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 (typically) involve Sentence Feature Extraction and Label Assignment.
  • It can (often) require Sentence Understanding and Classification Model Training.
  • ...
  • 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 range from being a Domain-Specific Sentence Classification to being an Open-Domain Sentence Classification.
  • ...
  • It can be solved by a Sentence Classification System that implements a sentence classification algorithm.
  • It can support other Text-Item Classification Tasks, such as spam detection.
  • It can require Context Analysis for accurate classification.
  • ...
• Example(s):
  • Pubmed 200k Rct Benchmark Tasks, where sentences in medical abstracts are classified by role.
  • Quora Question Pairs (QQPI) Benchmark Tasks, which classify question similarity.
  • Sentence Sentiment Classifications, which determine emotional content.
  • Sentence Grammatical Correctness Classifications, which verify grammar.
  • Sentence Language Classifications, which identify the language.
  • Chatbot User Request Sentence Intent Classifications, for understanding user intent.
  • Contract Sentence Classifications, for legal document analysis.
  • ...
• Counter-Example(s):
  • Sentence Scoring Tasks, which assign numeric values.
  • Document Classification Tasks, which work with full documents.
  • Sentence Parsing Tasks, which analyze structure.
  • Sentence Paraphrasing Tasks, which rewrite content.
  • Sentence Generation Tasks, which create new sentences.
• 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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