Aspect-level sentiment analysis or opinion mining consists of several core sub-tasks: aspect extraction, opinion identification, polarity classification, and separation of general and aspect-specific opinions. Various topic models have been proposed by researchers to address some of these sub-tasks. However, there is little work on modeling all of them together. In this paper, we first propose a holistic fine-grained topic model, called the JAST (Joint Aspectbased Sentiment Topic) model, that can simultaneously model all of above problems under a unified framework. To further improve it, we incorporate the idea of lifelong machine learning and propose a more advanced model, called the LAST (Lifelong Aspectbased Sentiment Topic) model. LAST automatically mines the prior knowledge of aspect, opinion, and their correspondence from other products or domains. Such knowledge is automatically extracted and incorporated into the proposed LAST model without any human involvement. Our experiments using reviews of a large number of product domains show major improvements of the proposed models over state-of-the-art baselines.
Abstract. This paper studies automatic extraction of structured data from Web pages. Each of such pages may contain several groups of structured data records. Existing automatic methods still have several limitations. In this paper, we propose a more effective method for the task. Given a page, our method first builds a tag tree based on visual information. It then performs a post-order traversal of the tree and matches subtrees in the process using a tree edit distance method and visual cues. After the process ends, data records are found and data items in them are aligned and extracted. The method can extract data from both flat and nested data records. Experimental evaluation shows that the method performs the extraction task accurately.
MG2 (the MUC7 gene product) is a low-molecular-mass mucin found in human submandibular/sublingual secretions. This mucin is believed to agglutinate a variety of microbes and thus is considered an important component of the non-immune host defence system in the oral cavity. We have shown that MUC7 can bind to cariogenic strains of Streptococcus mutans and that this binding requires a structural determinant in the N-terminal region. In the present study an expression construct, pNMuc7, encoding the N-terminal 144 amino acids of MUC7 was generated, and the recombinant protein rNMUC7 was expressed in Escherichia coli. Purified rNMUC7 was characterized and the binding of this protein to oral bacteria was investigated in an established assay. The results showed that the recombinant protein bound to S. mutans ATCC 25175 and ATCC 33402, and that alkylation of the two cysteine residues (Cys(45) and Cys(50)) resulted in the complete loss of bacterial binding. This suggests that binding of MUC7 to S. mutans occurs between the N-terminal region of the mucin molecule and the bacterial surface, and that this interaction is dependent on a cysteine-containing domain within this region of MUC7. In addition, the killing activity of rNMUC7 was compared with that of the candidacidal salivary protein histatin 5 in an established Candida albicans (ATCC 44505) blastoconidia killing assay. It was found that the LD(50) values of rNMUC7 and histatin 5 were comparable, and that the recombinant protein displayed significant killing activity at the physiological concentration range of MUC7 in whole saliva. This study is the first to show that the N-terminal region of MUC7 contains a structural determinant for bacterial binding and that this region exhibits candidacidal activity.
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