Papers with sentence classification approaches

2 papers
Hierarchical Neural Networks for Sequential Sentence Classification in Medical Scientific Abstracts (D18-1)

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Challenge: Existing sentences classification models often classify sentences in isolation without considering the context in which sentences appear.
Approach: They propose a hierarchical sequential labeling network to make use of contextual information within surrounding sentences to help classify the current sentence.
Outcome: The proposed model outperforms the state-of-the-art methods by 2%-3% on two benchmarking datasets for sequential sentence classification in medical scientific abstracts.
Detecting Sexual Content at the Sentence Level in First Millennium Latin Texts (2024.lrec-main)

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Challenge: a traditional approach to corpus building involves constructing a corpus centered around specific themes, such as colors.
Approach: They propose to use deep learning methods to accelerate corpus building in humanities . they propose to integrate metadata embeddings into the model to improve accuracy .
Outcome: The proposed method outperforms token-based searches in the humanities and linguistics field.

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