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Found 34 papers, 18 papers with code
CantonMT: Cantonese to English NMT Platform with Fine-Tuned Models Using Synthetic Back-Translation Data
We present the models we fine-tuned using the limited amount of real data and the synthetic data we generated using back-translation including OpusMT, NLLB, and mBART.
Neural Machine Translation of Clinical Text: An Empirical Investigation into Multilingual Pre-Trained Language Models and Transfer-Learning
Furthermore, to address the language resource imbalance issue, we also carry out experiments using a transfer learning methodology based on massive multilingual pre-trained language models (MMPLMs).
Exploring the Consistency, Quality and Challenges in Manual and Automated Coding of Free-text Diagnoses from Hospital Outpatient Letters
A gold standard was constructed by a panel of clinicians from a subset of the annotated diagnoses.
Generating Medical Prescriptions with Conditional Transformer
LT3 is trained on a set of around 2K lines of medication prescriptions extracted from the MIMIC-III database, allowing the model to produce valuable synthetic medication prescriptions.
Extraction of Medication and Temporal Relation from Clinical Text using Neural Language Models
Furthermore, we also designed a set of post-processing roles to generate structured output on medications and the temporal relation.
Investigating Large Language Models and Control Mechanisms to Improve Text Readability of Biomedical Abstracts
In this work, we investigate the ability of state-of-the-art large language models (LLMs) on the task of biomedical abstract simplification, using the publicly available dataset for plain language adaptation of biomedical abstracts (\textbf{PLABA}).
MC-DRE: Multi-Aspect Cross Integration for Drug Event/Entity Extraction
Extracting meaningful drug-related information chunks, such as adverse drug events (ADE), is crucial for preventing morbidity and saving many lives.
MedMine: Examining Pre-trained Language Models on Medication Mining
Automatic medication mining from clinical and biomedical text has become a popular topic due to its real impact on healthcare applications and the recent development of powerful language models (LMs).
Predictive Data Analytics with AI: assessing the need for post-editing of MT output by fine-tuning OpenAI LLMs
Translation Quality Evaluation (TQE) is an essential step of the modern translation production process.
PULSAR at MEDIQA-Sum 2023: Large Language Models Augmented by Synthetic Dialogue Convert Patient Dialogues to Medical Records
This paper describes PULSAR, our system submission at the ImageClef 2023 MediQA-Sum task on summarising patient-doctor dialogues into clinical records.
PULSAR: Pre-training with Extracted Healthcare Terms for Summarising Patients' Problems and Data Augmentation with Black-box Large Language Models
Medical progress notes play a crucial role in documenting a patient's hospital journey, including his or her condition, treatment plan, and any updates for healthcare providers.
Do You Hear The People Sing? Key Point Analysis via Iterative Clustering and Abstractive Summarisation
Furthermore, evaluating key points is crucial in ensuring that the automatically generated summaries are useful.
Student's t-Distribution: On Measuring the Inter-Rater Reliability When the Observations are Scarce
Then, this leads to our example with two human-generated observational scores, for which, we introduce ``Student's \textit{t}-Distribution'' method and explain how to use it to measure the IRR score using only these two data points, as well as the confidence intervals (CIs) of the quality evaluation.
Topic Modelling of Swedish Newspaper Articles about Coronavirus: a Case Study using Latent Dirichlet Allocation Method
In this study, from the social-media and healthcare domain, we apply popular Latent Dirichlet Allocation (LDA) methods to model the topic changes in Swedish newspaper articles about Coronavirus.
Exploring the Value of Pre-trained Language Models for Clinical Named Entity Recognition
The experimental outcomes show that 1) CRF layers improved all language models; 2) referring to BIO-strict span level evaluation using macro-average F1 score, although the fine-tuned LLMs achieved 0. 83+ scores, the TransformerCRF model trained from scratch achieved 0. 78+, demonstrating comparable performances with much lower cost - e. g. with 39. 80\% less training parameters; 3) referring to BIO-strict span-level evaluation using weighted-average F1 score, ClinicalBERT-CRF, BERT-CRF, and TransformerCRF exhibited lower score differences, with 97. 59\%/97. 44\%/96. 84\% respectively.
