Cheap Ways of Extracting Clinical Markers from Texts

• URL: http://arxiv.org/abs/2403.11227v1
• Date: Sun, 17 Mar 2024 14:21:42 GMT
• Title: Cheap Ways of Extracting Clinical Markers from Texts
• Authors: Anastasia Sandu, Teodor Mihailescu, Sergiu Nisioi,
• Abstract summary: This paper describes the work of the UniBuc Archaeology team for CLPsych's 2024 Shared Task. It involved finding evidence within the text supporting the assigned suicide risk level. Two types of evidence were required: highlights and summaries.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This paper describes the work of the UniBuc Archaeology team for CLPsych's 2024 Shared Task, which involved finding evidence within the text supporting the assigned suicide risk level. Two types of evidence were required: highlights (extracting relevant spans within the text) and summaries (aggregating evidence into a synthesis). Our work focuses on evaluating Large Language Models (LLM) as opposed to an alternative method that is much more memory and resource efficient. The first approach employs a good old-fashioned machine learning (GOML) pipeline consisting of a tf-idf vectorizer with a logistic regression classifier, whose representative features are used to extract relevant highlights. The second, more resource intensive, uses an LLM for generating the summaries and is guided by chain-of-thought to provide sequences of text indicating clinical markers.

Related papers

• Scaling Up Summarization: Leveraging Large Language Models for Long Text Extractive Summarization [0.27624021966289597]
  This paper introduces EYEGLAXS, a framework that leverages Large Language Models (LLMs) for extractive summarization. EYEGLAXS focuses on extractive summarization to ensure factual and grammatical integrity. The system sets new performance benchmarks on well-known datasets like PubMed and ArXiv.
  arXiv  Detail & Related papers  (2024-08-28T13:52:19Z)
• Extracting and Encoding: Leveraging Large Language Models and Medical Knowledge to Enhance Radiological Text Representation [31.370503681645804]
  We present a novel two-stage framework designed to extract high-quality factual statements from free-text radiology reports. Our framework also includes a new embedding-based metric ( CXRFE) for evaluating chest X-ray text generation systems.
  arXiv  Detail & Related papers  (2024-07-02T04:39:19Z)
• PromptReps: Prompting Large Language Models to Generate Dense and Sparse Representations for Zero-Shot Document Retrieval [76.50690734636477]
  We propose PromptReps, which combines the advantages of both categories: no need for training and the ability to retrieve from the whole corpus. The retrieval system harnesses both dense text embedding and sparse bag-of-words representations.
  arXiv  Detail & Related papers  (2024-04-29T04:51:30Z)
• ULTRA: Unleash LLMs' Potential for Event Argument Extraction through Hierarchical Modeling and Pair-wise Refinement [6.39480325103865]
  Event argument extraction (EAE) is the task of identifying role-specific text spans (i.e., arguments) for a given event. We propose a hierarchical framework that extracts event arguments more cost-effectively.
  arXiv  Detail & Related papers  (2024-01-24T04:13:28Z)
• Harnessing Explanations: LLM-to-LM Interpreter for Enhanced Text-Attributed Graph Representation Learning [51.90524745663737]
  A key innovation is our use of explanations as features, which can be used to boost GNN performance on downstream tasks. Our method achieves state-of-the-art results on well-established TAG datasets. Our method significantly speeds up training, achieving a 2.88 times improvement over the closest baseline on ogbn-arxiv.
  arXiv  Detail & Related papers  (2023-05-31T03:18:03Z)
• Element-aware Summarization with Large Language Models: Expert-aligned Evaluation and Chain-of-Thought Method [35.181659789684545]
  Automatic summarization generates concise summaries that contain key ideas of source documents. References from CNN/DailyMail and BBC XSum are noisy, mainly in terms of factual hallucination and information redundancy. We propose a Summary Chain-of-Thought (SumCoT) technique to elicit LLMs to generate summaries step by step. Experimental results show our method outperforms state-of-the-art fine-tuned PLMs and zero-shot LLMs by +4.33/+4.77 in ROUGE-L.
  arXiv  Detail & Related papers  (2023-05-22T18:54:35Z)
• Lay Text Summarisation Using Natural Language Processing: A Narrative Literature Review [1.8899300124593648]
  The aim of this literature review is to describe and compare the different text summarisation approaches used to generate lay summaries. We screened 82 articles and included eight relevant papers published between 2020 and 2021, using the same dataset. A combination of extractive and abstractive summarisation methods in a hybrid approach was found to be most effective.
  arXiv  Detail & Related papers  (2023-03-24T18:30:50Z)
• ReSel: N-ary Relation Extraction from Scientific Text and Tables by Learning to Retrieve and Select [53.071352033539526]
  We study the problem of extracting N-ary relations from scientific articles. Our proposed method ReSel decomposes this task into a two-stage procedure. Our experiments on three scientific information extraction datasets show that ReSel outperforms state-of-the-art baselines significantly.
  arXiv  Detail & Related papers  (2022-10-26T02:28:02Z)
• TRIE++: Towards End-to-End Information Extraction from Visually Rich Documents [51.744527199305445]
  This paper proposes a unified end-to-end information extraction framework from visually rich documents. Text reading and information extraction can reinforce each other via a well-designed multi-modal context block. The framework can be trained in an end-to-end trainable manner, achieving global optimization.
  arXiv  Detail & Related papers  (2022-07-14T08:52:07Z)
• UnifieR: A Unified Retriever for Large-Scale Retrieval [84.61239936314597]
  Large-scale retrieval is to recall relevant documents from a huge collection given a query. Recent retrieval methods based on pre-trained language models (PLM) can be coarsely categorized into either dense-vector or lexicon-based paradigms. We propose a new learning framework, UnifieR which unifies dense-vector and lexicon-based retrieval in one model with a dual-representing capability.
  arXiv  Detail & Related papers  (2022-05-23T11:01:59Z)
• Commonsense Evidence Generation and Injection in Reading Comprehension [57.31927095547153]
  We propose a Commonsense Evidence Generation and Injection framework in reading comprehension, named CEGI. The framework injects two kinds of auxiliary commonsense evidence into comprehensive reading to equip the machine with the ability of rational thinking. Experiments on the CosmosQA dataset demonstrate that the proposed CEGI model outperforms the current state-of-the-art approaches.
  arXiv  Detail & Related papers  (2020-05-11T16:31:08Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.