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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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- Is the Understanding of Explicit Discourse Relations Required in Machine Reading Comprehension?6
An ablation-based methodology to assess the extent to which MRC datasets evaluate the understanding of explicit discourse relations is proposed, which implies that most of the answered questions in the examined datasets do not require understanding the discourse structure of the text.
- SynBench: A Benchmark for Differentially Private Text Generation5
This work provides the first quantitative evidence that this"public pre-training and private generation"paradigm invalidates the guaranteed privacy bounds of real-world private datasets.
- Evaluating Differentially Private Generation of Domain-Specific Text5
A unified benchmark is introduced to systematically evaluate the utility and fidelity of text datasets generated under formal Differential Privacy (DP) guarantees, revealing significant utility and fidelity degradation compared to real data, especially under strict privacy constraints.
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- Incorporating Zoning Information into Argument Mining from Biomedical Literature5
Two transformer-based models to incorporate zoning information into argumentative component identification and classification tasks are proposed and the zoning labels predicted by an off-the-shelf model are added to the beginning of each sentence, inspired by the convention commonly used biomedical abstracts.
- A survey of methods for revealing and overcoming weaknesses of data-driven Natural Language Understanding5
This structured survey provides an overview of the evolving research area by categorising reported weaknesses in models and datasets and the methods proposed to reveal and alleviate those weaknesses for the English language.
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- Generating Realistic Multi-Beat ECG Signals4
A novel three-layer synthesis framework for generating realistic long-form ECG signals that first generates high-fidelity single beats using a diffusion model, synthesize inter-beat features preserving critical temporal dependencies, and finally assemble beats into coherent long sequences using feature-guided matching.
- Are Machine Reading Comprehension Systems Robust to Context Paraphrasing?4
This paper systematically design a pipeline to semi-automatically generate perturbed MRC instances which ultimately lead to the creation of a paraphrased test set, and finds that the existing high-performing MRC systems are still far away from real language understanding.
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- Automated Clinical Coding for Outpatient Departments4
This paper is the first to investigate how well state-of-the-art deep learning-based clinical coding approaches work in the outpatient setting at hospital scale and adapt four state-of-the-art clinical coding approaches to this setting and evaluate their potential to assist coders.
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- DBee: A Database for Creating and Managing Knowledge Graphs and Embeddings3
DBee provides a unique data model which operates jointly over large-scale knowledge graphs and embedding vector spaces, and aims to facilitate the construction of KGs and VSs, by providing a library of generators.
- Pay Attention to Real World Perturbations! Natural Robustness Evaluation in Machine Reading Comprehension2
This work presents a framework to automatically examine MRC models on naturally occurring textual perturbations, by replacing paragraph in MRC benchmarks with their counterparts based on available Wikipedia edit history, and observes that natural perturbations result in performance degradation in pre-trained encoder language models.
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- Generative Models and Sentence Transformers for the Recognition and Normalization of Continuous and Discontinuous Phenotype Mentions: Model Development and Evaluation2
The findings suggest that exact extraction of entity spans may not always be necessary for successful normalization, and partial mention matches can be sufficient as long as they capture the essential concept information, supporting the system’s utility in clinical downstream tasks.
- Towards Explainable Multi-Label Text Classification: A Multi-Task Rationalisation Framework for Identifying Indicators of Forced Labour2
A novel multi-task rationalisation approach tailored to enhancing the explainability of multi-label text classifiers to identify indicators of forced labour holding promising implications for addressing complex human rights issues with greater transparency and accountability using advanced NLP techniques.
- Seemingly Plausible Distractors in Multi-Hop Reasoning: Are Large Language Models Attentive Readers?2
- MMT’s Submission for the WMT 2023 Quality Estimation Shared Task2
A straightforward training data augmentation approach aimed at improving the correlation between QE model predictions and human quality assessments and results reveal that synonym replacement via the Paraphrase Database yields the most substantial performance boost for language pairs English-German, English-Marathi and English-Gujarati.
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- Structured Information Matters: Explainable ICD Coding with Patient-Level Knowledge Graphs1
This work compute a structured representation of the input documents, making use of document-level knowledge graphs that provide a comprehensive structured view of a patient's condition, and assesses the effectiveness of this graph for automated ICD-9 coding by integrating it into the state-of-the-art ICD coding architecture PLM-ICD.
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- Toward Trustworthy Diabetes Technologies: A Dynamic Platform for Safety and Security Evaluation–
A modular hardware-in-the-loop platform enables system-level cybersecurity assessment of AID under dynamic operating conditions and highlights safety–security interdependencies in closed-loop medical therapeutics.
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- Recognition and linking of discontinuous named entities in healthcare: a comparative performance analysis–
A comparative analysis of two state-of-the-art DiscNER models shows TriG-NER is more computationally efficient and performs strongly in sentence-level settings, whereas DocDiscNER is better suited to longer and more complex document-level contexts.
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- Jacobian-Guided Anisotropic Noise Reshaping for Enhancing Representation Utility under Local Differential Privacy–
This work proposes a novel approach that mitigates noise in task-relevant subspaces of the data representation by identifying task-critical subspaces via the Jacobian of a public downstream model, selectively attenuates noise along these directions, and reshapes the isotropic noise of standard LDP mechanisms into an anisotropic distribution.
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- Investigating a Benchmark for Training-set free Evaluation of Linguistic Capabilities in Machine Reading Comprehension–
It is found that despite the simplicity of the generation method, the data can compete with crowd-sourced datasets with regard to naturalness and lexical diversity for the purpose of evaluating the linguistic capabilities of MRC models.
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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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