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Experimental study of Chinese free-text IE algorithm based on WCA-selection using Hidden Markov model

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TLDR
A WCA-Selection Chinese free-text HMM IE algorithm that takes the Chinese Sci-tech journal abstract text as the extraction text and a WCA selection optimization strategy concreted is presented.
Abstract
This paper proposes the extraction task of the Chinese Sci-tech journal text and presents a WCA-Selection Chinese free-text HMM IE algorithm. The HMM IE algorithm takes the Chinese Sci-tech journal abstract text as the extraction text. According to the features of WCA, an idea of WCA selection model re-optimization is proposed. And a WCA selection optimization strategy is concreted. Then the experimental verification is conducted with a satisfied result. The experiment results show that the designed extraction algorithm and WCA selection optimization strategy have good performance in the the Chinese Sci-tech journal abstract text.

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References
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Book

Foundations of Statistical Natural Language Processing

TL;DR: This foundational text is the first comprehensive introduction to statistical natural language processing (NLP) to appear and provides broad but rigorous coverage of mathematical and linguistic foundations, as well as detailed discussion of statistical methods, allowing students and researchers to construct their own implementations.
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Kernel methods for relation extraction

TL;DR: This work introduces kernels defined over shallow parse representations of text, and design efficient algorithms for computing the kernels, and uses the devised kernels in conjunction with Support Vector Machine and Voted Perceptron learning algorithms for the task of extracting person-affiliation and organization-location relations from text.

Learning Hidden Markov Model Structure for Information Extraction

TL;DR: It is demonstrated that a manually-constructed model that contains multiple states per extraction field outperforms a model with one state per field, and the use of distantly-labeled data to set model parameters provides a significant improvement in extraction accuracy.
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Introduction to information extraction

TL;DR: An overview of the problems addressed, current approaches toward solutions, and the state of the art is assessed, and its potential for future progress is assessed.

BBN: Description of the SIFT System as Used for MUC-7

TL;DR: For MUC-7, BBN has for the first time fielded a fully-trained system for NE, TE, and TR; results are all the output of statistical language models trained on annotated data, rather than programs executing handwritten rules.
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