Concept extraction for structured text using entropy weight method With the rapid development of computer and communication, data mining and related fields are facing huge challenges. In the research of data mining and text analysis, how to extract concepts more objectively and reasonably is a key issue. Existing approaches seem to lack the intensive analysis of structural nature of structured text, which will affect the effectiveness and accuracy of text analysis. This paper proposes a novel approach of structured text analysis and concept extraction using entropy weight method. It quantitatively assesses the contribution of each module in evaluating the weight of concept. Based on the position of the concept in the text, concepts can be mined more effectively. Furthermore, the method is applied to the field of academic text analysis to verify its effectiveness and advantages over traditional methods. And the experimental results also prove that the method can provide solid theoretical and technical support for personalized academic information services

Exploiting MultiDocument Term Extraction to Improve Named Entity Recognition for Major Concept Detection With worldwide events happening everyday, itxe2x80x99s useful to discover the most discussed topics for intelligence and trend analysis. However, existing news media only provide manually annotated tags related to each article. Given huge amount of Web data with new or unknown terms, itxe2x80x99s critical if these topicrelated keywords can be automatically extracted in an efficient way. In this paper, we propose a multidocument term extraction approach to major concept detection. First, major keywords occurring in multiple documents are extracted by statistical term extraction methods. These are then utilized as the dictionary for word segmentation to improve named entity recognition, by which the most relevant concepts in each single document can be extracted. Finally, the extracted concepts are ranked by their importance score based on their popularity and average position in documents. In the experimental results for major online news sources in Chinese, exploiting multidocument term extraction helps to improve the effectiveness of named entity recognition, with the best F1measure of 0.61, a significant 24% improvement over the baseline. By combining the popularity with average position of terms as the importance score, we able to obtain the best precision of 68% when keeping the top25% content in documents. Keyword extraction results for selected major events further validated the effectiveness of our proposed approach using topicrelated news articles. Further investigation is needed to evaluate the performance of the proposed approach with various data sources.