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In this work, we propose a novel interaction framework called Semantic Interactive Learning for the domain of document classification.
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This paper proposes a multi-feature fusion model, which uses BERT to represent the text as a word vector, BiLSTM to extract the context features of the text.
Semantic Web technologies allow the usage of features on a higher semantic level than single words for text classification ... Interactive Retrieval Evealuation ...
The semantic feature analysis strategy uses a grid to help kids explore how sets of things are related to one another.
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We therefore propose Label Semantic Aware Pre-training (LSAP) to improve the generalization and data efficiency of text classification systems. LSAP ...
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This survey explores the past and recent advancements in semantic text classification and attempts to organize existing approaches under five fundamental ...
Sep 7, 2022 · We propose a novel interaction framework called Semantic Interactive Learning for the text domain. We frame the problem of incorporating constructive and ...
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These features recognize document contexts instead of n-grams. We describe a principled methodology to solicit dictionary features from a teacher, and present ...
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This paper extends the explainable and interactive CAIPI algorithm and provides an interface to simplify human-in-the-loop approaches for image classification.
To improve the classification accuracy of Chinese news texts, we present a text classification model based on multi-level semantic features.