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Articles 1 - 7 of 7
Full-Text Articles in Library and Information Science
Transformation Based Learning For Specialization Of Generic Event Extractions, Mary D. Taffet, Nancy Mccracken, Eileen Allen, Elizabeth D. Liddy
Transformation Based Learning For Specialization Of Generic Event Extractions, Mary D. Taffet, Nancy Mccracken, Eileen Allen, Elizabeth D. Liddy
School of Information Studies - Faculty Scholarship
As part of our Evidence Extraction and Link Discovery (EELD) project, we proposed to use Transformation Based Learning (TBL) to learn domain-specific specializations for generic event extractions. The primary goal of our learning task was to reduce the amount of human effort required for specializing generic event extractions to domains that are new and specific. Three initial annotation cycles and one annotation review and correction cycle involving a total of 70 documents were completed, with slightly over 32 hours required for the entire annotation effort; where possible, the annotation cycles started with bootstrapped files resulting from the application of TBL …
Concept Tree Based Clustering Visualization With Shaded Similarity Matrices, Bei Yu, Jun Wang, Les Gasser
Concept Tree Based Clustering Visualization With Shaded Similarity Matrices, Bei Yu, Jun Wang, Les Gasser
School of Information Studies - Faculty Scholarship
One of the problems with existing clustering methods is that the interpretation of clusters may be difficult. Two different approaches have been used to solve this problem: conceptual clustering in machine learning and clustering visualization in statistics and graphics. The purpose of this paper is to investigate the benefits of combining clustering visualization and conceptual clustering to obtain better cluster interpretations. In our research we have combined concept trees for conceptual clustering with shaded similarity matrices for visualization. Experimentation shows that the two interpretation approaches can complement each other to help us understand data better.
Commercial Websites And The Use Of Classification Schemes: The Case Of Amazon.Com. In Lopez-Huertas, Maria J. Challenges In Knowledge Represantation An Organization For The 21st Century: Intergration Of Knowledge Across Boundaries., Barbara H. Kwasnik
School of Information Studies - Faculty Scholarship
The structure and use of the classification for books on the Amazon.com website are described and analyzed. The contents of this large website are changing constantly and the access mechanisms have the main purpose of enabling searchers to find books for purchase. This includes finding books the searcher knows about at the start of the research, as well as those that might present themselves in the course of searching and that are related in some way. Underlying the many access paths to books is a classification scheme comprising a rich network of terms in an enumerative and multihierarchical structure.
A Breadth Of Nlp Applications, Elizabeth D. Liddy
A Breadth Of Nlp Applications, Elizabeth D. Liddy
School of Information Studies - Faculty Scholarship
The Center for Natural Language Processing (CNLP) was founded in September 1999 in the School of Information Studies, the “Original Information School”, at Syracuse University. CNLP’s mission is to advance the development of human-like, language understanding software capabilities for government, commercial, and consumer applications. The Center conducts both basic and applied research, building on its recognized capabilities in Natural Language Processing. The Center’s seventeen employees are a mix of doctoral students in information science or computer engineering, software engineers, linguistic analysts, and research engineers.
Ontological Representation Of Learning Objects, Jian Qin, Christina Finneran
Ontological Representation Of Learning Objects, Jian Qin, Christina Finneran
School of Information Studies - Faculty Scholarship
Many of the existing metadata standards use content metadata elements that are coarse-grained representations of learning resources. These metadata standards limit users ’ access to learning objects that may be at the component level. The authors discuss the need for component level access to learning resources and provide a conceptual framework of the knowledge representation of learning objects that would enable such access.
Question Answering: Cnlp At The Trec-2002 Question Answering Track, Anne R. Diekema, Jiangping Chen, Nancy Mccracken, Necati Ercan Ozgencil, Mary D. Taffet
Question Answering: Cnlp At The Trec-2002 Question Answering Track, Anne R. Diekema, Jiangping Chen, Nancy Mccracken, Necati Ercan Ozgencil, Mary D. Taffet
School of Information Studies - Faculty Scholarship
This paper describes the retrieval experiments for the main task and list task of the TREC-2002 question-answering track. The question answering system described automatically finds answers to questions in a large document collection. The system uses a two-stage retrieval approach to answer finding based on matching of named entities, linguistic patterns, keywords, and the use of a new inference module. In answering a question, the system carries out a detailed query analysis that produces a logical query representation, an indication of the question focus, and answer clue words.
Behavioral Information Security Item Ratings, Jeffrey M. Stanton
Behavioral Information Security Item Ratings, Jeffrey M. Stanton
School of Information Studies - Faculty Scholarship
Item rating data from behavioral information security project.