90 research outputs found
Personalized classification for keyword-based category profiles
Personalized classification refers to allowing users to define their own categories and automating the assignment of documents to these categories. In this paper, we examine the use of keywords to define personalized categories and propose the use of Support Vector Machine (SVM) to perform personalized classification. Two scenarios have been investigated. The first assumes that the personalized categories are defined in a flat category space. The second assumes that each personalized category is defined within a pre-defined general category that provides a more specific context for the personalized category. The training documents for personalized categories are obtained from a training document pool using a search engine and a set of keywords. Our experiments have delivered better classification results using the second scenario. We also conclude that the number of keywords used can be very small and increasing them does not always lead to better classification performance
Wie lernt der Computer den Menschen kennen? Bestandsaufnahme und Experimente zur Benutzermodellierung in der Wirtschaftsinformatik
Sub-Word Indexing and Blind Relevance Feedback for English, Bengali, Hindi, and Marathi IR
Molecular Dynamics Simulation of Phosphorylated KID Post-Translational Modification
BACKGROUND:Kinase-inducible domain (KID) as transcriptional activator can stimulate target gene expression in signal transduction by associating with KID interacting domain (KIX). NMR spectra suggest that apo-KID is an unstructured protein. After post-translational modification by phosphorylation, KID undergoes a transition from disordered to well folded protein upon binding to KIX. However, the mechanism of folding coupled to binding is poorly understood. METHODOLOGY:To get an insight into the mechanism, we have performed ten trajectories of explicit-solvent molecular dynamics (MD) for both bound and apo phosphorylated KID (pKID). Ten MD simulations are sufficient to capture the average properties in the protein folding and unfolding. CONCLUSIONS:Room-temperature MD simulations suggest that pKID becomes more rigid and stable upon the KIX-binding. Kinetic analysis of high-temperature MD simulations shows that bound pKID and apo-pKID unfold via a three-state and a two-state process, respectively. Both kinetics and free energy landscape analyses indicate that bound pKID folds in the order of KIX access, initiation of pKID tertiary folding, folding of helix alpha(B), folding of helix alpha(A), completion of pKID tertiary folding, and finalization of pKID-KIX binding. Our data show that the folding pathways of apo-pKID are different from the bound state: the foldings of helices alpha(A) and alpha(B) are swapped. Here we also show that Asn139, Asp140 and Leu141 with large Phi-values are key residues in the folding of bound pKID. Our results are in good agreement with NMR experimental observations and provide significant insight into the general mechanisms of binding induced protein folding and other conformational adjustment in post-translational modification
Cross-lingual C*ST*RD: English access to Hindi information
We present C*ST*RD, a cross-language information delivery system that supports cross-language information retrieval, information space visualization and navigation, machine translation, and text summarization of single documents and clusters of documents. C*ST*RD was assembled and trained within 1 month, in the context of DARPA’s Surprise Language Exercise, that selected as source a heretofore unstudied language, Hindi. Given the brief time, we could not create deep Hindi capabilities for all the modules, but instead experimented with combining shallow Hindi capabilities, or even English-only modules, into one integrated system. Various possible configurations, with different tradeoffs in processing speed and ease of use, enable the rapid deployment of C*ST*RD to new languages under various conditions
Incorporating contextual information in recommender systems using a multidimensional approach
User Modeling for Information Access Based on Implicit Feedback
User modeling can be used in information filtering and retrieval systems
to improve the representation of a users information needs. User models
can be constructed by hand, or learned automatically based on feedback
provided by the user about the relevance of documents that they have
examined. By observing user behavior, it is possible to infer implicit
feedback without requiring explicit relevance judgments. Previous studies
based on Internet discussion groups (USENET news) have shown reading time
to be a useful source of implicit feedback for predicting a users
preferences. The study reported in this paper extends that work by
providing framework for considering alternative sources of implicit
feedback, examining whether reading time is useful for predicting a users
preferences for academic and professional journal articles, and exploring
whether retention behavior can usefully augment the information that
reading time provides. Two user studies were conducted in which
undergraduate students examined articles and abstracts related to the
telecommunications and pharmaceutical industries. The results showed that
reading time could be used to predict the users assessment of relevance,
although reading time for journal articles and technical abstracts are
longer than has been reported for USENET news documents. Observation of
printing events, a type of retention behavior, was found to provide
additional useful evidence about relevance beyond that which could be
inferred from reading time. The paper concludes with a brief discussion
of the implications of the reported results.
(Also cross-referenced as UMIACS-TR-2000-29)
(Also cross-referenced as HCIL-TR-2000-11
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