Sitemap
A list of all the posts and pages found on the site. For you robots out there is an XML version available for digesting as well.
Pages
About me
About me
Posts
Future Blog Post
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Blog Post number 4
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This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool.
Blog Post number 3
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Blog Post number 2
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Blog Post number 1
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portfolio
Portfolio item number 1
Short description of portfolio item number 1
Portfolio item number 2
Short description of portfolio item number 2
publications
Modeling Event Importance for Ranking Daily News Even
Published in WSDM 17, 2017
We deal with the problem of ranking news events on a daily basis for large news corpora, an essential building block for news aggregation. News ranking has been addressed in the literature before but with individual news articles as the unit of ranking. However, estimating event importance accurately requires models to quantify current day event importance as well as its significance in the historical context. Consequently, in this paper we show that a cluster of news articles representing an event is a better unit of ranking as it provides an improved estimation of popularity, source diversity and authority cues. In addition, events facilitate quantifying their historical significance by linking them with long-running topics and recent chain of events. Our main contribution in this paper is to provide effective models for improved news event ranking.
Supervised contrastive learning approach for contextual ranking
Published in ICTIR 22, 2022
This paper proposes a simple yet effective method to improve ranking performance on smaller datasets using supervised contrastive learning for the document ranking problem. We perform data augmentation by creating training data using parts of the relevant documents in the query-document pairs. We then use a supervised contrastive learning objective to learn an effective ranking model from the augmented dataset. Our experiments on subsets of the TREC-DL dataset show that, although data augmentation leads to an increasing the training data sizes, it does not necessarily improve the performance using existing pointwise or pairwise training
Context Aware Query Rewriting for Text Rankers using LLM
Published in arxiv, 2023
In this work, we analyze the utility of LLMs for improved query rewriting for text ranking tasks. We adopt a simple, yet surprisingly effective, approach called context aware query rewriting (CAR) to leverage the benefits of LLMs for query understanding.
Data Augmentation for Sample Efficient and Robust Document Ranking
Published in ACM Transactions on Information Systems, 2024
The paper proposes data augmentation methods to boost the performance of contextual ranking models, which often demand substantial data for fine-tuning. Through supervised and unsupervised augmentation, relevant document segments in query-document pairs are utilized to enhance sample efficiency, particularly with limited training data. By adapting contrastive losses, the model effectively leverages augmented data, yielding a more robust and effective ranking model for document ranking tasks.
The Surprising Effectiveness of Rankers Trained on Expanded Queries
Published in SIGIR 24, 2024
This study addresses the challenge of ranking hard queries in text systems by enriching them with relevant documents and fine-tuning a specialized ranker for these queries. By combining relevance scores from the specialized and base rankers, along with query performance scores, the method achieves significant improvements of up to 25% on passage ranking and up to 48.4% on document ranking tasks compared to baseline approaches, outperforming even state-of-the-art models.
NUMTEMP: A real-world benchmark to verify claims with statistical and temporal expressions
Published in CoRR 24, 2024
Automated fact checking has gained immense interest to tackle the growing misinformation in the digital era. Existing systems primarily focus on synthetic claims on Wikipedia, and noteworthy progress has also been made on real-world claims. In this work, we release Numtemp, a diverse, multi-domain dataset focused exclusively on numerical claims, encompassing temporal, statistical and diverse aspects with fine-grained metadata and an evidence collection without leakage. This addresses the challenge of verifying real-world numerical claims, which are complex and often lack precise information, not addressed by existing works that mainly focus on synthetic claims. We evaluate and quantify the limitations of existing solutions for the task of verifying numerical claims.
Understanding the User: An Intent-Based Ranking Dataset
Published in CIKM 24, 2024
As information retrieval systems continue to evolve, accurate evaluation and benchmarking of these systems become pivotal. Web search datasets, such as MS MARCO, primarily provide short keyword queries without accompanying intent or descriptions, posing a challenge in comprehending the underlying information need. This paper proposes an approach to augmenting such datasets to annotate informative query descriptions, with a focus on two prominent benchmark datasets: TREC-DL-21 and TREC-DL-22. Our methodology involves utilizing state-of-the-art LLMs to analyze and comprehend the implicit intent within individual queries from benchmark datasets. By extracting key semantic elements, we construct detailed and contextually rich descriptions for these queries. To validate the generated query descriptions, we employ crowdsourcing as a reliable means of obtaining diverse human perspectives on the accuracy and informativeness of the descriptions. This information can be used as an evaluation set for tasks such as ranking, query rewriting, or others.
talks
Talk 1 on Relevant Topic in Your Field
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Conference Proceeding talk 3 on Relevant Topic in Your Field
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This is a description of your conference proceedings talk, note the different field in type. You can put anything in this field.
teaching
Teaching experience 1
Undergraduate course, University 1, Department, 2014
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Teaching experience 2
Workshop, University 1, Department, 2015
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