Jamie Callan

Learning To Rank Resources

2017

We present a learning-to-rank approach for resource selection. We develop features for resource ranking and present a training approach that does not require human judgments. Our method is well-suited to environments with a large number …

Learning to Reweight Terms with Distributed Representations

2015

Term weighting is a fundamental problem in IR research and numerous weighting models have been proposed. Proper term weighting can greatly improve retrieval accuracies, which essentially involves two types of query understanding: interpreting the query …

Bag-of-Entities Representation for Ranking

2016

This paper presents a new bag-of-entities representation for document ranking, with the help of modern knowledge bases and automatic entity linking. Our system represents query and documents by bag-of-entities vectors constructed from their entity annotations, …

Query-Biased Partitioning for Selective Search

2016

Selective search is a cluster-based distributed retrieval architecture that reduces computational costs by partitioning a corpus into topical shards, and selectively searching them. Prior research formed topical shards by clustering the corpus based on the …

Explicit Semantic Ranking for Academic Search via Knowledge Graph Embedding

2017

This paper introduces Explicit Semantic Ranking (ESR), a new ranking technique that leverages knowledge graph embedding. Analysis of the query log from our academic search engine, SemanticScholar.org, reveals that a major error source is its …

End-to-End Neural Ad-hoc Ranking with Kernel Pooling

2017

This paper proposes K-NRM, a kernel based neural model for document ranking. Given a query and a set of documents, K-NRM uses a translation matrix that models word-level similarities via word embeddings, a new kernel-pooling …

Convolutional Neural Networks for Soft-Matching N-Grams in Ad-hoc Search

2018

This paper presents \textttConv-KNRM, a Convolutional Kernel-based Neural Ranking Model that models n-gram soft matches for ad-hoc search. Instead of exact matching query and document n-grams, \textttConv-KNRM uses Convolutional Neural Networks to represent n-grams of …

Deeper Text Understanding for IR with Contextual Neural Language Modeling

2019

Neural networks provide new possibilities to automatically learn complex language patterns and query-document relations. Neural IR models have achieved promising results in learning query-document relevance patterns, but few explorations have been done on understanding the …

COIL: Revisit Exact Lexical Match in Information Retrieval with Contextualized Inverted List

2021

Classical information retrieval systems such as BM25 rely on exact lexical match and carry out search efficiently with inverted list index. Recent neural IR models shifts towards soft semantic matching all query document terms, but …

Active Retrieval Augmented Generation

2023

Zhengbao Jiang, Frank Xu, Luyu Gao, Zhiqing Sun, Qian Liu, Jane Dwivedi-Yu, Yiming Yang, Jamie Callan, Graham Neubig. Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing. 2023.