W. Bruce Croft

Publications748
h-index119
Citations56,584
Highly Influential Citations4,555
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A Markov random field model for term dependencies
TLDR
A novel approach is developed to train the model that directly maximizes the mean average precision rather than maximizing the likelihood of the training data, and significant improvements are possible by modeling dependencies, especially on the larger web collections.
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A Deep Relevance Matching Model for Ad-hoc Retrieval
TLDR
A novel deep relevance matching model (DRMM) for ad-hoc retrieval that employs a joint deep architecture at the query term level for relevance matching and can significantly outperform some well-known retrieval models as well as state-of-the-art deep matching models.
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LDA-based document models for ad-hoc retrieval
TLDR
This paper proposes an LDA-based document model within the language modeling framework, and evaluates it on several TREC collections, and shows that improvements over retrieval using cluster-based models can be obtained with reasonable efficiency.
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Relevance-Based Language Models
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A language modeling approach to information retrieval
TLDR
This work proposes an approach to retrieval based on probabilistic language modeling and integrates document indexing and document retrieval into a single model, which significantly outperforms standard tf.idf weighting on two different collections and query sets.
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Radical Construction Grammar: Syntactic Theory in Typological Perspective
TLDR
This chapter discusses Syntactic Theory and the Theory of Language, which aims to clarify and explain the role of language in the construction of grammar.
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Relevance-Based Language Models
TLDR
This work proposes a novel technique for estimating a relevance model with no training data and demonstrates that it can produce highly accurate relevance models, addressing important notions of synonymy and polysemy.
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Deriving concept hierarchies from text
TLDR
This paper presents a means of automatically deriving a hierarchical organization of concepts from a set of documents without use of training data or standard clustering techniques, using a type of co-occurrence known as subsumption.
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Predicting query performance
TLDR
It is suggested that clarity scores measure the ambiguity of a query with respect to a collection of documents and show that they correlate positively with average precision in a variety of TREC test sets.
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Searching distributed collections with inference networks
TLDR
Methods of addressing each issue in the inference network model are described, dkcusses their implementation in the INQUERY system, and experimental results demonstrating their effectiveness are presented.
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