Self-Supervised Detection of Contextual Synonyms in a Multi-Class Setting: Phenotype Annotation Use Case

• URL: http://arxiv.org/abs/2109.01935v1
• Date: Sat, 4 Sep 2021 21:35:01 GMT
• Title: Self-Supervised Detection of Contextual Synonyms in a Multi-Class Setting: Phenotype Annotation Use Case
• Authors: Jingqing Zhang, Luis Bolanos, Tong Li, Ashwani Tanwar, Guilherme Freire, Xian Yang, Julia Ive, Vibhor Gupta, Yike Guo
• Abstract summary: Contextualised word embeddings is a powerful tool to detect contextual synonyms. We propose a self-supervised pre-training approach which is able to detect contextual synonyms of concepts being training on the data created by shallow matching.
• Score: 11.912581294872767
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Contextualised word embeddings is a powerful tool to detect contextual synonyms. However, most of the current state-of-the-art (SOTA) deep learning concept extraction methods remain supervised and underexploit the potential of the context. In this paper, we propose a self-supervised pre-training approach which is able to detect contextual synonyms of concepts being training on the data created by shallow matching. We apply our methodology in the sparse multi-class setting (over 15,000 concepts) to extract phenotype information from electronic health records. We further investigate data augmentation techniques to address the problem of the class sparsity. Our approach achieves a new SOTA for the unsupervised phenotype concept annotation on clinical text on F1 and Recall outperforming the previous SOTA with a gain of up to 4.5 and 4.0 absolute points, respectively. After fine-tuning with as little as 20\% of the labelled data, we also outperform BioBERT and ClinicalBERT. The extrinsic evaluation on three ICU benchmarks also shows the benefit of using the phenotypes annotated by our model as features.

Related papers

• Large-scale investigation of weakly-supervised deep learning for the fine-grained semantic indexing of biomedical literature [7.171698704686836]
  This study proposes a new method for the automated refinement of subject annotations at the level of MeSH concepts. The new method is evaluated on a large-scale retrospective scenario, based on concepts promoted to descriptors.
  arXiv  Detail & Related papers  (2023-01-23T10:33:22Z)
• PromptCAL: Contrastive Affinity Learning via Auxiliary Prompts for Generalized Novel Category Discovery [39.03732147384566]
  Generalized Novel Category Discovery (GNCD) setting aims to categorize unlabeled training data coming from known and novel classes. We propose Contrastive Affinity Learning method with auxiliary visual Prompts, dubbed PromptCAL, to address this challenging problem. Our approach discovers reliable pairwise sample affinities to learn better semantic clustering of both known and novel classes for the class token and visual prompts.
  arXiv  Detail & Related papers  (2022-12-11T20:06:14Z)
• DetCLIP: Dictionary-Enriched Visual-Concept Paralleled Pre-training for Open-world Detection [118.36746273425354]
  This paper presents a paralleled visual-concept pre-training method for open-world detection by resorting to knowledge enrichment from a designed concept dictionary. By enriching the concepts with their descriptions, we explicitly build the relationships among various concepts to facilitate the open-domain learning. The proposed framework demonstrates strong zero-shot detection performances, e.g., on the LVIS dataset, our DetCLIP-T outperforms GLIP-T by 9.9% mAP and obtains a 13.5% improvement on rare categories.
  arXiv  Detail & Related papers  (2022-09-20T02:01:01Z)
• A Unified Understanding of Deep NLP Models for Text Classification [88.35418976241057]
  We have developed a visual analysis tool, DeepNLPVis, to enable a unified understanding of NLP models for text classification. The key idea is a mutual information-based measure, which provides quantitative explanations on how each layer of a model maintains the information of input words in a sample. A multi-level visualization, which consists of a corpus-level, a sample-level, and a word-level visualization, supports the analysis from the overall training set to individual samples.
  arXiv  Detail & Related papers  (2022-06-19T08:55:07Z)
• Better Language Model with Hypernym Class Prediction [101.8517004687825]
  Class-based language models (LMs) have been long devised to address context sparsity in $n$-gram LMs. In this study, we revisit this approach in the context of neural LMs.
  arXiv  Detail & Related papers  (2022-03-21T01:16:44Z)
• Knowledge-Rich Self-Supervised Entity Linking [58.838404666183656]
  Knowledge-RIch Self-Supervision ($tt KRISSBERT$) is a universal entity linker for four million UMLS entities. Our approach subsumes zero-shot and few-shot methods, and can easily incorporate entity descriptions and gold mention labels if available. Without using any labeled information, our method produces $tt KRISSBERT$, a universal entity linker for four million UMLS entities.
  arXiv  Detail & Related papers  (2021-12-15T05:05:12Z)
• Revisiting Self-Training for Few-Shot Learning of Language Model [61.173976954360334]
  Unlabeled data carry rich task-relevant information, they are proven useful for few-shot learning of language model. In this work, we revisit the self-training technique for language model fine-tuning and present a state-of-the-art prompt-based few-shot learner, SFLM.
  arXiv  Detail & Related papers  (2021-10-04T08:51:36Z)
• Inserting Information Bottlenecks for Attribution in Transformers [46.77580577396633]
  We apply information bottlenecks to analyze the attribution of each feature for prediction on a black-box model. We show the effectiveness of our method in terms of attribution and the ability to provide insight into how information flows through layers.
  arXiv  Detail & Related papers  (2020-12-27T00:35:43Z)
• Weakly-Supervised Aspect-Based Sentiment Analysis via Joint Aspect-Sentiment Topic Embedding [71.2260967797055]
  We propose a weakly-supervised approach for aspect-based sentiment analysis. We learn sentiment, aspect> joint topic embeddings in the word embedding space. We then use neural models to generalize the word-level discriminative information.
  arXiv  Detail & Related papers  (2020-10-13T21:33:24Z)
• Multi-domain Clinical Natural Language Processing with MedCAT: the Medical Concept Annotation Toolkit [5.49956798378633]
  We present the open-source Medical Concept EHR Toolkit (MedMedCAT) It provides a novel self-supervised machine learning algorithm for extracting concepts using any concept vocabulary including UMLS/SNOMED-CT. We show improved performance in extracting UMLS concepts from open datasets. Further real-world validation demonstrates SNOMED-CT extraction at 3 large London hospitals with self-supervised training over 8.8B words from 17M clinical records.
  arXiv  Detail & Related papers  (2020-10-02T19:01:02Z)
• PhenoTagger: A Hybrid Method for Phenotype Concept Recognition using Human Phenotype Ontology [6.165755812152143]
  PhenoTagger is a hybrid method that combines both dictionary and machine learning-based methods to recognize concepts in unstructured text. Our method is validated with two HPO corpora, and the results show that PhenoTagger compares favorably to previous methods.
  arXiv  Detail & Related papers  (2020-09-17T18:00:43Z)

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.