Zobrazeno 1 - 10
of 24
pro vyhledávání: '"Anni R. Coden"'
Publikováno v:
Advances in Knowledge Discovery and Data Mining ISBN: 9783319930367
PAKDD (2)
PAKDD (2)
Extracting relations from unstructured Web content is a challenging task and for any new relation a significant effort is required to design, train and tune the extraction models. In this work, we investigate how to obtain suitable results for relati
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::2958f918efda045ff606daf03c478fe8
https://doi.org/10.1007/978-3-319-93037-4_29
https://doi.org/10.1007/978-3-319-93037-4_29
Publikováno v:
K-CAP
Ontologies are dynamic artifacts that evolve both in structure and content. Keeping them up-to-date is a very expensive and critical operation for any application relying on semantic Web technologies. In this paper we focus on evolving the content of
Publikováno v:
International Journal of Information Systems for Crisis Response and Management. 4:17-28
Looking Glass enables the discovery of a city’s vulnerabilities in a scenario along with the exploration of alternative resolutions and their accompanying side effects. It is a tool for enabling city officials to bridge the silos defined by people,
Autor:
Igor L. Sominsky, Anni R. Coden, Piet C. de Groen, Rie Johnson, Christopher G. Chute, Philip V. Ogren, Guergana Savova
Publikováno v:
Journal of Biomedical Informatics. 41(6):1088-1100
The aim of this study is to explore the word sense disambiguation (WSD) problem across two biomedical domains—biomedical literature and clinical notes. A supervised machine learning technique was used for the WSD task. One of the challenges address
Publikováno v:
International Journal of Medical Informatics. 75:418-429
Purpose This paper presents a project whose main goal is to construct a corpus of clinical text manually annotated for part-of-speech (POS) information. We describe and discuss the process of training three domain experts to perform linguistic annota
Autor:
Eric W. Brown, Anni R. Coden
Publikováno v:
Information Retrieval. 9:95-109
Streaming data poses a variety of new and interesting challenges for information retrieval and text analysis. Unlike static document collections, which are typically analyzed and indexed off-line to support ad-hoc queries, streaming data often must b
Autor:
Eric W. Brown, Yosi Mass, Naohiko Uramoto, Sougata Mukherjea, L. V. Subramaniam, Anni R. Coden, Hirofumi Matsuzawa, Bala Iyer, James W. Cooper, Aya Soffer, Robert L. Mack, Akihiro Inokuchi
Publikováno v:
IBM Systems Journal. 43:490-515
Biomedical text plays a fundamental role in knowledge discovery in life science, in both basic research (in the field of bioinformatics) and in industry sectors devoted to improving medical practice, drug development, and health care (such as medical
Publikováno v:
ACM SIGIR Forum. 36:10-13
We organized a workshop at SIGIR'01 to explore the area of information retrieval techniques for speech applications. Here we summarize the results of that workshop
Autor:
Cartic Ramakrishnan, Anni R. Coden, Steve Welch, Daniel Gruhl, Neal R. Lewis, Pablo N. Mendes, Meena Nagarajan
Publikováno v:
Communications in Computer and Information Science ISBN: 9783319120232
SemWebEval@ESWC
SemWebEval@ESWC
We have developed a prototype for sentiment analysis that is able to identify aspects of an entity being reviewed, along with the sentiment polarity associated to those aspects. Our approach relies on a core ontology of the task, augmented by a workb
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::9fe11367c6d07dc3946c330e5515f02e
https://doi.org/10.1007/978-3-319-12024-9_4
https://doi.org/10.1007/978-3-319-12024-9_4
Autor:
Shimei Pan, Ching-Yung Lin, Danny Soroker, Yinglong Xia, Jui-Hsin Lai, Justin D. Weisz, Julie MacNaught, Anni R. Coden, Wan-Yi Lin, Keith Houck, Jie Lu, Jeff Boston, Michael A. Tanenblatt, Steve Wood
Publikováno v:
IBM Journal of Research and Development. 60:8:1-8:11
We present a system to detect anomalous and ultimately malevolent behavior of people from their digital footprint within an institution. Tripwire approaches based on single features cannot adequately distinguish between normal unpredictable activitie