Sentence Classification Task

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

• Context:
  • It can be solved by a Sentence Classification System (that implements a sentence classification algorithm).
  • It can range from being a Single-label Sentence Classification Task to being a Multi-label Sentence Classification Task.
  • It can range from being a Manual Sentence Classification Task to being an Automated Sentence Classification Task.
  • It can support other Text-Item Classification Tasks, such as spam detection.
  • …
• Example(s):
  • a Pubmed 200k Rct Benchmark Task, where each sentence in medical abstracts is classified based on its role in the abstract.
  • A Quora Question Pairs (QQPI) Benchmark Task, where the goal is to classify if two questions are paraphrases of each other.
  • Sentence Sentiment Classification
  • Sentence Grammatical Correctness Classification.
  • Sentence Language Classification.
  • Chatbot User Request Sentence Intent Classification (e.g. in chatbots).
  • …
• Counter-Example(s):
  • a Sentence Scoring Task.
  • a Document Classification Task.
  • a Sentence Parsing Task.
  • a Sentence Paraphrasing Task.
  • a Sentence Generation Task.
• See: Supervised Sentence Classification, Definitional Sentence, Run-on Sentence.

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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.

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