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- Riza Batista-NavarroSuggested from co-authorship
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Works71 from public data
- Mimic-IV-ICD: A new benchmark for eXtreme MultiLabel Classification34
This paper proposes a public benchmark suite for ICD-10 coding using a large EHR dataset derived from MIMIC-IV, the most recent public EHR datasets, and implements and compares several popular methods for I CD coding prediction tasks to standardize data preprocessing and establish a comprehensive ICD coding benchmark dataset.
- BRIDGE: Bootstrapping Text to Control Time-Series Generation via Multi-Agent Iterative Optimization and Diffusion Modeling21
It is argued that text can provide semantic insights, domain information and instance-specific temporal patterns, to guide and improve TSG, and a novel LLM-based Multi-Agent framework that synthesizes diverse, realistic text-to-TS datasets is proposed.
- Large Language Models in Argument Mining: A Survey21
This survey synthesise research and provides the first unified account of AM in the LLM era, revisiting canonical AM subtasks, and outlining persistent challenges, including long-context reasoning, multimodal and multilingual robustness, interpretability, and cost-efficient deployment.
- Semantics Altering Modifications for Evaluating Comprehension in Machine Reading21
Whether state-of-the-art MRC models are able to correctly process Semantics Altering Modifications (SAM), linguistically-motivated phenomena that alter the semantics of a sentence while preserving most of its lexical surface form, is investigated.
- Identifying Supporting Facts for Multi-hop Question Answering with Document Graph Networks18
Document Graph Network is proposed, a message passing architecture for the identification of supporting facts over a graph-structured representation of text that obtains competitive results when compared to a reading comprehension baseline operating on raw text, confirming the relevance of structured representations for supporting multi-hop reasoning.
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- Our Heritage, Our Stories : developing AI tools to link and support community-generated digital cultural heritage17
The authors' Heritage, Their Stories (OHOS), funded by the UK’s AHRC programme Towards a National Collection, responds to these urgent challenges by bringing together cutting-edge approaches from cultural heritage, humanities and computer science.
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- Vajra14
A novel end-user programming paradigm for Python is presented, which maps natural language commands into Python code and aims to reduce the barriers for producing well-formed code and for exploring third-party APIs.
- WLASL-LEX: a Dataset for Recognising Phonological Properties in American Sign Language13
This paper uses existing resources to construct a large-scale dataset of American Sign Language signs annotated with six different phonological properties, and conducts an extensive empirical study to investigate whether data-driven end-to-end and feature-based approaches can be optimised to automatically recognise these properties.
- ‘Am I the Bad One’? Predicting the Moral Judgement of the Crowd Using Pre–trained Language Models13
This paper constructs a dataset that can be used for moral judgement tasks by collecting data from the AITA? (Am I the A*******?) subreddit and harnesses the power of pre-trained language models, including BERT, RoBERTa, RoberTa-large, ALBERT and Longformer.
- PULSAR: Pre-training with Extracted Healthcare Terms for Summarising Patients' Problems and Data Augmentation with Black-box Large Language Models12
The proposed approach to this task, which integrates two complementary components, employs large language models (LLMs) for data augmentation; the other is an abstractive summarisation LLM with a novel pre-training objective for generating the patients’ problems summarised as a list.
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- Representation Learning of Structured Data for Medical Foundation Models9
The UniStruct architecture is introduced to design a multimodal medical foundation model of unstructured text and structured data, which addresses the challenges LLMs face in processing medical codes due to the shortcomings of current tokenization methods.
- From outputs to insights: a survey of rationalization approaches for explainable text classification9
An overview of the progress the community has achieved thus far in rationalization approaches for text classification is provided and various rationale-annotated data sets that facilitate the training and evaluation of rationalization models are presented.
- Entity Coreference and Co-occurrence Aware Argument Mining from Biomedical Literature8
An Entity Coreference and Co-occurrence aware Argument Mining (ECCAM) framework based on an edge-oriented graph model for BAM is proposed that argues that entities can serve as bridges that connect different ACs since entities and their mentions convey significant semantic information in biomedical argumentation.
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- Discontinuous named entities in clinical text: A systematic literature review7
Significant progress has been made in discontinuous named entity recognition; however, there remains a need for more adaptable, generalisable solutions that are independent of custom annotation schemes.
- Generating Synthetic Data with Formal Privacy Guarantees: State of the Art and the Road Ahead7
This survey provides a comprehensive framework for understanding the landscape of privacy-preserving synthetic data, presenting the theoretical foundations of generative models and differential privacy followed by a review of state-of-the-art methods across tabular data, images, and text.
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Publication data from OpenAlex, with missing venues and authors filled in from Crossref; citation counts are the higher of OpenAlex and Semantic Scholar, last synced 2026-10-12. One-sentence summaries under some papers are written by Semantic Scholar’s model. Citation counts may be lower than on Google Scholar, which indexes more sources.
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