CLaCLab at SocialDisNER: Using Medical Gazetteers for Named-Entity Recognition of Disease Mentions in Spanish Tweets

• URL: http://arxiv.org/abs/2209.03528v1
• Date: Thu, 8 Sep 2022 02:08:51 GMT
• Title: CLaCLab at SocialDisNER: Using Medical Gazetteers for Named-Entity Recognition of Disease Mentions in Spanish Tweets
• Abstract summary: This paper summarizes the CLaC submission for SMM4H 2022 Task 10 which concerns the recognition of diseases mentioned in Spanish tweets. We encode each token with a transformer encoder using features from Multilingual RoBERTa Large, UMLS gazetteer, and DISTEMIST gazetteer. We obtain a strict F1 score of 0.869, with competition mean of 0.675, standard deviation of 0.245, and median of 0.761.
• Score: 0.7918886297003019
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This paper summarizes the CLaC submission for SMM4H 2022 Task 10 which concerns the recognition of diseases mentioned in Spanish tweets. Before classifying each token, we encode each token with a transformer encoder using features from Multilingual RoBERTa Large, UMLS gazetteer, and DISTEMIST gazetteer, among others. We obtain a strict F1 score of 0.869, with competition mean of 0.675, standard deviation of 0.245, and median of 0.761.

