, the Improved-Edit-Distance (IED) is used to calculate the similarity between two Chinese strings By employing the Voted Perceptron and Support Vector Machine (SVM) kernel machines with the IED kernel as the classifiers, we tested the method by extracting person-affiliation relation from Chinese texts By comparing with traditional feature-based learning methods, we conclude that our method needs less manual efforts in feature transformation and achieves a better performance

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Jianmin Jiang

Papers

Improved-Edit-Distance Kernel for Chinese Relation Extraction