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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. Expand
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. Expand
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. Expand
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. Expand
Relevance-Based Language Models
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. Expand
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. Expand
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. Expand
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. Expand
Query expansion using local and global document analysis
TLDR
It is shown that using global analysis techniques, such as word contezt and phrase structure, on the local aet of documents produces results that are both more effective and more predictable than simple local feedback. Expand
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