Donald Metzler

A Markov random field model for term dependencies

This paper develops a general, formal framework for modeling term dependencies via Markov random fields. The model allows for arbitrary text features to be incorporated as evidence. In particular, we… (More)

Indri : A language-model based search engine for complex queries ( extended version )

Trevor Strohman, Donald Metzler, Howard R. Turtle, W. Bruce Croft
2005

Search engines are a critical tool for intelligence analysis. A number of innovations for search have been introduced since research with an emphasis on analyst needs began in the TIPSTER project.… (More)

Linear feature-based models for information retrieval

Donald Metzler, W. Bruce Croft
Information Retrieval
2006

There have been a number of linear, feature-based models proposed by the information retrieval community recently. Although each model is presented differently, they all share a common underlying… (More)

Combining the language model and inference network approaches to retrieval

Donald Metzler, W. Bruce Croft
Inf. Process. Manage.
2004

The inference network retrieval model, as implemented in the InQuery search engine, allows for richly structured queries. However, it incorporates a form of ad hoc tf.idf estimates for word… (More)

Latent concept expansion using markov random fields

Query expansion, in the form of pseudo-relevance feedback or relevance feedback, is a common technique used to improve retrieval effectiveness. Most previous approaches have ignored important issues,… (More)

Improving the estimation of relevance models using large external corpora

Information retrieval algorithms leverage various collection statistics to improve performance. Because these statistics are often computed on a relatively small evaluation corpus, we believe using… (More)

Automatic feature selection in the markov random field model for information retrieval

Donald Metzler
CIKM
2007

Previous applications of the Markov random field model for information retrieval have used manually chosen features. However, it is often difficult or impossible to know, a priori, the best set of… (More)

Learning concept importance using a weighted dependence model

Michael Bendersky, Donald Metzler, W. Bruce Croft
WSDM
2010

Modeling query concepts through term dependencies has been shown to have a significant positive effect on retrieval performance, especially for tasks such as web search, where relevance at high ranks… (More)

Parameterized concept weighting in verbose queries

Michael Bendersky, Donald Metzler, W. Bruce Croft
SIGIR
2011

The majority of the current information retrieval models weight the query concepts (e.g., terms or phrases) in an unsupervised manner, based solely on the collection statistics. In this paper, we go… (More)

Donald Metzler, Yaniv Bernstein, W. Bruce Croft, Alistair Moffat, Justin Zobel
CIKM
2005

Text similarity spans a spectrum, with broad topical similarity near one extreme and document identity at the other. Intermediate levels of similarity -- resulting from summarization, paraphrasing,… (More)

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