1 code implementation • 29 Mar 2024 • Monica Munnangi, Sergey Feldman, Byron C Wallace, Silvio Amir, Tom Hope, Aakanksha Naik
In this work we set out to improve LLM performance on biomedical NER in limited data settings via a new knowledge augmentation approach which incorporates definitions of relevant concepts on-the-fly.
1 code implementation • 27 Jul 2023 • Katherine A. Keith, Sergey Feldman, David Jurgens, Jonathan Bragg, Rohit Bhattacharya
We contribute a new sampling algorithm, which we call RCT rejection sampling, and provide theoretical guarantees that causal identification holds in the observational data to allow for valid comparisons to the ground-truth RCT.
1 code implementation • 30 Apr 2023 • Yuze Lou, Bailey Kuehl, Erin Bransom, Sergey Feldman, Aakanksha Naik, Doug Downey
Entity linking (EL) is the task of linking a textual mention to its corresponding entry in a knowledge base, and is critical for many knowledge-intensive NLP applications.
1 code implementation • 24 Jan 2023 • Rodney Kinney, Chloe Anastasiades, Russell Authur, Iz Beltagy, Jonathan Bragg, Alexandra Buraczynski, Isabel Cachola, Stefan Candra, Yoganand Chandrasekhar, Arman Cohan, Miles Crawford, Doug Downey, Jason Dunkelberger, Oren Etzioni, Rob Evans, Sergey Feldman, Joseph Gorney, David Graham, Fangzhou Hu, Regan Huff, Daniel King, Sebastian Kohlmeier, Bailey Kuehl, Michael Langan, Daniel Lin, Haokun Liu, Kyle Lo, Jaron Lochner, Kelsey MacMillan, Tyler Murray, Chris Newell, Smita Rao, Shaurya Rohatgi, Paul Sayre, Zejiang Shen, Amanpreet Singh, Luca Soldaini, Shivashankar Subramanian, Amber Tanaka, Alex D. Wade, Linda Wagner, Lucy Lu Wang, Chris Wilhelm, Caroline Wu, Jiangjiang Yang, Angele Zamarron, Madeleine van Zuylen, Daniel S. Weld
The volume of scientific output is creating an urgent need for automated tools to help scientists keep up with developments in their field.
2 code implementations • 23 Nov 2022 • Amanpreet Singh, Mike D'Arcy, Arman Cohan, Doug Downey, Sergey Feldman
In response, we introduce SciRepEval, the first comprehensive benchmark for training and evaluating scientific document representations.
1 code implementation • Findings (NAACL) 2022 • Aakanksha Naik, Sravanthi Parasa, Sergey Feldman, Lucy Lu Wang, Tom Hope
We present BEEP (Biomedical Evidence-Enhanced Predictions), a novel approach for clinical outcome prediction that retrieves patient-specific medical literature and incorporates it into predictive models.
no code implementations • 11 Aug 2021 • Asia J. Biega, Fernando Diaz, Michael D. Ekstrand, Sergey Feldman, Sebastian Kohlmeier
This paper provides an overview of the NIST TREC 2020 Fair Ranking track.
1 code implementation • 3 Mar 2021 • Sean MacAvaney, Andrew Yates, Sergey Feldman, Doug Downey, Arman Cohan, Nazli Goharian
Managing the data for Information Retrieval (IR) experiments can be challenging.
2 code implementations • 2 Nov 2020 • Sean MacAvaney, Sergey Feldman, Nazli Goharian, Doug Downey, Arman Cohan
Pretrained contextualized language models such as BERT and T5 have established a new state-of-the-art for ad-hoc search.
5 code implementations • ACL 2020 • Arman Cohan, Sergey Feldman, Iz Beltagy, Doug Downey, Daniel S. Weld
We propose SPECTER, a new method to generate document-level embedding of scientific documents based on pretraining a Transformer language model on a powerful signal of document-level relatedness: the citation graph.
Ranked #1 on Document Classification on SciDocs (MAG)
no code implementations • NAACL 2018 • Waleed Ammar, Dirk Groeneveld, Chandra Bhagavatula, Iz Beltagy, Miles Crawford, Doug Downey, Jason Dunkelberger, Ahmed Elgohary, Sergey Feldman, Vu Ha, Rodney Kinney, Sebastian Kohlmeier, Kyle Lo, Tyler Murray, Hsu-Han Ooi, Matthew Peters, Joanna Power, Sam Skjonsberg, Lucy Lu Wang, Chris Wilhelm, Zheng Yuan, Madeleine van Zuylen, Oren Etzioni
We describe a deployed scalable system for organizing published scientific literature into a heterogeneous graph to facilitate algorithmic manipulation and discovery.
1 code implementation • NAACL 2018 • Chandra Bhagavatula, Sergey Feldman, Russell Power, Waleed Ammar
We present a content-based method for recommending citations in an academic paper draft.
no code implementations • NeurIPS 2012 • Sergey Feldman, Maya Gupta, Bela Frigyik
We present a multi-task learning approach to jointly estimate the means of multiple independent data sets.