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With the development of natural language processing (NLP) technology, the need for automatic named entity recognition (NER) is highlighted in order to enhance the performance of information extraction systems. In this paper, a hybrid model for Chinese person based on conditional random fields model is proposed, which fuses multiple features. It differentiates from most of the previous approaches,...
Entity relation extraction is the important research field in many languages processing, such as the knowledge management, semantic network, information retrieval and information extraction. In this paper, some specific entity relations can be extracted in the football match domain, firstly, the definition of the five types relation had been defined. Based on the conditional random fields, aiming...
Entity relation extraction (RE) is one of the important research fields in information extraction, we regard RE as a classification problem in this paper. This paper presents a novel approach, conditional random fields (CRFs)-based machine learning is used to extract entity relation between entities from Chinese texts, ten features have been designed for entity relation extraction, which includes...
This paper proposes a new approach for personal name recognition in Chinese language domain. Combining rule-based and statistical method, we consider wonderful linguistics knowledge; firstly step, we collect personal name as candidate entity, and send it into statistical model to decide whether it is the relevant entity, the conditional random fields (CRFs) is used in this paper. At the same time,...
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