Using semantic similarity for multi-label zero-shot classification of text documents

By P. Sappadla, Jinseok Nam, Eneldo Loza Mencía, Johannes Fürnkranz. 2016

54 citations recorded by the source corpus.

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In Natural Language Processing

Citations in the corpus

Benchmarking Zero-shot Text Classification: Datasets, Evaluation and Entailment Approach (2019, background): “With the prevalence of word embeddings, more and more work adopts pretrained word embed-dings to represent the meaning of words, so as to provide the models with the knowledge of labels (Sappadla et al., 2016; Yogatama et al., 2017; Rios and Kavuluru, 2018; Xia et al., 2018).”
Reconstructing Capsule Networks for Zero-shot Intent Classification (2019, background): “It has been widely studied in computer vision (Ba et al., 2015; Xian et al., 2016) and natural language processing (Sappadla et al., 2016; Zhang et al., 2019).”
Unknown Intent Detection Using Gaussian Mixture Model with an Application to Zero-shot Intent Classification (2020, background): “…methods require considerable amount of labeled data for each class to train a discriminative classiﬁer, while zero-shot intent classiﬁcation (Sappadla et al., 2016; Zhang et al., 2019) addresses the problem that not all intent categories are seen during the training phase, which is an…”
True Few-Shot Learning with Prompts—A Real-World Perspective (2021, background): “…learning, some works have investigated ways of informing classi-ﬁers about the meaning of different output classes both for text (Chang et al., 2008; Veeranna et al., 2016; Zhou et al., 2018) and image classiﬁcation (Norouzi et al., 2014; Romera-Paredes and Torr, 2015); actually providing…”
Zero-shot Text Classification via Reinforced Self-training (2020, background): “The general idea of zero-shot learning is to transfer knowledge from seen classes to unseen classes (Wang et al., 2019).”