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

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sl:arxiv_author :
sl:arxiv_firstAuthor : Guoyin Wang
sl:arxiv_num : 1805.04174
sl:arxiv_published : 2018-05-10T20:42:52Z
sl:arxiv_summary : Word embeddings are effective intermediate representations for capturing semantic regularities between words, when learning the representations of text sequences. We propose to view text classification as a label-word joint embedding problem: each label is embedded in the same space with the word vectors. We introduce an attention framework that measures the compatibility of embeddings between text sequences and labels. The attention is learned on a training set of labeled samples to ensure that, given a text sequence, the relevant words are weighted higher than the irrelevant ones. Our method maintains the interpretability of word embeddings, and enjoys a built-in ability to leverage alternative sources of information, in addition to input text sequences. Extensive results on the several large text datasets show that the proposed framework outperforms the state-of-the-art methods by a large margin, in terms of both accuracy and speed.@en
sl:arxiv_title : Joint Embedding of Words and Labels for Text Classification@en
sl:arxiv_updated : 2018-05-10T20:42:52Z
sl:bookmarkOf : https://arxiv.org/abs/1805.04174
sl:creationDate : 2020-02-18
sl:creationTime : 2020-02-18T15:01:31Z

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Bookmark of: https://arxiv.org/abs/1805.04174

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[1911.11506] Word-Class Embeddings for Multiclass Text Classification

Tags:

> 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 A propos

Documents with similar tags (experimental)

[1911.11506] Word-Class Embeddings for Multiclass Text Classification

Tags:

> 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 A propos