Semanlink - Data science ; NLP girls and guys AND Distributional semantics

A Self-Training Approach for Short Text Clustering - (Hadifar 2019)

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2021-05-20 A propos

[1909.01259] Neural Attentive Bag-of-Entities Model for Text Classification

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A model that performs **text classification using entities in a knowledge base**.

> 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.

2020-09-02 A propos

[1812.06280] Wikipedia2Vec: An Efficient Toolkit for Learning and Visualizing the Embeddings of Words and Entities from Wikipedia

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2020-09-02 A propos

On word embeddings

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2020-06-05 A propos

Approximating the Softmax for Learning Word Embeddings

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2020-06-04 A propos

[1703.02507] Unsupervised Learning of Sentence Embeddings using Compositional n-Gram Features

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2019-03-25 A propos

Contrastive Unsupervised Learning of Semantic Representations: A Theoretical Framework – Off the convex path (2019-03)

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2019-03-20 A propos

Word Embeddings: Explaining their properties – Off the convex path (2016)

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2019-03-20 A propos

nlp-notebooks/Simple Sentence Similarity.ipynb at master · nlptown/nlp-notebooks

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2019-02-07 A propos

[1601.01343] Joint Learning of the Embedding of Words and Entities for Named Entity Disambiguation

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2019-01-27 A propos

Cross-Lingual Word Representations: Induction and Evaluation (Tutorial EMNLP 2017)

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2018-11-05 A propos

Unsupervised Random Walk Sentence Embeddings: A Strong but Simple Baseline (Ethayarajh 2018)

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2018-10-08 A propos

Linear algebraic structure of word meanings – Off the convex path

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2018-09-20 A propos

A La Carte Embedding: Cheap but Effective Induction of Semantic Feature Vectors (2018)

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A La Carte Embedding

2018-09-18 A propos

Simple and efficient semantic embeddings for rare words, n-grams, and language features – Off the convex path

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2018-09-18 A propos

A Latent Variable Model Approach to PMI-based Word Embeddings (2016)

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2018-08-28 A propos

[1601.03764] Linear Algebraic Structure of Word Senses, with Applications to Polysemy

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> Here it is shown that multiple word senses reside
in linear superposition within the word
embedding and simple sparse coding can recover
vectors that approximately capture the
senses

> Each extracted word sense is accompanied by one of about  2000 “discourse atoms” that gives a succinct description of which other words co-occur with that word sense.

> The success of the approach is mathematically explained using a variant of
the random walk on discourses model

("random walk": a generative model for language). Under the assumptions of this model,  there
exists a linear relationship between the vector of a
word w and the vectors of the words in its contexts (It is not the average of the words in w's context, but in a given corpus the matrix of the linear relationship does not depend on w. It can be estimated, and so we can compute the embedding of a word from the contexts it belongs to)

[Related blog post](/doc/?uri=https%3A%2F%2Fwww.offconvex.org%2F2016%2F07%2F10%2Fembeddingspolysemy%2F)

2018-08-28 A propos

Learning to Understand Phrases by Embedding the Dictionary (2016)

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2018-08-23 A propos

[1608.05426] A Strong Baseline for Learning Cross-Lingual Word Embeddings from Sentence Alignments

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2018-07-23 A propos

Deep-learning-free Text and Sentence Embedding, Part 2 – Off the convex path

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2018-06-25 A propos

Deep-learning-free Text and Sentence Embedding, Part 1 – Off the convex path