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Finding an effective way to represent human actions is yet an open problem because it usually requires taking evidences extracted from various temporal resolutions into account. A conventional way of representing an action employs temporally ordered fine-grained movements, e.g., key poses or subtle motions. Many existing approaches model actions by directly learning the transitional relationships...
Medical records of Traditional Chinese Medicine (TCM) are usually free text and unstructured data, how to extract medical terms from TCM medical records based on conditional random fields is an interesting problem. TCM medical records obtained from dermatology in Guangdong Provincial Hospital of Chinese Medicine are segmented to single words and labeled with grammatical properties of words by TCM...
Chinese chunking is defined as a task to automatically segment Chinese sentences into small chunks which hold semantic meanings. To improve the performance of Chinese chunking, we propose an approach to use frequently used words (FUW) for Chinese chunking. We use conditional random fields for chunking, and modified the training corpus according to the frequency of the words in it. Finally we devise...
Hidden Markov model (HMM) is successfully used in speech recognition. However, there is an unavoidable flaw in assuming strong independence for sequences labeling in HMM. While conditional random fields (CRFs) can relax this assumption for HMM, and can also solve the label bias problem efficiently. In this paper, we investigate CRFs for Chinese syllable recognition in continuous speech due to its...
Anaphora resolution plays an important role in nature language processing. According to features of Chinese personal pronoun, we present an approach which adopts Conditional Random Fields to do anaphora resolution of personal pronoun in Chinese texts. The method takes into account all kinds of anaphoric features and those effects among each other. The experimental results on the Chinese ACE training...
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