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Differential Privacy in natural language processing: The Story So Far 4

Differential Privacy in Natural Language Processing: The Sto...

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4th workshop on Privacy in natural language processing, PrivateNLP 2022

作者： Klymenko, Oleksandra Meisenbacher, Stephen Matthes, Florian Technical University of Munich Department of Informatics Garching Germany

ISBN: (纸本)9781955917797

As the tide of Big Data continues to influence the landscape of natural language processing (NLP), the utilization of modern NLP methods has grounded itself in this data, in order to tackle a variety of text-based tasks. These methods without a doubt can include private or otherwise personally identifiable information. As such, the question of privacy in NLP has gained fervor in recent years, coinciding with the development of new Privacy-Enhancing Technologies (PETs). Among these PETs, Differential Privacy boasts several desirable qualities in the conversation surrounding data privacy. naturally, the question becomes whether Differential Privacy is applicable in the largely unstructured realm of NLP. This topic has sparked novel research, which is unified in one basic goal: how can one adapt Differential Privacy to NLP methods? This paper aims to summarize the vulnerabilities addressed by Differential Privacy, the current thinking, and above all, the crucial next steps that must be considered. © 2022 Association for Computational Linguistics.

关键词： Data privacy

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WisPerMed at "Discharge Me!": Advancing Text Generation in Healthcare with Large language Models, Dynamic Expert Selection, and Priming Techniques on MIMIC-IV 23

WisPerMed at "Discharge Me!": Advancing Text Generation in H...

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23rd Meeting of the ACL Special Interest Group on Biomedical natural language processing, BioNLP 2024

作者： Damm, Hendrik Pakull, Tabea M.G. Eryılmaz, Bahadır Becker, Helmut Idrissi-Yaghir, Ahmad Schäfer, Henning Schultenkämper, Sergej Friedrich, Christoph M. Department of Computer Science University of Applied Sciences and Arts Dortmund Dortmund Germany University Hospital Essen Essen Germany Institute for Transfusion Medicine University Hospital Essen Essen Germany University Hospital Essen Essen Germany Institute of Diagnostic and Interventional Radiology and Neuroradiology University Hospital Essen Essen Germany Bielefeld University of Applied Sciences and Arts Bielefeld Germany

ISBN: (纸本)9798891761308

This study aims to leverage state of the art language models to automate generating the "Brief Hospital Course" and "Discharge Instructions" sections of Discharge Summaries from the MIMIC-IV dataset, reducing clinicians’ administrative workload. We investigate how automation can improve documentation accuracy, alleviate clinician burnout, and enhance operational efficacy in healthcare facilities. This research was conducted within our participation in the Shared Task Discharge Me! at BioNLP @ ACL 2024. Various strategies were employed, including Few-Shot learning, instruction tuning, and Dynamic Expert Selection (DES), to develop models capable of generating the required text sections. Utilizing an additional clinical domain-specific dataset demonstrated substantial potential to enhance clinical language processing. The DES method, which optimizes the selection of text outputs from multiple predictions, proved to be especially effective. It achieved the highest overall score of 0.332 in the competition, surpassing single-model outputs. This finding suggests that advanced deep learning methods in combination with DES can effectively automate parts of electronic health record documentation. These advancements could enhance patient care by freeing clinician time for patient interactions. The integration of text selection strategies represents a promising avenue for further research.. ©2024 Association for Computational Linguistics.

关键词： Electronic health record

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Human-Centered Computing in Legal NLP An Application to Refugee Status Determination 2

Human-Centered Computing in Legal NLP An Application to Refu...

引用

2nd workshop on Bridging Human-Computer Interaction and natural language processing, HCI+NLP 2022

作者： Barale, Claire The University of Edinburgh School of Informatics Edinburgh United Kingdom

ISBN: (纸本)9781955917902

This paper proposes an approach to the design of an ethical human-AI reasoning support system for decision makers in refugee law. In the context of refugee status determination, practitioners mostly rely on text data. We therefore investigate human-AI cooperation in legal natural language processing. Specifically, we want to determine which design methods can be transposed to legal text analytics. Although little work has been done so far on human-centered design methods applicable to the legal domain, we assume that introducing iterative cooperation and user engagement in the design process is (1) a method to reduce technical limitations of an NLP system and (2) that it will help design more ethical and effective applications by taking users' preferences and feedback into account. The proposed methodology is based on three main design steps: cognitive process formalization in models understandable by both humans and computers, speculative design of prototypes, and semi-directed interviews with a sample of potential users. © 2022 Association for Computational Linguistics.

