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Automatic metadata generation using associative networks
Marko A. Rodriguez, Johan Bollen, Herbert Van De Sompel
Article No.: 7
In spite of its tremendous value, metadata is generally sparse and incomplete, thereby hampering the effectiveness of digital information services. Many of the existing mechanisms for the automated creation of metadata rely primarily on content...
An analysis of latent semantic term self-correlation
Laurence A. F. Park, Kotagiri Ramamohanarao
Article No.: 8
Latent semantic analysis (LSA) is a generalized vector space method that uses dimension reduction to generate term correlations for use during the information retrieval process. We hypothesized that even though the dimension reduction establishes...
An adaptive threshold framework for event detection using HMM-based life profiles
Chien Chin Chen, Meng Chang Chen, Ming-Syan Chen
Article No.: 9
When an event occurs, it attracts attention of information sources to publish related documents along its lifespan. The task of event detection is to automatically identify events and their related documents from a document stream, which is a set...
Information filtering and query indexing for an information retrieval model
Christos Tryfonopoulos, Manolis Koubarakis, Yannis Drougas
Article No.: 10
In the information filtering paradigm, clients subscribe to a server with continuous queries or profiles that express their information needs. Clients can also publish documents to servers. Whenever a document is published, the continuous queries...
User language model for collaborative personalized search
Gui-Rong Xue, Jie Han, Yong Yu, Qiang Yang
Article No.: 11
Traditional personalized search approaches rely solely on individual profiles to construct a user model. They are often confronted by two major problems: data sparseness and cold-start for new individuals. Data sparseness refers to the fact that...
Textual analysis of stock market prediction using breaking financial news: The AZFin text system
Robert P. Schumaker, Hsinchun Chen
Article No.: 12
Our research examines a predictive machine learning approach for financial news articles analysis using several different textual representations: bag of words, noun phrases, and named entities. Through this approach, we investigated 9,211...