Search Results for author:
Found 12 papers, 6 papers with code
BLAR: Biomedical Local Acronym Resolver
NLP has emerged as an essential tool to extract knowledge from the exponentially increasing volumes of biomedical texts.
MTRAG: A Multi-Turn Conversational Benchmark for Evaluating Retrieval-Augmented Generation Systems
Evaluating them on multi-turn RAG conversations, where the system is asked to generate a response to a question in the context of a preceding conversation is an important and often overlooked task with several additional challenges.
Beyond Labels: Empowering Human Annotators with Natural Language Explanations through a Novel Active-Learning Architecture
Our AL architecture leverages an explanation-generation model to produce explanations guided by human explanations, a prediction model that utilizes generated explanations toward prediction faithfully, and a novel data diversity-based AL sampling strategy that benefits from the explanation annotations.
SPOT: Knowledge-Enhanced Language Representations for Information Extraction
To address these problems, we propose a new pre-trained model that learns representations of both entities and relationships from token spans and span pairs in the text respectively.
Ranked #4 on
Relation Extraction
on SemEval-2010 Task-8
Label Sleuth: From Unlabeled Text to a Classifier in a Few Hours
Text classification can be useful in many real-world scenarios, saving a lot of time for end users.
Abstractified Multi-instance Learning (AMIL) for Biomedical Relation Extraction
In this work, we propose a novel reformulation of MIL for biomedical relation extraction that abstractifies biomedical entities into their corresponding semantic types.
AIT-QA: Question Answering Dataset over Complex Tables in the Airline Industry
Recent advances in transformers have enabled Table Question Answering (Table QA) systems to achieve high accuracy and SOTA results on open domain datasets like WikiTableQuestions and WikiSQL.
Development of an Enterprise-Grade Contract Understanding System
Contracts are arguably the most important type of business documents.
Theoretical Rule-based Knowledge Graph Reasoning by Connectivity Dependency Discovery
Discovering precise and interpretable rules from knowledge graphs is regarded as an essential challenge, which can improve the performances of many downstream tasks and even provide new ways to approach some Natural Language Processing research topics.
A Survey of the State of Explainable AI for Natural Language Processing
Recent years have seen important advances in the quality of state-of-the-art models, but this has come at the expense of models becoming less interpretable.
CORD-19: The COVID-19 Open Research Dataset
The COVID-19 Open Research Dataset (CORD-19) is a growing resource of scientific papers on COVID-19 and related historical coronavirus research.
NormCo: Deep Disease Normalization for Biomedical Knowledge Base Construction
Biomedical knowledge bases are crucial in modern data-driven biomedical sciences, but auto-mated biomedical knowledge base construction remains challenging.
