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Proceedings - Relevance Feedback 2010

Mining Specific and General Features in Both Positive and Negative Relevance Feedback: QUT E-Discovery Lab at the TREC 2010 Relevance Feedback Track

Abdulmohsen Algarni, Yuefeng Li, Xiaohui Tao

Abstract

User relevance feedback is usually utilized by Web systems to interpret user information needs and retrieve effective results for users. However, how to discover useful knowledge in user relevance feedback and how to wisely use the discovered knowledge are two critical problems. However, understanding what makes an individual document good or bad for feedback can lead to the solution of the previous problem. In TREC 2010, we participated in the Relevance Feedback Track and experimented two models for extracting pseudo-relevance feedback to improve the ranking of retrieved documents. The first one, the main run, was a pattern-based model, whereas the second one, the optional run, was a term-based model. The two models consisted of two stages: one using relevance feedback provided by TREC'10 to expand queries to extract pseudo-relevance feedback; one using pseudo-relevance feedback to find useful patterns and terms according to their relevance and irrelevance judgements to rank documents. In this paper, the detailed description of those models is presented.

Bibtex 
@inproceedings{DBLP:conf/trec/AlgarniLT10,
    author = {Abdulmohsen Algarni and Yuefeng Li and Xiaohui Tao},
    editor = {Ellen M. Voorhees and Lori P. Buckland},
    title = {Mining Specific and General Features in Both Positive and Negative Relevance Feedback: {QUT} E-Discovery Lab at the {TREC} 2010 Relevance Feedback Track},
    booktitle = {Proceedings of The Nineteenth Text REtrieval Conference, {TREC} 2010, Gaithersburg, Maryland, USA, November 16-19, 2010},
    series = {{NIST} Special Publication},
    volume = {500-294},
    publisher = {National Institute of Standards and Technology {(NIST)}},
    year = {2010},
    url = {http://trec.nist.gov/pubs/trec19/papers/queensland.univ.tech.RF.rev.pdf},
    timestamp = {Thu, 12 Mar 2020 00:00:00 +0100},
    biburl = {https://dblp.org/rec/conf/trec/AlgarniLT10.bib},
    bibsource = {dblp computer science bibliography, https://dblp.org}
}

The University of Amsterdam at TREC 2010: Session, Entity and Relevance Feedback

Marc Bron, Jiyin He, Katja Hofmann, Edgar Meij, Maarten de Rijke, Manos Tsagkias, Wouter Weerkamp

Abstract

We describe the participation of the University of Amsterdam's ILPS group in the session, entity, and relevance feedback track at TREC 2010. In the Session Track we explore the use of blind relevance feedback to bias a follow-up query towards or against the topics covered in documents returned to the user in response to the original query. In the Entity Track REF task we experiment with a window size parameter to limit the amount of context considered by the entity co-occurrence models and explore the use of Freebase for type filtering, entity normalization and homepage finding. In the ELC task we use an approach that uses the number of links shared between candidate and example entities to rank candidates. In the Relevance Feedback Track we experiment with a novel model that uses Wikipedia to expand the query language model.

Bibtex 
@inproceedings{DBLP:conf/trec/BronHHMRTW10,
    author = {Marc Bron and Jiyin He and Katja Hofmann and Edgar Meij and Maarten de Rijke and Manos Tsagkias and Wouter Weerkamp},
    editor = {Ellen M. Voorhees and Lori P. Buckland},
    title = {The University of Amsterdam at {TREC} 2010: Session, Entity and Relevance Feedback},
    booktitle = {Proceedings of The Nineteenth Text REtrieval Conference, {TREC} 2010, Gaithersburg, Maryland, USA, November 16-19, 2010},
    series = {{NIST} Special Publication},
    volume = {500-294},
    publisher = {National Institute of Standards and Technology {(NIST)}},
    year = {2010},
    url = {http://trec.nist.gov/pubs/trec19/papers/univ.amsterdam.session.ent.RF.rev.pdf},
    timestamp = {Thu, 12 Mar 2020 00:00:00 +0100},
    biburl = {https://dblp.org/rec/conf/trec/BronHHMRTW10.bib},
    bibsource = {dblp computer science bibliography, https://dblp.org}
}

Evaluation of a Methodology for Modeling Term Relationship through Geometry: Experiments at TREC 2010 Relevance Feedback Track

Emanuele Di Buccio, Massimo Melucci, Jian-Yun Nie

Abstract

The work reported in this paper is focused on the experimental evaluation of a methodology which models sources for feedback through a vector subspace formalism. This work considers a specific application of the methodology that exploits correlation among terms in documents judged as relevant to support feedback. Experiments were carried out during the participation to the TREC 2010 Relevance Feedback Track, thus investigating the effectiveness of the methodology application for modeling term correlation on a very large text corpus and when little evidence, namely one relevant document, is used as input for feedback.

Bibtex 
@inproceedings{DBLP:conf/trec/BuccioMN10,
    author = {Emanuele Di Buccio and Massimo Melucci and Jian{-}Yun Nie},
    editor = {Ellen M. Voorhees and Lori P. Buckland},
    title = {Evaluation of a Methodology for Modeling Term Relationship through Geometry: Experiments at {TREC} 2010 Relevance Feedback Track},
    booktitle = {Proceedings of The Nineteenth Text REtrieval Conference, {TREC} 2010, Gaithersburg, Maryland, USA, November 16-19, 2010},
    series = {{NIST} Special Publication},
    volume = {500-294},
    publisher = {National Institute of Standards and Technology {(NIST)}},
    year = {2010},
    url = {http://trec.nist.gov/pubs/trec19/papers/univ.padua.RF.rev2.pdf},
    timestamp = {Thu, 12 Mar 2020 00:00:00 +0100},
    biburl = {https://dblp.org/rec/conf/trec/BuccioMN10.bib},
    bibsource = {dblp computer science bibliography, https://dblp.org}
}