Abstract:
A University of Illinois project is developing an infrastructure for indexing scientific literature so that multiple Internet sources can be searched as a single federated digital library.
Abstract:
Archives of computer-mediated communication (CMC) could be valuable organizational resources. Most CMC archive systems focus on presenting one of the three aspects of a CMC community: discussion content, participants' behavior, or social networks among participants. Very few CMC archive systems support the easy integration of these three aspects. This paper thus describes two-phase research to propose an automatic approach that facilitates users' integrated understanding of discussion content and behavior of CMC participants. We validated the approach through the development and evaluation of a prototype system, the Communication-Garden system. © 2008 Elsevier B.V. All rights reserved.
Abstract:
Dark Web forums are heavily used by extremist and terrorist groups for communication, recruiting, ideology sharing, and radicalization. These forums often have relevance to the Iraqi insurgency or Al-Qaeda and are of interest to security and intelligence organizations. This paper presents an automated approach to sentiment and affect analysis of selected radical international Jihadist Dark Web forums. The approach incorporates a rich textual feature representation and machine learning techniques to identify and measure the sentiment polarities and affect intensities expressed in forum communications. The results of sentiment and affect analysis performed on two large-scale Dark Web forums are presented, offering insight into the communities and participants. ©2008 IEEE.
Abstract:
The increasing amount of publicly available literature and experimental data in biomedicine makes it hard for biomedical researchers to stay up-to-date. Genescene is a toolkit that will help alleviate this problem by providing an overview of published literature content. We combined a linguistic parser with Concept Space, a co-occurrence based semantic net. Both techniques extract complementary biomedical relations between noun phrases from MEDLINE abstracts. The parser extracts precise and semantically rich relations from individual abstracts. Concept Space extracts relations that hold true for the collection of abstracts. The Gene Ontology, the Human Genome Nomenclature, and the Unified Medical Language System, are also integrated in Genescene. Currently, they are used to facilitate the integration of the two relation types, and to select the more interesting and high-quality relations for presentation. A user study focusing on p53 literature is discussed. All MEDLINE abstracts discussing p53 were processed in Genescene. Two researchers evaluated the terms and relations from several abstracts of interest to them. The results show that the terms were precise (precision 93%) and relevant, as were the parser relations (precision 95%). The Concept Space relations were more precise when selected with ontological knowledge (precision 78%) than without (60%). © 2005 Wiley Periodicals, Inc.
