Semanlink - Technical girls and guys ; Representation learning AND Text Classification

> Entities provide unambiguous and relevant semantic signals that are beneficial for capturing semantics in texts. We combine **simple high-recall entity detection based on a dictionary** (word->list of entities), to detect entities in a document, with a novel neural **attention mechanism that enables the model to focus on a small number of unambiguous and relevant entities**.

2 steps:

1. Entity detection
2. Classification using the detected entities (+text) as inputs

Regarding entity linking, a local model which uses cosine
similarity between the embedding of the target
entity and the word-based representation of
the document to capture the relevance of an entity
given a document.

Embeddings from the KB: computed using [#Wikipedia2Vec](tag:wikipedia2vec) (similar words and entities
close to one another in a unified vector space)

Model using attention, with 2 features :

- cosine similarity between the
embedding of the entity and the word based
representation of the document
- the probability that the entity
name refers to the entity in KB.

Somewhat [related](doc:2020/01/investigating_entity_knowledge_)

### Conclusion:

>a neural
network model that performs text classification using
entities in Wikipedia. We combined simple
dictionary-based entity detection with a neural attention
mechanism to enable the model to focus
on a small number of unambiguous and relevant
entities in a document.

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[Learning Note] StarSpace For Multi-label Text Classification

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[1712.09405] Advances in Pre-Training Distributed Word Representations

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2017-12-29 About

facebookresearch/MUSE: A library for Multilingual Unsupervised or Supervised word Embeddings

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2017-12-22 About

Using pre-trained word embeddings in a Keras model

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[1607.01759] Bag of Tricks for Efficient Text Classification

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2017-09-10 About