Bhaskar Mitra

Cross Domain Regularization for Neural Ranking Models using Adversarial Learning

2018

Unlike traditional learning to rank models that depend on hand-crafted features, neural representation learning models learn higher level features for the ranking task by training on large datasets. Their ability to learn new features directly …

Overview of the TREC 2019 deep learning track

2020 · arXiv (Cornell University)

The Deep Learning Track is a new track for TREC 2019, with the goal of studying ad hoc ranking in a large data regime. It is the first track with large human-labeled training sets, introducing …

Overview of the TREC 2021 deep learning track

2025 · arXiv (Cornell University)

This is the fifth year of the TREC Deep Learning track. As in previous years, we leverage the MS MARCO datasets that made hundreds of thousands of human-annotated training labels available for both passage and …

An Introduction to Neural Information Retrieval

2018 · Foundations and Trends® in Information Retrieval

Neural ranking models for information retrieval (IR) use shallow or deep neural networks to rank search results in response to a query. Traditional learning to rank models employ supervised machine learning (ML) techniques—including neural networks—over …

Learning to Match Using Local and Distributed Representations of Text\n for Web Search

2016 · arXiv (Cornell University)

Models such as latent semantic analysis and those based on neural embeddings\nlearn distributed representations of text, and match the query against the\ndocument in the latent semantic space. In traditional information retrieval\nmodels, on the other hand, …

Improving Document Ranking with Dual Word Embeddings

2016

This paper investigates the popular neural word embedding method Word2vec as a source of evidence in document ranking. In contrast to NLP applications of word2vec, which tend to use only the input embeddings, we retain …

Query Expansion with Locally-Trained Word Embeddings

2016

Continuous space word embeddings have received a great deal of attention in the natural language processing and machine learning communities for their ability to model term similarity and other relationships. We study the use of …

Learning to Match using Local and Distributed Representations of Text for Web Search

2017

Models such as latent semantic analysis and those based on neural embeddings learn distributed representations of text, and match the query against the document in the latent semantic space. In traditional information retrieval models, on …

Neural Ranking Models with Multiple Document Fields

2018

Deep neural networks have recently shown promise in the ad-hoc retrieval task. However, such models have often been based on one field of the document, for example considering document title only or document body only. …

Analysis of Points of Interests Recommended for Leisure Walk Descriptions

2024 · arXiv (Cornell University)

Data for Sub-Task 1 of the Advertisement in Retrieval-Augmented Generation task at Touché 2025. The dataset contains segments retrieved from the segmented version of MS MARCO V2.1. The queries used in retrieval are taken from …

Evaluating Stochastic Rankings with Expected Exposure

2020

We introduce the concept of expected exposure as the average attention ranked items receive from users over repeated samples of the same query. Furthermore, we advocate for the adoption of the principle of equal expected …

Large Language Models can Accurately Predict Searcher Preferences

2024

Much of the evaluation and tuning of a search system relies on relevance labels---annotations that say whether a document is useful for a given search and searcher. Ideally these come from real searchers, but it …