Sentence Classification Task: Difference between revisions

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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]].
A [[Sentence Classification Task]] is a [[text classification task]] whose input is a [[sentence]] and whose output is a [[labeled sentence]].
* <B>Context:</B>
* <B>Context:</B>
** It can be solved by a [[Sentence Classification System]] (that implements a [[sentence classification algorithm]]).
** 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 [[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 [[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 Task]]s, such as [[spam detection]].
** It can support other [[Text-Item Classification Task]]s, such as [[spam detection]].
**
** It can require [[Context Analysis]] for accurate classification.
** ...
* <B>Example(s):</B>
* <B>Example(s):</B>
** a [[Pubmed 200k Rct Benchmark Task]], where each sentence in medical abstracts is classified based on its role in the abstract.
** [[Pubmed 200k Rct Benchmark Task]]s, where sentences in medical abstracts are classified by role.
** A [[Quora Question Pairs (QQPI) Benchmark Task]], where the goal is to classify if two questions are paraphrases of each other.
** [[Quora Question Pairs (QQPI) Benchmark Task]]s, which classify question similarity.
** [[Sentence Sentiment Classification]]
** [[Sentence Sentiment Classification]]s, which determine emotional content.
** [[Sentence Grammatical Correctness Classification]].
** [[Sentence Grammatical Correctness Classification]]s, which verify grammar.
** [[Sentence Language Classification]].
** [[Sentence Language Classification]]s, which identify the language.
** [[Chatbot User Request Sentence Intent Classification]] (e.g. in [[chatbot]]s).
** [[Chatbot User Request Sentence Intent Classification]]s, for understanding user intent.
** [[Contract Sentence Classification]].
** [[Contract Sentence Classification]]s, for legal document analysis.
**
** ...
* <B>Counter-Example(s):</B>
* <B>Counter-Example(s):</B>
** a [[Sentence Scoring Task]].
** [[Sentence Scoring Task]]s, which assign numeric values.
** a [[Document Classification Task]].
** [[Document Classification Task]]s, which work with full documents.
** a [[Sentence Parsing Task]].
** [[Sentence Parsing Task]]s, which analyze structure.
** a [[Sentence Paraphrasing Task]].
** [[Sentence Paraphrasing Task]]s, which rewrite content.
** a [[Sentence Generation Task]].
** [[Sentence Generation Task]]s, which create new sentences.
* <B>See:</B> [[Supervised Sentence Classification]], [[Definitional Sentence]], [[Run-on Sentence]].
* <B>See:</B> [[Supervised Sentence Classification]], [[Definitional Sentence]], [[Run-on Sentence]].



Latest revision as of 07:28, 13 November 2024

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.