Semanlink - Text Classification AND Label Embedding

[1911.11506] Word-Class Embeddings for Multiclass Text Classification

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> In supervised tasks such as multiclass
text classification (the focus of this article) it seems appealing to enhance word representations
with ad-hoc embeddings that encode task-specific information. We propose (supervised) word-class
embeddings (WCEs), and show that, when concatenated to (unsupervised) pre-trained word embeddings,
they substantially facilitate the training of deep-learning models in multiclass classification by
topic.
>
> A differentiating aspect of our method is that it keeps the modelling of word-class interactions separate from the
original word embedding. Word-class correlations are confined in a dedicated vector space, whose vectors enhance
(by concatenation) the unsupervised representations. The net effect is an embedding matrix that is better suited to
classification, and imposes no restriction to the network architecture using it.

[github](https://github.com/AlexMoreo/word-class-embeddings). Refers to [LEAM](doc:2020/02/joint_embedding_of_words_and_la) :

> [in LEAM] Once words and labels are embedded in a common vector space, word-label
compatibility is measured via cosine similarity. Our method instead models these compatibilities directly, without
generating intermediate embeddings for words or labels.

2020-10-11 About

[1805.04174] Joint Embedding of Words and Labels for Text Classification (ACL Anthology 2018)

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2020-02-18 About

Knowledge-Based Short Text Categorization Using Entity and Category Embedding | Springer for Research & Development (2019)

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2019-05-30 About

[1902.05196] Categorical Metadata Representation for Customized Text Classification

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2019-02-18 About

[1607.07956] Joint Embedding of Hierarchical Categories and Entities for Concept Categorization and Dataless Classification (COLING 2016)

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2018-05-12 About