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This paper proposes an novel approach to annotate function tags for unparsed text. What distinguishes our work from other attempts in such task is that we assign function tags directly basing on lexical information other than on parsed trees. In order to demonstrate the effectiveness and versatility of our method, we investigate two statistical models for automatic annotation, one is log-linear maximum...
Entity relation extraction (RE) is an very important research domain in information extraction, we can regard RE as a classification problem in this paper, RE is still original study field in Chinese language now, maximum entropy (ME)-based machine learning is the first time to be used to extract entity relations between named entities from Chinese texts, Thirteen features have been designed for entity...
In the task of Chinese word segmentation, there are two main segmentation ambiguities, overlapping ambiguity and combination ambiguity. The paper analyzes properties of ambiguities and supposes multi-knowledge approach to disambiguate. Multi-knowledge refers to the knowledge from statistic of large corpus and syntactic, semantic or discourse information about ambiguous words. Class based N-gram and...
A specific prototype information service system was proposed by this paper, which can send interesting information to user with database search way from unstructured text. In order to achieve this goal, two fundamental issues were studied by using maximum entropy (ME) algorithm, which is named entity recognition and relation extraction. Our named entity recognition approach is distinguished from most...
In Chinese word segmentation task, combination ambiguity is one of challenges not being well settled. The main obstacle exists in the detection of ambiguous words in given texts and their proper segmentations. This paper puts forward a practical approach to automatically collecting ambiguous words and disambiguating based on maximum entropy principle. The experimental result reveals the approach of...
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