Demo Data from SIGIR 2026 / reviewers in the wild / expert
Eliot Brenner
dblp:135/6003
· DBLP profile ↗
3ranked-venue papers
2as first author
2since 2021 · last 2025
—ORCID · unresolved
Domains — the database's venue-derived domains; a paper can count in several
Artificial intelligence and machine learning · 3 · 2 first-author · 2 since 2021Databases, data management, data science and information retrieval · 1 · 1 first-author · 1 since 2021Applied, interdisciplinary, general and emerging computing · 1 · 1 first-author · 1 since 2021
Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.
| Artificial intelligence
1 paper |
Image recognition and object detection · 56% Language models and text generation · 44% |
Topics — the 3 heaviest of 3, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Computer vision › Image recognition and object detection › text recognition › optical character recognition
Post-OCR correction |
0.5 | 1 | 2021 | Unsupervised Multi-View Post-OCR Error Correction With Language Models · EMNLP (1) 2021 |
Natural language and speech › Language models and text generation
pre-trained language model |
0.5 | 1 | 2021 | Unsupervised Multi-View Post-OCR Error Correction With Language Models · EMNLP (1) 2021 |
Computer vision › Image recognition and object detection › text recognition
optical character recognition |
0.1 | 1 | 2021 | Unsupervised Multi-View Post-OCR Error Correction With Language Models · EMNLP (1) 2021 |
Methods — techniques the papers use, named apart from their topics
domain adaptation · 0.5
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2025 | Adaptation of Embedding Models to Financial Filings Via LLM Distillation
Eliot Brenner, Dominic Seyler, Manjunath Hegde, Andrei Simion, Koustuv Dasgupta, Bing Xiang |
IEEE Big Data | 1 |
| 2021 | Unsupervised Multi-View Post-OCR Error Correction With Language ModelsabstractWe investigate post-OCR correction in a setting where we have access to different OCR views of the same document.The goal of this study is to understand if a pretrained language model (LM) can be used in an unsupervised way to reconcile the different OCR views such that their combination contains fewer errors than each individual view.This approach is motivated by scenarios in which unconstrained text generation for error correction is too risky.We evaluated different pretrained LMs on two datasets and found significant gains in realistic scenarios with up to 15% WER improvement over the best OCR view.We also show the importance of domain adaptation for post-OCR correction on out-of-domain documents. Luciano Del Corro, Samuel Broscheit, Johannes Hoffart, Eliot Brenner |
EMNLP (1) | 5 |
| 2013 | SparsityBoost: A New Scoring Function for Learning Bayesian Network Structure
Eliot Brenner, David A. Sontag |
UAI | 1 |