Sentence Classification Task

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A Sentence Classification Task is a text classification task whose input is a sentence and whose output is a labeled sentence.



References

2017

  • (Dernoncourt & Lee, 2017) ⇒ Franck Dernoncourt, and Ji Young Lee. (2017). “Pubmed 200k Rct: A Dataset for Sequential Sentence Classification in Medical Abstracts.” arXiv preprint arXiv:1710.06071
    • NOTE:
      1. Sentence Role Classification: Each sentence in the medical abstracts is classified based on its role, such as background, objective, methods, results, or conclusions.
      2. Sequential Context Consideration: Unlike isolated sentence classification, this task involves understanding the sequence and context in which sentences appear within an abstract.
      3. Handling Large-Scale Corpus: The dataset provides a large-scale setting with approximately 200,000 abstracts, which is crucial for developing robust models that can handle real-world, extensive datasets.
      4. Domain-Specific Language Processing: Focusing on medical texts, the task involves understanding and processing specialized language and terminology used in the medical field.
      5. Application in Efficient Literature Review: The ultimate goal of this classification task is to aid researchers in efficiently skimming through medical literature, which can be particularly helpful in fields where abstracts are lengthy and dense with information.

2012

  • (Chang et al., 2012) ⇒ Yi Chang, Jana Diesner, and Kathleen M. Carley. (2012). “Toward Automated Definition Acquisition From Operations Law.” In: IEEE Transactions on Systems, Man, and Cybernetics, 42(2). doi:10.1109/TSMCC.2011.2110643
    • NOTE:
      • It explores the automation of definition acquisition from operations law for assisting military personnel.
      • It frames the process as a sentence classification task, addressed using machine learning techniques.
      • It reports high accuracy with supervised learning methods, achieving significant F1 and recall scores.
      • It addresses the challenge of manual data labeling by proposing a semi-supervised learning approach.
      • It provides insights into the balance between accuracy and efficiency in machine learning for legal applications.