Consensus-based Approach for Keyword Extraction from Urban Events Collections

Automatic keyword extraction (AKE) from textual sources took a valuable step towards harnessing the problem of efficient scanning of large document collections. Particularly in the context of urban mobility, where the most relevant events in the city are advertised on-line, it becomes difficult to know exactly what is happening in a place./nIn this paper we tackle this problem by extracting a set of keywords from different kinds of textual sources, focusing on the urban events context. We propose an ensemble of automatic keyword extraction systems KEA (Key-phrase Extraction Algorithm) and KUSCO (Knowledge Unsupervised Search for instantiating Concepts on lightweight Ontologies) and Conditional Random Fields (CRF)./nUnlike KEA and KUSCO which are well-known tools for automatic keyword extraction, CRF needs further pre-processing. Therefore, a tool for handling AKE from the documents using CRF is developed. The architecture for the AKE ensemble system is designed and efficient integration of component applications is presented in which a consensus between such classifiers is achieved. Finally, we empirically show that our AKE ensemble system significantly succeeds on baseline sources and urban events collections

Applying semantic web technologies to knowledge sharing in aerospace engineering

This paper details an integrated methodology to optimise Knowledge reuse and sharing, illustrated with a use case in the aeronautics domain. It uses Ontologies as a central modelling strategy for the Capture of Knowledge from legacy docu-ments via automated means, or directly in systems interfacing with Knowledge workers, via user-defined, web-based forms. The domain ontologies used for Knowledge Capture also guide the retrieval of the Knowledge extracted from the data using a Semantic Search System that provides support for multiple modalities during search. This approach has been applied and evaluated successfully within the aerospace domain, and is currently being extended for use in other domains on an increasingly large scale

Topic Tracking for Punjabi Language

This paper introduces Topic Tracking for Punjabi language. Text mining is a field that automatically extracts previously unknown and useful information from unstructured textual data. It has strong connections with natural language processing. NLP has produced technologies that teach computers natural language so that they may analyze, understand and even generate text. Topic tracking is one of the technologies that has been developed and can be used in the text mining process. The main purpose of topic tracking is to identify and follow events presented in multiple news sources, including newswires, radio and TV broadcasts. It collects dispersed information together and makes it easy for user to get a general understanding. Not much work has been done in Topic tracking for Indian Languages in general and Punjabi in particular. First we survey various approaches available for Topic Tracking, then represent our approach for Punjabi. The experimental results are shown

Adverse event detection by integrating twitter data and VAERS

Background: Vaccinehasbeenoneofthemostsuccessfulpublichealthinterventionstodate.However,vaccines are pharmaceutical products that carry risks so that many adverse events (AEs) are reported after receiving vaccines. Traditional adverse event reporting systems suffer from several crucial challenges including poor timeliness. This motivates increasing social media-based detection systems, which demonstrate successful capability to capture timely and prevalent disease information. Despite these advantages, social media-based AE detection suffers from serious challenges such as labor-intensive labeling and class imbalance of the training data. Results: Totacklebothchallengesfromtraditionalreportingsystemsandsocialmedia,weexploittheircomplementary strength and develop a combinatorial classification approach by integrating Twitter data and the Vaccine Adverse Event Reporting System (VAERS) information aiming to identify potential AEs after influenza vaccine. Specifically, we combine formal reports which have accurately predefined labels with social media data to reduce the cost of manual labeling; in order to combat the class imbalance problem, a max-rule based multi-instance learning method is proposed to bias positive users. Various experiments were conducted to validate our model compared with other baselines. We observed that (1) multi-instance learning methods outperformed baselines when only Twitter data were used; (2) formal reports helped improve the performance metrics of our multi-instance learning methods consistently while affecting the performance of other baselines negatively; (3) the effect of formal reports was more obvious when the training size was smaller. Case studies show that our model labeled users and tweets accurately. Conclusions: WehavedevelopedaframeworktodetectvaccineAEsbycombiningformalreportswithsocialmedia data. We demonstrate the power of formal reports on the performance improvement of AE detection when the amount of social media data was small. Various experiments and case studies show the effectiveness of our model