Interactive cross-language document selection

The problem of finding documents written in a language that the searcher cannot read is perhaps the most challenging application of cross-language information retrieval technology. In interactive applications, that task involves at least two steps: (1) the machine locates promising documents in a collection that is larger than the searcher could scan, and (2) the searcher recognizes documents relevant to their intended use from among those nominated by the machine. This article presents the results of experiments designed to explore three techniques for supporting interactive relevance assessment: (1) full machine translation, (2) rapid term-by-term translation, and (3) focused phrase translation. Machine translation was found to better support this task than term-by-term translation, and focused phrase translation further improved recall without an adverse effect on precision. The article concludes with an assessment of the strengths and weaknesses of the evaluation framework used in this study and some remarks on implications of these results for future evaluation campaigns

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

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