Chinese syntax has complex structure and high dimension features, and the best known Chinese parsing performance is still inferior to that of other western languages. In order to improve the efficiency and accuracy of Chinese parsing,we propose a L2-norm soft margin optimization structural support vector machines (structural SVMs) approach. By constructing the structural function ψ(x,y), the input information of syntactic tree can be mapped well. Since Chinese syntax has a strong correlation, we use father node of phrase structure trees to enrich the structure information of ψ(x,y). The experiment results on the benchmark dataset of PCTB demonstrate that the proposed approach is effective and efficient compared with classical Structural SVMs and Berkeley Parser system.

Question understanding of complex questions is a challenging issue in question answering system. For complex questions containing events (actions) information, this paper presents a question semantic representation (QSR) model based on semantic chunk. The semantic components of a complex question are labeled abstractly as the question focus, the question topic and the question event. A Semantic Structure of Question Event is then created to represent the semantic information of question event, including the question focus chunk, the question topic chunk and the question event chunk. To map the interrogative sentence into this question semantic representation, the Conditional Random Fields model is adopted for automatic semantic labeling of question semantic representation. The results show that automatic semantic labeling gains better performance.

This paper treat the phrase-level sentiment analysis as a sequence annotation problem, and proposes an extension model of conditional random fields, YACRFs, to annotate sentiment orientation of phrases. In contrast to previous works focusing on linear-chain CRFs, which corresponds to nite-state machines wtih efficient exact inference algorithms,we wish to label sequence data in multiple interacting ways—for example, performing word based semantic orientations tagging and phrase-level sentiment analysis simultaneously, increasing joint accuracy by sharing information between them. The proposed model incorporates the word emotional orientation analysis process and the phrase analysis through the incorporation of the features of polarity words, phrase rules template as well as part of speech characteristics. Experiments shows the proposed model performs best with an accuracy of 81.07%. And applied the results in sentence-level sentiment analysis, it brings again the best accuracy of 94.30%.

As a new media, Microblogging has been playing an indispensable role in people’s life. To extract sentimental information from the Microblogs, this paper introduces a two-stage CRF model and an iterative two-stage CRF model. The two-stage CRF model reaches an F-score of 0.505 on the COAE2014 evaluation data, and the iterative two-stage CRF model reaches an F-score up to 0.513 by an improvement in the recall.