60 citations · 547 across the 40 of their papers we have counts for
23 papers · 1 filter
Context Aware Query Rewriting for Text Rankers using LLM
Abhijit Anand, Venktesh V, Vinay Setty +1
Query rewriting refers to an established family of approaches that are applied to underspecified and ambiguous queries to overcome the vocabulary mismatch problem in document ranki…
Query Understanding in the Age of Large Language Models
Avishek Anand, Venktesh V, Abhijit Anand +1
Querying, conversing, and controlling search and information-seeking interfaces using natural language are fast becoming ubiquitous with the rise and adoption of large-language mod…
Distribution-Aligned Fine-Tuning for Efficient Neural Retrieval
Jurek Leonhardt, Marcel Jahnke, Avishek Anand
Dual-encoder-based neural retrieval models achieve appreciable performance and complement traditional lexical retrievers well due to their semantic matching capabilities, which mak…
Supervised Contrastive Learning Approach for Contextual Ranking
Abhijit Anand, Jurek Leonhardt, Koustav Rudra +1
Contextual ranking models have delivered impressive performance improvements over classical models in the document ranking task. However, these highly over-parameterized models ten…
BERT Rankers are Brittle: a Study using Adversarial Document Perturbations
Yumeng Wang, Lijun Lyu, Avishek Anand
Contextual ranking models based on BERT are now well established for a wide range of passage and document ranking tasks. However, the robustness of BERT-based ranking models under…
Efficient Neural Ranking using Forward Indexes
Jurek Leonhardt, Koustav Rudra, Megha Khosla +2
Neural document ranking approaches, specifically transformer models, have achieved impressive gains in ranking performance. However, query processing using such over-parameterized…
Extractive Explanations for Interpretable Text Ranking
Jurek Leonhardt, Koustav Rudra, Avishek Anand
Neural document ranking models perform impressively well due to superior language understanding gained from pre-training tasks. However, due to their complexity and large number of…
Conversational Search -- A Report from Dagstuhl Seminar 19461
Avishek Anand, Lawrence Cavedon, Matthias Hagen +3
Dagstuhl Seminar 19461 "Conversational Search" was held on 10-15 November 2019. 44~researchers in Information Retrieval and Web Search, Natural Language Processing, Human Computer…
Question Answering over Curated and Open Web Sources
Rishiraj Saha Roy, Avishek Anand
The last few years have seen an explosion of research on the topic of automated question answering (QA), spanning the communities of information retrieval, natural language process…
A study on the Interpretability of Neural Retrieval Models using DeepSHAP
Zeon Trevor Fernando, Jaspreet Singh, Avishek Anand
A recent trend in IR has been the usage of neural networks to learn retrieval models for text based adhoc search. While various approaches and architectures have yielded significan…
Expedition: A Time-Aware Exploratory Search System Designed for Scholars
Jaspreet Singh, Wolfgang Nejdl, Avishek Anand
Archives are an important source of study for various scholars. Digitization and the web have made archives more accessible and led to the development of several time-aware explora…
Designing Search Tasks for Archive Search
Jaspreet Singh, Avishek Anand
Longitudinal corpora like legal, corporate and newspaper archives are of immense value to a variety of users, and time as an important factor strongly influences their search behav…
Discovering Entities with Just a Little Help from You
Jaspreet Singh, Johannes Hoffart, Avishek Anand
Linking entities like people, organizations, books, music groups and their songs in text to knowledge bases (KBs) is a fundamental task for many downstream search and mining applic…
History by Diversity: Helping Historians search News Archives
Jaspreet Singh, Wolfgang Nejdl, Avishek Anand
Longitudinal corpora like newspaper archives are of immense value to historical research, and time as an important factor for historians strongly influences their search behaviour…
Interpreting search result rankings through intent modeling
Jaspreet Singh, Avishek Anand
Given the recent interest in arguably accurate yet non-interpretable neural models, even with textual features, for document ranking we try to answer questions relating to how to i…
EXS: Explainable Search Using Local Model Agnostic Interpretability
Jaspreet Singh, Avishek Anand
Retrieval models in information retrieval are used to rank documents for typically under-specified queries. Today machine learning is used to learn retrieval models from click logs…
Posthoc Interpretability of Learning to Rank Models using Secondary Training Data
Jaspreet Singh, Avishek Anand
Predictive models are omnipresent in automated and assisted decision making scenarios. But for the most part they are used as black boxes which output a prediction without understa…
Finding News Citations for Wikipedia
Besnik Fetahu, Katja Markert, Wolfgang Nejdl +1
An important editing policy in Wikipedia is to provide citations for added statements in Wikipedia pages, where statements can be arbitrary pieces of text, ranging from a sentence…
How much is Wikipedia Lagging Behind News?
Besnik Fetahu, Abhijit Anand, Avishek Anand
Wikipedia, rich in entities and events, is an invaluable resource for various knowledge harvesting, extraction and mining tasks. Numerous resources like DBpedia, YAGO and other kno…
Automated News Suggestions for Populating Wikipedia Entity Pages
Besnik Fetahu, Katja Markert, Avishek Anand
Wikipedia entity pages are a valuable source of information for direct consumption and for knowledge-base construction, update and maintenance. Facts in these entity pages are typi…
On the Applicability of Delicious for Temporal Search on Web Archives
Helge Holzmann, Wolfgang Nejdl, Avishek Anand
Web archives are large longitudinal collections that store webpages from the past, which might be missing on the current live Web. Consequently, temporal search over such collectio…
Tempas: Temporal Archive Search Based on Tags
Helge Holzmann, Avishek Anand
Limited search and access patterns over Web archives have been well documented. One of the key reasons is the lack of understanding of the user access patterns over such collection…
Balancing Novelty and Salience: Adaptive Learning to Rank Entities for Timeline Summarization of High-impact Events
Tuan Tran, Claudia Niederée, Nattiya Kanhabua +2
Long-running, high-impact events such as the Boston Marathon bombing often develop through many stages and involve a large number of entities in their unfolding. Timeline summariza…