关键词： natural language processing systems

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Legal Named Entity Recognition with Multi-Task Domain Adaptation 4

Legal Named Entity Recognition with Multi-Task Domain Adapta...

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4th natural Legal language processing workshop, NLLP 2022, co-located with the 2022 Conference on Empirical methods in natural language processing, EMNLP 2022

作者： Smădu, Răzvan-Alexandru Dinică, Ion-Robert Avram, Andrei-Marius Cercel, Dumitru-Clementin Pop, Florin Cercel, Mihaela-Claudia University Politehnica of Bucharest Faculty of Automatic Control and Computers Romania National Institute for Research & Development in Informatics - ICI Bucharest Romania First District Court of Buftea Romania

ISBN: (纸本)9781959429180

Named Entity Recognition (NER) is a well-explored area from Information Retrieval and natural language processing with an extensive research community. Despite that, few languages, such as English and German, are well-resourced, whereas many other languages, such as Romanian, have scarce resources, especially in domain-specific applications. In this work, we address the NER problem in the legal domain from both Romanian and German languages and evaluate the performance of our proposed method based on domain adaptation. We employ multi-task learning to jointly train a neural network on two legal and general domains and perform adaptation among them. The results show that domain adaptation increase performances by a small amount, under 1%, while considerable improvements are in the recall metric. © 2022 Association for Computational Linguistics.

关键词： natural language processing systems

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Ableist language Teching over Sign language Research 2

Ableist Language Teching over Sign Language Research

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2nd workshop on Resources and Representations for Under-Resourced languages and Domains, RESOURCEFUL 2023

作者： Börstell, Carl Norway

ISBN: (纸本)9781959429739

The progress made in computer-assisted linguistics has led to huge advances in natural language processing (NLP) research. This research often benefits linguistics in a broader sense, e.g., by digitizing pre-existing data and analyzing ever larger quantities of linguistic data in audio or visual form, such as sign language video data using computer vision methods. A large portion of research conducted on sign languages today is based in computer science and engineering, but much of this research is unfortunately conducted without any input from experts on the linguistics of sign languages or deaf communities. This is obvious from some of the language used in the published research, which regularly contains ableist labels. In this paper, I illustrate this by demonstrating the distribution of words in titles of research papers indexed by Google Scholar. By doing so, we see that the number of tech papers is increasing, while the number of linguistics papers is (relatively) decreasing, and that ableist language is more frequent in tech papers. By extension, this suggest that much of the tech-related work on sign languages - heavily under-researched and underresourced languages - is conducted without collaboration and consultation with deaf communities and experts, against ethical recommendations. © 2023 Association for Computational Linguistics.

关键词： Visual languages

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BERTbek: A Pretrained language Model for Uzbek 3

BERTbek: A Pretrained Language Model for Uzbek

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3rd Annual Meeting of the ELRA-ISCA Special Interest Group on Under-Resourced languages, SIGUL 2024

作者： Kuriyozov, Elmurod Vilares, David Gómez-Rodríguez, Carlos Universidade da Coruña CITIC Grupo LYS Depto. de Computación y Tecnologías de la Información Facultade de Informática Campus de Elviña A Coruña15071 Spain Urgench State University Department of Computer Science 14 Khamid Alimdjan street Urgench city220100 Uzbekistan

ISBN: (纸本)9782493814296

Recent advances in neural networks based language representation made it possible for pretrained language models to outperform previous models in many downstream natural language processing (NLP) tasks. These pretrained language models have also shown that if large enough, they exhibit good few-shot abilities, which is especially beneficial for low-resource scenarios. In this respect, although there are some large-scale multilingual pretrained language models available, language-specific pretrained models have demonstrated to be more accurate for monolingual evaluation setups. In this work, we present BERTbek - pretrained language models based on the BERT (Bidirectional Encoder Representations from Transformers) architecture for the low-resource Uzbek language. We also provide a comprehensive evaluation of the models on a number of NLP tasks: sentiment analysis, multi-label topic classification, and named entity recognition, comparing the models with various machine learning methods as well as multilingual BERT (mBERT). Experimental results indicate that our models outperform mBERT and other task-specific baseline models in all three tasks. Additionally, we also show the impact of training data size and quality on the downstream performance of BERT models, by training three different models with different text sources and corpus sizes. © 2024 ELRA language Resource Association.

关键词： Sentiment analysis

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Working Towards Digital Documentation of Uralic languages With Open-Source Tools and Modern NLP methods 1

Working Towards Digital Documentation of Uralic Languages Wi...

