Entity-Relation Extraction for Chinese Based on Pattern Evolution and PageRank

Current methods for entity-relation extraction can be broadly classified into three categories: Kernel-based method which is supervised one, Bootstrap-based method which is semi-supervised one and clustering-based method which is unsupervised one. Kernel function, also known as the similarity function, is the promotion of the nuclear matrix and inner product. It is often used as classification function of some classification algorithms such as SVM, Voted Perceptron. In all Kernel-based methods, the kernel function directly affects the effects of extraction. So, how to construct a high-quality Kernel function becomes the focus of the study. According to the matched content, these kernels can be divided into the latent semantic kernel, principal component analysis kernel, word-sequence kernel, tree kernel, as well as directed acyclic graph kernel. These methods based on word-sequence kernel function[4] use the number of public sub-word sequence about two word-sequence to measure the similarity. Because this method does not consider the structure of sentences, it need be improved further. These methods based on tree kernel function[6] use shallow parsing techniques to obtain the sentence syntax tree firstly, and then calculate the similarity according to the number of common sub-tree in the syntax tree. Composite kernels[1] are developed to integrate and extend individual kernels such as argument kernel, Bigram kernel, link sequence kernel, dependency path kernel and local dependency kernel so that processing errors occurring at one level can be overcome by information from other levels. Edit-distance kernel[5] is used to calculate the at least required number that one string is changed into another string by inserting characters, deleting characters and replacing characters in one string. The less changed number implies the two more similar strings. As one of supervised methods, kernel-based machine learning methods usually need train a large number of manual tagging of data to determine the similarity threshold, which is not feasible for entity-relation extraction in open domain. So Brin[3] first proposed the semi-supervised method named DIPRE(Dual Iterative Pattern Relation Extraction) for entity-relation extraction. In DIPRE, a few entity-pairs appointed by user are used to search WWW for their contexts firstly. And then these contexts are used to search WWW for new entity-pairs in turn. So repeatedly, the algorithm terminate until no new entity pairs is found or the max iteration number is achieved. [2] improve DIPRE by carefully analyzing the entity type, extraction patterns, as well as the relationship between them. But a few used thresholds such as Tsim, Tsup, Wupdate,etc are difficultly determined. The advantages of semi-supervised methods are to avoid the heavy work of the artificial mark and do not need parse structure of sentence, so the methods has a very high efficiency. The inadequacy is that the quality of extraction patterns and entity-pairs is difficult to control in iteration process so that precision rate is far below than those supervised methods. In this paper we further advance the work of Brin for entity-relation extraction for Chinese. Synonymy relation and hyponymy relation defined in HowNet[7] are used to evolve extraction patterns in order to make the collection of extraction patterns more expressive. In addition, we draw on the idea of the PageRank algorithm to sort the final acquisition of the entity-pairs in accordance with the level of credibility in order to ensure a global optimal.