Search Results for author:
Found 32 papers, 9 papers with code
Beyond Explanation: A Case for Exploratory Text Visualizations of Non-Aggregated, Annotated Datasets
This paper presents an overview of text visualization techniques relevant for data perspectivism, aiming to facilitate analysis of annotated datasets for the datasets’ creators and stakeholders.
Edinburgh_UCL_Health@SMM4H’22: From Glove to Flair for handling imbalanced healthcare corpora related to Adverse Drug Events, Change in medication and self-reporting vaccination
Our team participated in the tasks related to the Identification of Adverse Drug Events (ADEs), the classification of change in medication (change-med) and the classification of self-report of vaccination (self-vaccine).
Enhanced Labelling in Active Learning for Coreference Resolution
In this paper we describe our attempt to increase the amount of information that can be retrieved through active learning sessions compared to previous approaches.
Uncertainty and Inclusivity in Gender Bias Annotation: An Annotation Taxonomy and Annotated Datasets of British English Text
Mitigating harms from gender biased language in Natural Language Processing (NLP) systems remains a challenge, and the situated nature of language means bias is inescapable in NLP data.
Handwriting recognition for Scottish Gaelic
Like most other minority languages, Scottish Gaelic has limited tools and resources available for Natural Language Processing research and applications.
Developing Automatic Speech Recognition for Scottish Gaelic
This paper discusses our efforts to develop a full automatic speech recognition (ASR) system for Scottish Gaelic, starting from a point of limited resource.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
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Horses to Zebras: Ontology-Guided Data Augmentation and Synthesis for ICD-9 Coding
We propose data augmentation and synthesis techniques in order to address these scenarios.
Not a cute stroke: Analysis of Rule- and Neural Network-based Information Extraction Systems for Brain Radiology Reports
Our analysis finds that our rule-based system outperforms the neural models on both datasets and seems to generalise to the out-of-sample dataset.
Investigating the Capabilities and Limitations of Machine Learning for Identifying Bias in English Language Data with Information and Heritage Professionals
Despite numerous efforts to mitigate their biases, ML systems continue to harm already-marginalized people.
Evaluating and Adapting Large Language Models to Represent Folktales in Low-Resource Languages
Folktales are a rich resource of knowledge about the society and culture of a civilisation.
DeCoRe: Decoding by Contrasting Retrieval Heads to Mitigate Hallucinations
Large Language Models (LLMs) often hallucinate, producing unfaithful or factually incorrect outputs by misrepresenting the provided context or incorrectly recalling internal knowledge.
Automated neuroradiological support systems for multiple cerebrovascular disease markers -- A systematic review and meta-analysis
The systems to quantify WMH and atrophy are focused on neurodegenerative disease support, where these CVD markers are also of significance.
Perceptions of Edinburgh: Capturing Neighbourhood Characteristics by Clustering Geoparsed Local News
More specifically, we show how we can use Natural Language Processing (NLP) to unlock further information about neighbourhoods by analysing, geoparsing and clustering news articles.
A Comparative Study on Patient Language across Therapeutic Domains for Effective Patient Voice Classification in Online Health Discussions
However, the abundance of non-patient posts on social media necessitates filtering out such irrelevant content to distinguish the genuine voices of patients, a task we refer to as patient voice classification.
Infusing clinical knowledge into tokenisers for language models
Specifically, using K-Tokeniser, the language models would only require 50\% of the training data to achieve the best performance of the baseline tokeniser using all training data in the concept extraction task and less than 20\% of the data for the automated coding task.
Edinburgh Clinical NLP at MEDIQA-CORR 2024: Guiding Large Language Models with Hints
The MEDIQA-CORR 2024 shared task aims to assess the ability of Large Language Models (LLMs) to identify and correct medical errors in clinical notes.
Edinburgh Clinical NLP at SemEval-2024 Task 2: Fine-tune your model unless you have access to GPT-4
The NLI4CT task assesses Natural Language Inference systems in predicting whether hypotheses entail or contradict evidence from Clinical Trial Reports.
Can GPT-3.5 Generate and Code Discharge Summaries?
Neural coding models were trained on baseline and augmented data and evaluated on a MIMIC-IV test set.
Classifying patient voice in social media data using neural networks: A comparison of AI models on different data sources and therapeutic domains
We used linguistic analysis to understand and identify similarities between datasets, across patient language, between data sources (Reddit, SocialGist) and therapeutic domains (cardiovascular, oncology, immunology, neurology).
Parameter-Efficient Fine-Tuning of LLaMA for the Clinical Domain
In this study, we propose a two-step PEFT framework and evaluate it in the clinical domain.
Ontology-Driven and Weakly Supervised Rare Disease Identification from Clinical Notes
Computational text phenotyping is the practice of identifying patients with certain disorders and traits from clinical notes.
Automated Clinical Coding: What, Why, and Where We Are?
Knowledge-based methods that represent and reason the standard, explainable process of a task may need to be incorporated into deep learning-based methods for clinical coding.
CoPHE: A Count-Preserving Hierarchical Evaluation Metric in Large-Scale Multi-Label Text Classification
We propose a set of metrics for hierarchical evaluation using the depth-based representation.
Multi Label Text Classification
Multi-Label Text Classification
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The Online Pivot: Lessons Learned from Teaching a Text and Data Mining Course in Lockdown, Enhancing online Teaching with Pair Programming and Digital Badges
In this paper we provide an account of how we ported a text and data mining course online in summer 2020 as a result of the COVID-19 pandemic and how we improved it in a second pilot run.
A Systematic Review of Natural Language Processing Applied to Radiology Reports
Understanding recent developments in NLP application to radiology is of significance but recent reviews on this are limited.
Situated Data, Situated Systems: A Methodology to Engage with Power Relations in Natural Language Processing Research
We propose a bias-aware methodology to engage with power relations in natural language processing (NLP) research.
Geoparsing the historical Gazetteers of Scotland: accurately computing location in mass digitised texts
This paper describes work in progress on devising automatic and parallel methods for geoparsing large digital historical textual data by combining the strengths of three natural language processing (NLP) tools, the Edinburgh Geoparser, spaCy and defoe, and employing different tokenisation and named entity recognition (NER) techniques.
Plague Dot Text: Text mining and annotation of outbreak reports of the Third Plague Pandemic (1894-1952)
Our interdisciplinary research investigates more than 100 reports from the third plague pandemic (1894-1952) evaluating ways of building a corpus to extract and structure this narrative information through text mining and manual annotation.
Named Entity Recognition for Electronic Health Records: A Comparison of Rule-based and Machine Learning Approaches
This work investigates multiple approaches to Named Entity Recognition (NER) for text in Electronic Health Record (EHR) data.
Homing in on Twitter Users: Evaluating an Enhanced Geoparser for User Profile Locations
As tweet-level geotagging remains rare, most prior work exploited tweet content, timezone and network information to inform geolocation, or else relied on off-the-shelf tools to geolocate users from location information in their user profiles.