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1st Big Picture workshop: Crafting a Research Narrative, BigPicture 2023

作者： Hämäläinen, Mika Rueter, Jack Alnajjar, Khalid Partanen, Niko Metropolia University of Applied Sciences Finland University of Helsinki Finland Rootroo Ltd

ISBN: (纸本)9798891760516

We present our work towards building an infrastructure for documenting endangered languages with the focus on Uralic languages in particular. Our infrastructure consists of tools to write dictionaries so that entries are structured in XML format. These dictionaries are the foundation for rule-based NLP tools such as FSTs. We also work actively towards enhancing these dictionaries and tools by using the latest state-of-the-art neural models by generating training data through rules and lexica. © 2023 - Big Picture *** rights reserved

关键词： natural language processing systems

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Exploring the role of transformer-based language models in medical transcript summarization 7

Exploring the role of transformer-based language models in m...

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7th International Conference on Informatics and Data-Driven Medicine, IDDM 2024

作者： Zimokha, Mariia Yemets, Kyrylo Queen Mary University of London London United Kingdom Lviv Polytechnic National University Lviv Ukraine

The increasing complexity of medical documentation necessitates effective summarization tools that enable healthcare professionals to swiftly access critical patient information. This study investigates the efficacy of four transformer-based models-T5, Flan-T5, GPT-4 Mini, and BART-in summarizing medical transcripts. We conducted a comprehensive comparative analysis utilizing various learning paradigms, including zero-shot learning, one-shot learning, and pretraining methods, to assess the models' capabilities. For evaluation, we employed a diverse set of metrics: ROUGE, METEOR, and BERTScore, providing a holistic view of each model's performance. Our findings indicate that the BART model consistently outperformed the others in summarizing medical texts, demonstrating superior fluency and coherence. In contrast, the GPT-4 Mini model exhibited notable flexibility in domain-specific fine-tuning through zero-shot learning. These results highlight the potential of leveraging transformer-based models to enhance the efficiency of medical documentation processes, ultimately contributing to improved patient care and clinical outcomes. This study underscores the importance of integrating advanced natural language processing techniques into healthcare practices to address the challenges posed by complex medical information. © 2024 CEUR-WS. All rights reserved.

关键词： Zero-shot learning

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Worst of Both Worlds: Biases Compound in Pre-trained Vision-and-language Models 4

Worst of Both Worlds: Biases Compound in Pre-trained Vision-...

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4th workshop on Gender Bias in natural language processing, GeBNLP 2022

作者： Srinivasan, Tejas Bisk, Yonatan University of Southern California United States Carnegie Mellon University United States

ISBN: (纸本)9781955917681

Numerous works have analyzed biases in vision and pre-trained language models individually - however, less attention has been paid to how these biases interact in multimodal settings. This work extends text-based bias analysis methods to investigate multimodal language models, and analyzes intra- and inter-modality associations and biases learned by these models. Specifically, we demonstrate that VL-BERT (Su et al., 2020) exhibits gender biases, often preferring to reinforce a stereotype over faithfully describing the visual scene. We demonstrate these findings on a controlled case-study and extend them for a larger set of stereotypically gendered entities. © 2022 Association for Computational Linguistics.

关键词： Computational linguistics

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Reusing, Recycling and Reducing Large Models for Developing Green and Responsible language Technology 7

Reusing, Recycling and Reducing Large Models for Developing ...

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7th Doctoral Symposium on natural language processing, NLP-DS 2024

作者： Vivel-Couso, Ainhoa Universidade de Santiago de Compostela Spain

natural language (NL) is the most common and efficient tool for humans to transmit information. natural language processing (NLP), which includes NL Understanding (NLU) and NL Generation (NLG), is one of the main challenges in Artificial Intelligence (AI) and has a growing economic impact on the current digital transformation. Despite their impressive capabilities, large pre-trained language models present serious drawbacks from a research, environmental, and ethical advancement perspective. The primary research objective of this doctoral thesis is to advance the state of the art in NL technology by (i) developing efficient methods to extend existing models to new domains, genres, and languages for the official languages of Spain (Spanish, Catalan, Basque, and Galician) and English;(ii) exploring new ways to pre-train and fine-tune language models efficiently in terms of parameters, thereby reducing the carbon footprint required to train such models;(iii) addressing the explainability of large pretrained language models for NLG tasks;(iv) developing a series of advanced domain-based content applications across multiple languages, sectors and domains (e.g., Meteorology and Health) with an emphasis on explainability and evaluation tasks;and (v) defining (and overseeing compliance with) guidelines and requirements for the development of Responsible NLP with an Ethical, Legal, Social, Economical, and Cultural (ELSEC) perspective. © 2024 Copyright for this paper by its authors.

关键词： Economic and social effects

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