Investigating Massive Multilingual Pre-Trained Machine Translation Models for Clinical Domain via Transfer Learning
To the best of our knowledge, this is the first work on using MMPLMs towards \textit{clinical domain transfer-learning NMT} successfully for totally unseen languages during pre-training.
EDU-level Extractive Summarization with Varying Summary Lengths
To fill these gaps, this paper first conducts the comparison analysis of oracle summaries based on EDUs and sentences, which provides evidence from both theoretical and experimental perspectives to justify and quantify that EDUs make summaries with higher automatic evaluation scores than sentences.
Examining Large Pre-Trained Language Models for Machine Translation: What You Don't Know About It
Pre-trained language models (PLMs) often take advantage of the monolingual and multilingual dataset that is freely available online to acquire general or mixed domain knowledge before deployment into specific tasks.
Semantics Altering Modifications for Evaluating Comprehension in Machine Reading
Advances in NLP have yielded impressive results for the task of machine reading comprehension (MRC), with approaches having been reported to achieve performance comparable to that of humans.
An efficient representation of chronological events in medical texts
In this work we addressed the problem of capturing sequential information contained in longitudinal electronic health records (EHRs).
Beyond Leaderboards: A survey of methods for revealing weaknesses in Natural Language Inference data and models
Recent years have seen a growing number of publications that analyse Natural Language Inference (NLI) datasets for superficial cues, whether they undermine the complexity of the tasks underlying those datasets and how they impact those models that are optimised and evaluated on this data.
MASK: A flexible framework to facilitate de-identification of clinical texts
The software is able to perform named entity recognition using some of the state-of-the-art techniques and then mask or redact recognized entities.
A Framework for Evaluation of Machine Reading Comprehension Gold Standards
Machine Reading Comprehension (MRC) is the task of answering a question over a paragraph of text.
GNTeam at 2018 n2c2: Feature-augmented BiLSTM-CRF for drug-related entity recognition in hospital discharge summaries
The recurrent neural networks that use the pre-trained domain-specific word embeddings and a CRF layer for label optimization perform drug, adverse event and related entities extraction with micro-averaged F1-score of over 91%.
MedNorm: A Corpus and Embeddings for Cross-terminology Medical Concept Normalisation
The medical concept normalisation task aims to map textual descriptions to standard terminologies such as SNOMED-CT or MedDRA.
Extracting adverse drug reactions and their context using sequence labelling ensembles in TAC2017
Adverse drug reactions (ADRs) are unwanted or harmful effects experienced after the administration of a certain drug or a combination of drugs, presenting a challenge for drug development and drug administration.
From web crawled text to project descriptions: automatic summarizing of social innovation projects
In the past decade, social innovation projects have gained the attention of policy makers, as they address important social issues in an innovative manner.
A framework for information extraction from tables in biomedical literature
The scientific literature is growing exponentially, and professionals are no more able to cope with the current amount of publications.
Creating a contemporary corpus of similes in Serbian by using natural language processing
Simile is a figure of speech that compares two things through the use of connection words, but where comparison is not intended to be taken literally.
As Cool as a Cucumber: Towards a Corpus of Contemporary Similes in Serbian
Similes are natural language expressions used to compare unlikely things, where the comparison is not taken literally.
ManTIME: Temporal expression identification and normalization in the TempEval-3 challenge
This paper describes a temporal expression identification and normalization system, ManTIME, developed for the TempEval-3 challenge.
LINNAEUS: A species name identification system for biomedical literature
In this paper we describe an open-source species name recognition and normalization software system, LINNAEUS, and evaluate its performance relative to several automatically generated biomedical corpora, as well as a novel corpus of full-text documents manually annotated for species mentions.
