285 research outputs found
Using the Global Web as an Expertise Evidence Source
This paper describes the details of our participation in expert search task of the TREC 2007 Enterprise track. The presented study demonstrates the predicting potential of the expertise evidence that can be found outside of
the organization. We discovered that combining the ranking built solely on the Enterprise data with the Global Web
based ranking may produce significant increases in performance. However, our main goal was to explore whether
this result can be further improved by using various quality measures to distinguish among web result items. While,
indeed, it was beneficial to use some of these measures, especially those measuring relevance of URL strings and titles,
it stayed unclear whether they are decisively important
University of Twente at the TREC 2008 Enterprise Track: using the Global Web as an expertise evidence source
This paper describes the details of our participation in expert search task of the TREC 2007 Enterprise track.\ud
This is the fourth (and the last) year of TREC 2007 Enterprise Track and the second year the University of Twente (Database group) submitted runs for the expert nding task. In the methods that were used to produce these runs, we mostly rely on the predicting potential of those expertise evidence sources that are publicly available on the Global Web, but not hosted at the website of the organization under study (CSIRO). This paper describes the follow-up studies\ud
complimentary to our recent research [8] that demonstrated how taking the web factor seriously signicantly improves the performance of expert nding in the enterprise
Sheffield University CLEF 2000 submission - bilingual track: German to English
We investigated dictionary based cross language information
retrieval using lexical triangulation. Lexical triangulation combines the results
of different transitive translations. Transitive translation uses a pivot language
to translate between two languages when no direct translation resource is
available. We took German queries and translated then via Spanish, or Dutch
into English. We compared the results of retrieval experiments using these
queries, with other versions created by combining the transitive translations or
created by direct translation. Direct dictionary translation of a query introduces
considerable ambiguity that damages retrieval, an average precision 79% below
monolingual in this research. Transitive translation introduces more ambiguity,
giving results worse than 88% below direct translation. We have shown that
lexical triangulation between two transitive translations can eliminate much of
the additional ambiguity introduced by transitive translation
How effective is stemming and decompounding for German text retrieval?
Erworben im Rahmen der Schweizer Nationallizenzen (http://www.nationallizenzen.ch
Being Omnipresent To Be Almighty: The Importance of The Global Web Evidence for Organizational Expert Finding
Modern expert nding algorithms are developed under the
assumption that all possible expertise evidence for a person
is concentrated in a company that currently employs the
person. The evidence that can be acquired outside of an
enterprise is traditionally unnoticed. At the same time, the
Web is full of personal information which is sufficiently detailed to judge about a person's skills and knowledge. In this work, we review various sources of expertise evidence out-side of an organization and experiment with rankings built on the data acquired from six dierent sources, accessible through APIs of two major web search engines. We show that these rankings and their combinations are often more realistic and of higher quality than rankings built on organizational data only
Towards a belief revision based adaptive and context sensitive information retrieval system
In an adaptive information retrieval (IR) setting, the information seekers' beliefs about which terms are relevant or nonrelevant will naturally fluctuate. This article investigates how the theory of belief revision can be used to model adaptive IR. More specifically, belief revision logic provides a rich representation scheme to formalize retrieval contexts so as to disambiguate vague user queries. In addition, belief revision theory underpins the development of an effective mechanism to revise user profiles in accordance with information seekers' changing information needs. It is argued that information retrieval contexts can be extracted by means of the information-flow text mining method so as to realize a highly autonomous adaptive IR system. The extra bonus of a belief-based IR model is that its retrieval behavior is more predictable and explanatory. Our initial experiments show that the belief-based adaptive IR system is as effective as a classical adaptive IR system. To our best knowledge, this is the first successful implementation and evaluation of a logic-based adaptive IR model which can efficiently process large IR collections
Personalization of Search Engine Services for Effective Retrieval and Knowledge Management
The Internet and corporate intranets provide far more information than anybody can absorb. People use search engines to find the information they require. However, these systems tend to use only one fixed term weighting strategy regardless of the context to which it applies, posing serious performance problems when characteristics of different users, queries, and text collections are taken into consideration. In this paper, we argue that the term weighting strategy should be context specific, that is, different term weighting strategies should be applied to different contexts, and we propose a new systematic approach that can automatically generate term weighting strategies for different contexts based on genetic programming (GP). The new proposed framework was tested on TREC data and the results are very promising
Verbosity and Interface Design
Users pose very short queries to information retrieval systems. This study shows that the apparent length of the query field has an effect on the length of the query users enter
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