Label Embedding
http://www.semanlink.net/tag/label_embedding
Documents tagged with Label Embedding[1911.11506] Word-Class Embeddings for Multiclass Text Classification
http://www.semanlink.net/doc/2020/10/1911_11506_word_class_embeddi
> 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.
2020-10-11T19:29:28Z[1805.04174] Joint Embedding of Words and Labels for Text Classification (ACL Anthology 2018)
http://www.semanlink.net/doc/2020/02/joint_embedding_of_words_and_la
> 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.
(from introduction:)
> For the task of text classification,
labels play a central role of the final performance.
A natural question to ask is how we can
directly use label information in constructing the
text-sequence representations
> The proposed LEAM (Label-
Embedding Attentive Mode) is implemented by jointly
embedding the word and label in the same latent
space, and **the text representations are constructed
directly using the text-label compatibility**.
2020-02-18T15:01:31Z[1503.08677] Label-Embedding for Image Classification
http://www.semanlink.net/doc/2020/02/_1503_08677_label_embedding_fo
2020-02-18T15:00:20Zrakuten-nlp/category2vec (2015)
http://www.semanlink.net/doc/2019/08/rakuten_nlp_category2vec
2019-08-05T09:31:44ZKnowledge-Based Short Text Categorization Using Entity and Category Embedding | Springer for Research & Development (2019)
http://www.semanlink.net/doc/2019/05/knowledge_based_short_text_cate
> we propose a novel probabilistic model for Knowledge-Based Short Text Categorization (KBSTC), **which does not require any labeled training data to classify a short text**. This is achieved by leveraging **entities and categories from large knowledge bases**, which are further embedded into a common vector space, for which we propose a new entity and category embedding model. **Given a short text, its category (e.g. Business, Sports, etc.) can then be derived based on the entities mentioned in the text by exploiting semantic similarity between entities and categories**
2019-05-30T11:38:19Z[1902.05196] Categorical Metadata Representation for Customized Text Classification
https://arxiv.org/abs/1902.05196v1
> We observe that **current representation methods for categorical metadata... are not as effective as claimed** in popular classification methods, outperformed even by simple concatenation of categorical features in the final layer of the sentence encoder. We conjecture that categorical features are harder to represent for machine use, as available context only indirectly describes the category
2019-02-18T08:20:43Z[1607.07956] Joint Embedding of Hierarchical Categories and Entities for Concept Categorization and Dataless Classification (COLING 2016)
https://arxiv.org/abs/1607.07956
a framework that embeds entities and categories into a semantic space by integrating structured
knowledge and taxonomy hierarchy from large knowledge bases.
two methods:
1. Category Embedding model: it replaces the entities in the context with their directly
labeled categories to build categories’ context;
2. Hierarchical Category Embedding: it
further incorporates all ancestor categories of the context entities to utilize the hierarchical information.
2018-05-12T16:41:35Z