Search Results for author:
Found 21 papers, 8 papers with code
How essential are unstructured clinical narratives and information fusion to clinical trial recruitment?
We perform an empirical study to validate the argument and show that structured data alone is insufficient in resolving eligibility criteria for recruiting patients onto clinical trials for chronic lymphocytic leukemia (CLL) and prostate cancer.
Addressing Limited Data for Textual Entailment Across Domains
We seek to address the lack of labeled data (and high cost of annotation) for textual entailment in some domains.
Annotating Electronic Medical Records for Question Answering
Over the course of 11 months, 11 medical students followed our annotation methodology, resulting in a question answering dataset of 5696 questions over 71 patient records, of which 1747 questions have corresponding answers generated by the medical students.
emrQA: A Large Corpus for Question Answering on Electronic Medical Records
We propose a novel methodology to generate domain-specific large-scale question answering (QA) datasets by re-purposing existing annotations for other NLP tasks.
Entity-Enriched Neural Models for Clinical Question Answering
We explore state-of-the-art neural models for question answering on electronic medical records and improve their ability to generalize better on previously unseen (paraphrased) questions at test time.
TransINT: Embedding Implication Rules in Knowledge Graphs with Isomorphic Intersections of Linear Subspaces
We propose TransINT, a novel and interpretable KG embedding method that isomorphically preserves the implication ordering among relations in the embedding space.
Learning to Ask Like a Physician
The questions are generated by medical experts from 100+ MIMIC-III discharge summaries.
Understanding BLOOM: An empirical study on diverse NLP tasks
We view the landscape of large language models (LLMs) through the lens of the recently released BLOOM model to understand the performance of BLOOM and other decoder-only LLMs compared to BERT-style encoder-only models.
HeySQuAD: A Spoken Question Answering Dataset
Spoken question answering (SQA) systems are critical for digital assistants and other real-world use cases, but evaluating their performance is a challenge due to the importance of human-spoken questions.
Correcting Semantic Parses with Natural Language through Dynamic Schema Encoding
In addressing the task of converting natural language to SQL queries, there are several semantic and syntactic challenges.
Self-training Strategies for Sentiment Analysis: An Empirical Study
Self-training has recently emerged as an economical and efficient technique for developing sentiment analysis models by leveraging a small amount of labeled data and a large amount of unlabeled data.
Towards leveraging LLMs for Conditional QA
This study delves into the capabilities and limitations of Large Language Models (LLMs) in the challenging domain of conditional question-answering.
BlendSQL: A Scalable Dialect for Unifying Hybrid Question Answering in Relational Algebra
Many existing end-to-end systems for hybrid question answering tasks can often be boiled down to a "prompt-and-pray" paradigm, where the user has limited control and insight into the intermediate reasoning steps used to achieve the final result.
Jetsons at FinNLP 2024: Towards Understanding the ESG Impact of a News Article using Transformer-based Models
For the impact type classification task, our XLM-RoBERTa model fine-tuned using a custom fine-tuning strategy ranked first for the English language.
Advancing Seq2seq with Joint Paraphrase Learning
We address the problem of model generalization for sequence to sequence (seq2seq) architectures.
emrKBQA: A Clinical Knowledge-Base Question Answering Dataset
We perform experiments to validate the quality of the dataset and set benchmarks for question to logical form learning that helps answer questions on this dataset.
Towards Generalizable Methods for Automating Risk Score Calculation
Clinical risk scores enable clinicians to tabulate a set of patient data into simple scores to stratify patients into risk categories.
