Automatic event-level textual emotion sensing using mutual action histogram between entities

International audienceAutomatic emotion sensing in textual data is crucial for the development of intelligent interfaces in many interactive computer applications. This paper describes a high-precision, knowledgebase-independent approach for automatic emotion sensing for the subjects of events embedded within sentences. The proposed approach is based on the probability distribution of common mutual actions between the subject and the object of an event. We have incorporated web-based text mining and semantic role labeling techniques, together with a number of reference entity pairs and hand-crafted emotion generation rules to realize an event emotion detection system. The evaluation outcome reveals a satisfactory result with about 85% accuracy for detecting the positive, negative and neutral emotions

Women with endometriosis have higher comorbidities: Analysis of domestic data in Taiwan

AbstractEndometriosis, defined by the presence of viable extrauterine endometrial glands and stroma, can grow or bleed cyclically, and possesses characteristics including a destructive, invasive, and metastatic nature. Since endometriosis may result in pelvic inflammation, adhesion, chronic pain, and infertility, and can progress to biologically malignant tumors, it is a long-term major health issue in women of reproductive age. In this review, we analyze the Taiwan domestic research addressing associations between endometriosis and other diseases. Concerning malignant tumors, we identified four studies on the links between endometriosis and ovarian cancer, one on breast cancer, two on endometrial cancer, one on colorectal cancer, and one on other malignancies, as well as one on associations between endometriosis and irritable bowel syndrome, one on links with migraine headache, three on links with pelvic inflammatory diseases, four on links with infertility, four on links with obesity, four on links with chronic liver disease, four on links with rheumatoid arthritis, four on links with chronic renal disease, five on links with diabetes mellitus, and five on links with cardiovascular diseases (hypertension, hyperlipidemia, etc.). The data available to date support that women with endometriosis might be at risk of some chronic illnesses and certain malignancies, although we consider the evidence for some comorbidities to be of low quality, for example, the association between colon cancer and adenomyosis/endometriosis. We still believe that the risk of comorbidity might be higher in women with endometriosis than that we supposed before. More research is needed to determine whether women with endometriosis are really at risk of these comorbidities

Discovering Informative Content Blocks from Web Documents

In this paper, we propose a new approach to discover informative contents from a set of tabular documents (or Web pages) of a Web site. Our system, InfoDiscoverer, first partitions a page into several content blocks according to HTML tag <TABLE> in a Web page. Based on the occurrence of the features (terms) in the set of pages, it calculates entropy value of each feature. According to the entropy value of each feature in a content block, the entropy value of the block is defined. By analyzing the information measure, we propose a method to dynamically select the entropy-threshold that partitions blocks into either informative or redundant. Informative content blocks are distinguished parts of the page, whereas redundant content blocks are common parts. Based on the answer set generated from 13 manually tagged news Web sites with a total of 26,518 Web pages, experiments show that both recall and precision rates are greater than 0.956. That is, using the approach, informative blocks (news articles) of these sites can be automatically separated from semantically redundant contents such as advertisements, banners, navigation panels, news categories, etc. By adopting InfoDiscoverer as the preprocessor of information retrieval and extraction applications, the retrieval and extracting precision will be increased, and the indexing size and extracting complexity will also be reduced

An agile approach for supply chain modeling

This paper proposes the generic label correcting (GLC) algorithm incorporated with the decision rules to solve supply chain modeling problems. The rough set theory is applied to reduce the complexity of data space and to induct decision rules. This proposed approach is agile because by combining various operators and comparators, different types of paths in the reduced networks can be solved with one algorithm. Furthermore, the four cases of the supply chain modeling are illustrated.Supply chain modeling Agility Generic label correcting algorithm Rough set

Entropy-based link analysis for mining web informative structures

In this paper, we study the problem of mining the informative structure of a news Web site which consists of thousands of hyperlinked documents. We define the informative structure of a news Web site as a set of index pages (or referred to as TOC, i.e., table of contents, pages) and a set of article pages linked by TOC pages through informative links. It is noted that the Hyperlink Induced Topics Search (HITS) algorithm has been employed to provide a solution to analyzing authorities and hubs of pages. However, most of the content sites tend to contain some extra hyperlinks, such as navigation panels, advertisements and banners, so as to increase the add-on values of their Web pages. Therefore, due to the structure induced by these extra hyperlinks, HITS is found to be insufficient to provide a good precision in solving the problem. To remedy this, we develop an algorithm to utilize entropy-based link analysis to mine Web informative structures. This algorithm is referred to as LAMIS, standing for entropy-based Link Analysis on Mining web Informative Structures. The key idea of LAMIS is to utilize information entropy for representing the knowledge that corresponds to the amount of information in a link or a page in the link analysis. Experiments on several real news Web sites show that the precision and recall of LAMIS is much superior to those obtained by heuristic methods and also that the link analysis techniques derived are very powerful to mining the informative structures of news Web sites. In average, the augmented LAMIS leads to prominent performance improvement and increases the precision by a factor ranging from 133 % to 232 % when the desired recall falls between 0.5 and 1