The Importance of Prior Probabilities for Entry Page Search

By Wessel Kraaij, Thijs Westerveld, Djoerd Hiemstra. Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, 2002

309 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Information Systems

View Paper: https://www.semanticscholar.org/paper/8ad051197dcdeba248a4b8ffc00a678638216f19

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. W. Bruce Croft
2. Chengxiang Zhai
3. Donald Metzler
4. Jimmy J. Lin
5. Hamed Zamani
6. Jamie Callan
7. Jiwoon Jeon
8. Hang Li

In Information Systems

1. W. Bruce Croft
2. Chengxiang Zhai
3. Donald Metzler
4. Hamed Zamani
5. Jamie Callan
6. Jiwoon Jeon
7. Jimmy J. Lin
8. John D. Lafferty

Citations in the corpus

• A framework to predict the quality of answers with non-textual features (2006, methodology): “Query independent prior information such as time, quality and popularity have been successfully integrated into the model using the prior probability [11, 23, 14].”
• Statistical Language Models for Information Retrieval: A Critical Review (2008, methodology): “…optimization techniques, and scalability • Question answering • Summarization of single documents, multiple documents, and corpora • Text mining • Topic detection and tracking • Usability, interactivity, and visualization issues in IR • User modelling and user studies for IR • Web search”
• A Study of Smoothing Methods for Language Models Applied to Ad Hoc Information Retrieval (2001, background): “, the length of a document or links in a web page, as well as other format/style features of a document; see [Miller et al. 1999; Kraaij et al. 2002] for empirical studies that exploit simple alternative priors.”
• Discriminative models for information retrieval (2004, methodology): “Some work has also been done to include query-independent features into the prior probability (see equation 7) of the document [14].”
• Blind late fusion in multimedia event retrieval (2016, background): “[8], the joint probability JP can be derived from P(e|v,m) under the assumption that the representations v and m are statistically independent and conditional independent given the event e.”
• Web site design with the patron in mind: A step-by-step guide for libraries (2006, background): “As reported in [14], the Web pages having URLs of the first three types constitute less than 8% of the WT10g dataset but contribute more than 94% of the homepages.”
• Time-based language models (2003, background): “Other related work includes efforts to improve the extraction of time tags for question answering [10], incorporating pre-learned fixed prior probabilities for different categories of web pages into language models for entry page search [11], and using the Web for dynamic language model adaptation [”
• A study of smoothing methods for language models applied to information retrieval (2004, background): “, the length of a document or links in a web page, as well as other format/style features of a document; see [Miller et al. 1999; Kraaij et al. 2002] for empirical studies that exploit simple alternative priors.”
• Linear feature-based models for information retrieval (2007, background): “Examples of these features are PageRank (Brin and Page, 1998), URL depth (Kraaij et al., 2002), document quality (Zhou and Croft, 2005), readability (Si and Callan, 2001), sentiment (Pang et al.”