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SVM and TSVM Based Chinese Entity Relation Extraction

Author: XuFen
Tutor: WangTing
School: National University of Defense Science and Technology
Course: Computer Science and Technology
Keywords: Information Extraction Entity relation extraction SVM TSVM Feature Selection The number of training examples Multiple classifiers
CLC: TP391.1
Type: Master's thesis
Year: 2007
Downloads: 232
Quote: 6
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Automatic unstructured text information extraction technology into the structure of the text, either own system to meet the strong demand, or other applications such as information retrieval, text classification, automatic question answering and other important basic technology. Entity relation extraction is an important link in the information extraction technology is becoming more and more popular research topic. Chinese entity relation extraction work is still in its infancy, there is a lot of work needs to be done. In this paper, the characteristics of the Chinese entity-relationship, design a series of characteristics, including word tagging, entity attributes, and referred to information, overlapping relationships between entities and HowNet provide conceptual information to form the characteristics of the context of the relationship between entities vector and SVM classifier for Chinese entity relation extraction. ACE2004 training corpus as experimental data to obtain good recognition performance. According to the results of the classification experiments investigated in detail the performance of various feature sets and different number of training examples Chinese entities. The experimental results show that: the tasks of different degree of refinement should select a different degree of abstraction feature set combination. POS Feature sets are more suited to relationship discovery tasks, the the HowNet concept of feature set than for the relationship between categories and subcategories recognition task, word feature set is a basic set of features, the overlap feature sets between entities extraction performance for the greatest contribution. Increase in the size of the training corpus can improve recognition performance, the development of large-scale training corpus, it is necessary to use the SVM classifier; However, when the corpus reaches a certain size, of Corpus scale increase performance weakened, then the should be the main focus on the feature set constructed. On the basis of the above study, for SVM dependence on large-scale training corpus, the introduction of semi-supervised learning methods TSVM to Chinese entity relation extraction. Experimental results show that, far more than the number of training vectors hours the TSVM The performance SVM, TSVM performance but not as good as SVM, but a large number of training vectors. TSVM classifier using only a small amount of annotation corpus and a large number of unlabeled corpus, you can get a good performance and reduce the cost of extraction system to improve its portability; found such a relatively simple question in the relationship, but in more complex relationship categories to identify issues TSVM classifier performance is still not satisfactory, should consider additional semi-supervised learning method. At the same time of this study and to achieve a the TSVM multi-classifier constructed. Further work include two aspects, one is to improve the existing feature set as more features such as group block identification, the HowNet concept of structure is added to the feature set to improve the relation extraction performance and more precise parameter selection, quantitative research dimension data selection performance SVM and TSVM requirements, annotation data size law.

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CLC: > Industrial Technology > Automation technology,computer technology > Computing technology,computer technology > Computer applications > Information processing (information processing) > Text Processing
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