Investigating the role of Named Entity Recognition in Question Answering Models

Nadapana, Vasuki; Kommanti, Hima Bindu

doi:10.1109/gcat55367.2022.9972157

Cited by 3 publications

(1 citation statement)

References 2 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…A fundamental component of nearly all automated Question Answering (QA) systems is a Named Entity Recognition (NER) model (Nadapana and Kommanti, 2022;Aliod et al, 2006). To accu-1 https://github.com/alvi-khan/ Bangla-HealthNER rately answer a question, the system must be capable of identifying important entities in the text.…”

Section: Introductionmentioning

confidence: 99%

NERvous About My Health: Constructing a Bengali Medical Named Entity Recognition Dataset

Khan,

Kamal,

Nower

et al. 2023

Findings of the Association for Computational Linguistics: EMNLP 2023

View full text Add to dashboard Cite

The ability to identify important entities in a text, known as Named Entity Recognition (NER), is useful in a large variety of downstream tasks in the biomedical domain. This is a considerably difficult task when working with Consumer Health Questions (CHQs), which consist of informal language used in day-to-day life by patients. These difficulties are amplified in the case of Bengali, which allows for a huge amount of flexibility in sentence structures and has significant variances in regional dialects. Unfortunately, the complexity of the language is not accurately reflected in the limited amount of available data, which makes it difficult to build a reliable decision-making system. To address the scarcity of data, this paper presents 'Bangla-HealthNER', a comprehensive dataset designed to identify named entities in healthrelated texts in the Bengali language. It consists of 31,783 samples sourced from a popular online public health platform, which allows it to capture the diverse range of linguistic styles and dialects used by native speakers from various regions in their day-to-day lives. The insight into this diversity in language will prove useful to any medical decision-making systems that are developed for use in real-world applications. To highlight the difficulty of the dataset, it has been benchmarked on state-ofthe-art token classification models, where Ban-glishBERT achieved the highest performance with an F1-score of 56.13 ± 0.75%. The dataset and all relevant code used in this work have been made publicly available 1 .

show abstract

Section: Introductionmentioning

confidence: 99%

NERvous About My Health: Constructing a Bengali Medical Named Entity Recognition Dataset

Khan,

Kamal,

Nower

et al. 2023

Findings of the Association for Computational Linguistics: EMNLP 2023

View full text Add to dashboard Cite

show abstract

Deep learning-based automatic analysis of legal contracts: a named entity recognition benchmark

Aejas,

Belhi,

Zhang

et al. 2024

Neural Comput & Applic

View full text Add to dashboard Cite

Question-answering system extracts information on injection drug use from clinical notes

Mahbub,

Goethert,

Danciu

et al. 2024

Commun Med

View full text Add to dashboard Cite

Background Injection drug use (IDU) can increase mortality and morbidity. Therefore, identifying IDU early and initiating harm reduction interventions can benefit individuals at risk. However, extracting IDU behaviors from patients’ electronic health records (EHR) is difficult because there is no other structured data available, such as International Classification of Disease (ICD) codes, and IDU is most often documented in unstructured free-text clinical notes. Although natural language processing can efficiently extract this information from unstructured data, there are no validated tools. Methods To address this gap in clinical information, we design a question-answering (QA) framework to extract information on IDU from clinical notes for use in clinical operations. Our framework involves two main steps: (1) generating a gold-standard QA dataset and (2) developing and testing the QA model. We use 2323 clinical notes of 1145 patients curated from the US Department of Veterans Affairs (VA) Corporate Data Warehouse to construct the gold-standard dataset for developing and evaluating the QA model. We also demonstrate the QA model’s ability to extract IDU-related information from temporally out-of-distribution data. Results Here, we show that for a strict match between gold-standard and predicted answers, the QA model achieves a 51.65% F1 score. For a relaxed match between the gold-standard and predicted answers, the QA model obtains a 78.03% F1 score, along with 85.38% Precision and 79.02% Recall scores. Moreover, the QA model demonstrates consistent performance when subjected to temporally out-of-distribution data. Conclusions Our study introduces a QA framework designed to extract IDU information from clinical notes, aiming to enhance the accurate and efficient detection of people who inject drugs, extract relevant information, and ultimately facilitate informed patient care.

show abstract

Investigating the role of Named Entity Recognition in Question Answering Models

Cited by 3 publications

References 2 publications

NERvous About My Health: Constructing a Bengali Medical Named Entity Recognition Dataset

NERvous About My Health: Constructing a Bengali Medical Named Entity Recognition Dataset

Deep learning-based automatic analysis of legal contracts: a named entity recognition benchmark

Question-answering system extracts information on injection drug use from clinical notes

Contact Info

Product

Resources

About