UvA-DARE ( Digital Academic Repository ) Boosting Web Retrieval through

We explore the use of phrase and proximity terms in the context of web retrieval, which is different from traditional ad-hoc retrieval both in document structure and in query characteristics. We show that for this type of task, the usage of both phrase and proximity terms is highly beneficial for early precision as well as for overall retrieval effectiveness. We also analyze why phrase and proximity terms are far more effective for web retrieval than for ad-hoc retrieval.

[1]  Michael McGill,et al.  Introduction to Modern Information Retrieval , 1983 .

[2]  Editors , 1986, Brain Research Bulletin.

[3]  Joel L Fagan,et al.  Experiments in Automatic Phrase Indexing For Document Retrieval: A Comparison of Syntactic and Non-Syntactic Methods , 1987 .

[4]  W. Bruce Croft,et al.  The use of phrases and structured queries in information retrieval , 1991, SIGIR '91.

[5]  E. Michael Keen,et al.  Term position ranking: some new test results , 1992, SIGIR '92.

[6]  Garrison W. Cottrell,et al.  Automatic combination of multiple ranked retrieval systems , 1994, SIGIR '94.

[7]  David Hawking,et al.  Proximity Operators - So Near And Yet So Far , 1995, TREC.

[8]  David Hawking,et al.  Relevance weighting using distance between term occurrences , 1996 .

[9]  Hinrich Schütze,et al.  Xerox TREC-5 Site Report: Routing, Filtering, NLP, and Spanish Tracks , 1996, TREC.

[10]  Claire Cardie,et al.  An Analysis of Statistical and Syntactic Phrases , 1997, RIAO.

[11]  Wessel Kraaij,et al.  Comparing the Effect of Syntactic vs. Statistical Phrase Indexing Strategies for Dutch , 1998, ECDL.

[12]  W. Bruce Croft,et al.  An exploratory analysis of phrases in text retrieval , 2000, RIAO.

[13]  Amanda Spink,et al.  Real life, real users, and real needs: a study and analysis of user queries on the web , 2000, Inf. Process. Manag..

[14]  Charles L. A. Clarke,et al.  Shortest-substring retrieval and ranking , 2000, TOIS.

[15]  Avi Arampatzis,et al.  An Evaluation of Linguistically-motivated Indexing Schemes , 2000 .

[16]  Fidel Cacheda,et al.  Understanding how people use search engines: a statistical analysis for e-Business , 2000 .

[17]  Amanda Spink,et al.  Searching the Web: the public and their queries , 2001 .

[18]  David Hawking,et al.  Overview of the TREC-2002 Web Track , 2002, TREC.

[19]  Amanda Spink,et al.  From E-Sex to E-Commerce: Web Search Changes , 2002, Computer.

[20]  Susan T. Dumais,et al.  An Analysis of the AskMSR Question-Answering System , 2002, EMNLP.

[21]  David Carmel,et al.  Juru at TREC 2003 - Topic Distillation using Query-Sensitive Tuning and Cohesiveness Filtering , 2003, TREC.

[22]  Jacques Savoy,et al.  Term Proximity Scoring for Keyword-Based Retrieval Systems , 2003, ECIR.

[23]  David Hawking,et al.  Overview of the TREC 2003 Web Track , 2003, TREC.

[24]  Jacques Savoy,et al.  Report on the TREC 2003 Experiment: Genomic and Web Searches , 2003, TREC.

[25]  James P. Callan,et al.  Combining document representations for known-item search , 2003, SIGIR.

[26]  William R. Hersh,et al.  TREC GENOMICS Track Overview , 2003, TREC.

[27]  Shuming Shi,et al.  Microsoft Research Asia at the Web Track of TREC 2009 , 2009, TREC.

[28]  David Hawking,et al.  Overview of the TREC 2004 Web Track , 2004, TREC.

[29]  Katja Hofmann,et al.  The University of Amsterdam at TREC 2010: Session, Entity and Relevance Feedback , 2010, TREC.

[30]  J. Golbeck In real life , 2016, Science.