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.

A Meaningful Information Extraction System for Interactive Analysis of Documents This paper is related to a project aiming at discovering weak signals from different streams of information, possibly sent by whistleblowers. The study presented in this paper tackles the particular problem of clustering topics at multilevels from multiple documents, and then extracting meaningful descriptors, such as weighted lists of words for document representations in a multidimensions space. In this context, we present a novel idea which combines Latent Dirichlet Allocation and Word2vec (providing a consistency metric regarding the partitioned topics) as potential method for limiting the a priori number of cluster K usually needed in classical partitioning approaches. We proposed 2 implementations of this idea, respectively able to: (1) finding the best K for LDA in terms of topic consistency; (2) gathering the optimal clusters from different levels of clustering. We also proposed a nontraditional visualization approach based on a multiagents system which combines both dimension reduction and interactivity.