Related papers

• Reliable Multilingual Orthopedic Decision Support from Clinical Narratives: Language-Aware Adaptation and Verification-Guided Deferral [0.0]
  This article presents a reliability-oriented framework for classifying free-text orthopedic notes in English, Hindi and Punjabi.<n>We compare task-aligned multilingual transformer encoders, a task-fine-tuned DistilBERT baseline, zero-shot instruction-tuned large language models (LLMs) and a domain-adaptive encoder, IndicBERT-HPA.
  arXiv  Detail & Related papers  (2026-05-29T16:30:45Z)
• Generalistic or Specific Embeddings, Which is Better? An Empirical Study on Search for Clinical Coding in Non-English Languages [1.2891210250935148]
  We build a two-stage retriever (bi-encoder followed by cross-encoder reranker) on Gemini-generated synthetic data covering English, Spanish, Catalan, Italian, Portuguese and French.<n>The bi-encoder alone matches BioBERT-ST on MRR (0.876 vs. 0.866) and overtakes it on R@3 (0.650 vs. 0.626) and R@5 (0.804 vs. 0.790) without English biomedical pretraining.<n> Adding a cross-encoder reranker lifts aggregate R@5 to 0.822 and dominates on four of five languages (+0.0
  arXiv  Detail & Related papers  (2026-05-28T20:06:43Z)
• Symbolic Augmentation Closes a Canonical-Equivalence Blind Spot in Neural Fact-Checkers [1.7844382164707184]
  We introduce a five-class typed-quantity error taxonomy and a 1500-item benchmark.<n>A ModernBERT encoder fine-tuned on this benchmark reaches macro-F1 = 0.899, far above any off-the-shelf neural fact-checker.<n>We propose Symbolic Augmentation, a training-time framework that runs the modules of a symbolic verifier in reverse to generate label-preserving augmented training data.
  arXiv  Detail & Related papers  (2026-05-19T15:58:00Z)
• Beyond Long Context: When Semantics Matter More than Tokens [0.0]
  Electronic Health Records store clinical documentation as base64 attachments encoded in FHIR DocumentReference resources.<n>The Clinical Entity Augmented Retrieval (CLEAR) method, introduced by Lopez et al. 2025, uses entity aware retrieval.<n>CLEAR achieved a 58.3 percent win rate, an average semantic similarity of 0.878, and used 78 percent fewer tokens than wide context processing.
  arXiv  Detail & Related papers  (2025-10-29T16:41:44Z)
• Using LLMs for Multilingual Clinical Entity Linking to ICD-10 [3.7463543521744764]
  We propose an approach for linking clinical terms to ICD-10 codes in different languages using Large Language Models (LLMs)<n>Our system shows promising results in predicting ICD-10 codes on different benchmark datasets in Spanish.
  arXiv  Detail & Related papers  (2025-09-05T07:30:40Z)
• Application of CARE-SD text classifier tools to assess distribution of stigmatizing and doubt-marking language features in EHR [1.58767962323279]
  Stigmatizing language occurred at higher rates among historically stigmatized patients, perpetuated by multiple provider types.<n>We identified linguistic features of doubt markers and stigmatizing labels in MIMIC-III EHR.
  arXiv  Detail & Related papers  (2025-07-11T18:55:28Z)
• myNER: Contextualized Burmese Named Entity Recognition with Bidirectional LSTM and fastText Embeddings via Joint Training with POS Tagging [0.0]
  We introduce myNER, a novel word-level NER corpus featuring a 7-tag annotation scheme. We also conduct a comprehensive evaluation of NER models, including Conditional Random Fields (CRF), Bidirectional LSTM (BiLSTM)-CRF, and their combinations with fastText embeddings. Experiments reveal the effectiveness of contextualized word embeddings and the impact of joint training with POS tagging.
  arXiv  Detail & Related papers  (2025-04-05T03:13:33Z)
• Diagnosis extraction from unstructured Dutch echocardiogram reports using span- and document-level characteristic classification [0.0]
  This study tests the feasibility of automatic span- and document-level diagnosis extraction from unstructured Dutch echocardiogram reports. We developed and tested several automatic labelling techniques at both span and document levels, using weighted and macro F1-score, precision, and recall for performance evaluation. The SpanCategorizer and MedRoBERTa$.$nl models outperformed all other span and document classifiers, respectively.
  arXiv  Detail & Related papers  (2024-08-13T14:33:32Z)
• ThangDLU at #SMM4H 2024: Encoder-decoder models for classifying text data on social disorders in children and adolescents [49.00494558898933]
  This paper describes our participation in Task 3 and Task 5 of the #SMM4H (Social Media Mining for Health) 2024 Workshop. Task 3 is a multi-class classification task centered on tweets discussing the impact of outdoor environments on symptoms of social anxiety. Task 5 involves a binary classification task focusing on tweets reporting medical disorders in children. We applied transfer learning from pre-trained encoder-decoder models such as BART-base and T5-small to identify the labels of a set of given tweets.
  arXiv  Detail & Related papers  (2024-04-30T17:06:20Z)
• CTC-based Non-autoregressive Speech Translation [51.37920141751813]
  We investigate the potential of connectionist temporal classification for non-autoregressive speech translation. We develop a model consisting of two encoders that are guided by CTC to predict the source and target texts. Experiments on the MuST-C benchmarks show that our NAST model achieves an average BLEU score of 29.5 with a speed-up of 5.67$times$.
  arXiv  Detail & Related papers  (2023-05-27T03:54:09Z)
• Transformer-based Model for Word Level Language Identification in Code-mixed Kannada-English Texts [55.41644538483948]
  We propose the use of a Transformer based model for word-level language identification in code-mixed Kannada English texts. The proposed model on the CoLI-Kenglish dataset achieves a weighted F1-score of 0.84 and a macro F1-score of 0.61.
  arXiv  Detail & Related papers  (2022-11-26T02:39:19Z)
• A Scalable Workflow to Build Machine Learning Classifiers with Clinician-in-the-Loop to Identify Patients in Specific Diseases [10.658425378457363]
  Clinicians may rely on medical coding systems such as International Classification of Diseases (ICD) to identify patients with diseases from Electronic Health Records (EHRs) Recent studies suggest the ICD codes often cannot characterise patients accurately for specific diseases in real clinical practice. This paper proposes a scalable workflow which leverages both structured data and unstructured textual notes from EHRs with techniques including NLP, AutoML and Clinician-in-the-Loop mechanism.
  arXiv  Detail & Related papers  (2022-05-18T12:24:07Z)
• WSSS4LUAD: Grand Challenge on Weakly-supervised Tissue Semantic Segmentation for Lung Adenocarcinoma [51.50991881342181]
  This challenge includes 10,091 patch-level annotations and over 130 million labeled pixels. First place team achieved mIoU of 0.8413 (tumor: 0.8389, stroma: 0.7931, normal: 0.8919)
  arXiv  Detail & Related papers  (2022-04-13T15:27:05Z)
• Few-Shot Cross-lingual Transfer for Coarse-grained De-identification of Code-Mixed Clinical Texts [56.72488923420374]
  Pre-trained language models (LMs) have shown great potential for cross-lingual transfer in low-resource settings. We show the few-shot cross-lingual transfer property of LMs for named recognition (NER) and apply it to solve a low-resource and real-world challenge of code-mixed (Spanish-Catalan) clinical notes de-identification in the stroke.
  arXiv  Detail & Related papers  (2022-04-10T21:46:52Z)
• Automatic Difficulty Classification of Arabic Sentences [0.0]
  The accuracy of our 3-way CEFR classification is F-1 of 0.80 and 0.75 for Arabic-Bert and XLM-R classification respectively and 0.71 Spearman correlation for regression. We compare the use of sentence embeddings of different kinds (fastText, mBERT, XLM-R and Arabic-BERT) as well as traditional language features such as POS tags, dependency trees, readability scores and frequency lists for language learners.
  arXiv  Detail & Related papers  (2021-03-07T16:02:04Z)
• CO-Search: COVID-19 Information Retrieval with Semantic Search, Question Answering, and Abstractive Summarization [53.67205506042232]
  CO-Search is a retriever-ranker semantic search engine designed to handle complex queries over the COVID-19 literature. To account for the domain-specific and relatively limited dataset, we generate a bipartite graph of document paragraphs and citations. We evaluate our system on the data of the TREC-COVID information retrieval challenge.
  arXiv  Detail & Related papers  (2020-06-17T01:32:48Z)

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.