Semanlink - Analyse sémantique AND Data mining

> 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

BERT Word Embeddings Tutorial · Chris McCormick

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2020-07-06 A propos

[1910.00163] Specializing Word Embeddings (for Parsing) by Information Bottleneck

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2020-06-29 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

[1709.03933] Hash Embeddings for Efficient Word Representations

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2020-05-19 A propos

[1910.04126] Scalable Nearest Neighbor Search for Optimal Transport

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2020-02-20 A propos

davidsbatista/BREDS: "Bootstrapping Relationship Extractors with Distributional Semantics" (Batista et al., 2015) - code for EMNLP'15 paper

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2020-01-11 A propos

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2020-01-10 A propos

Lecture 14 – Contextual Vectors | Stanford CS224U: Natural Language Understanding | Spring 2019

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2020-01-05 A propos

Open-sourcing hyperparameter autotuning for fastText

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FastText

2019-08-27 A propos

[1810.04882] Towards Understanding Linear Word Analogies

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2019-06-24 A propos

Kawin Ethayarajh sur Twitter : "When and why does king - man + woman = queen?"

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Word embeddings

2019-06-24 A propos

Word Embeddings: 6 Years Later

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Word embeddings

2019-06-03 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

[1902.11269] Efficient Contextual Representation Learning Without Softmax Layer

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**how to accelerate contextual representation learning**.

> Contextual representation models are difficult to train due to the large parameter sizes and high computational complexity

> We find that the softmax layer (the output layer) causes significant inefficiency due to the large vocabulary size.
Therefore, we redesign the learning objectiv.
> Specifically, the proposed approach bypasses the softmax layer by performing language modeling with dimension reduction, and allows the models to leverage pre-trained word embeddings.
Our framework reduces the time spent on the output layer to a negligible level, eliminates almost all the trainable parameters of the softmax layer and performs language modeling without truncating the vocabulary.
When applied to ELMo, our method achieves a 4 times speedup and eliminates 80% trainable parameters while achieving competitive performance on downstream tasks.

**decouples learning contexts and words**

> Instead of using
a softmax layer to predict the distribution of the
missing word, we utilize and extend the SEMFIT
layer (Kumar and Tsvetkov, 2018) to **predict the
embedding of the missing word**.

2019-03-02 A propos

[1902.10618] Still a Pain in the Neck: Evaluating Text Representations on Lexical Composition

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2019-02-28 A propos

Keywords2vec

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2019-02-09 A propos

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

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2019-02-07 A propos

BPEmb: Subword Embeddings

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2019-01-31 A propos

Cheatsheet · fastText

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2019-01-29 A propos

Using FastText models (not vectors) for robust embeddings | Kaggle

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2019-01-29 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

Wikipedia2Vec

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2018-12-23 A propos

The Illustrated BERT, ELMo, and co. (How NLP Cracked Transfer Learning) – Jay Alammar

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2018-12-03 A propos

[1811.05370] Unsupervised Transfer Learning for Spoken Language Understanding in Intelligent Agents

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2018-11-20 A propos

iliaschalkidis/ELMo-keras: Re-implementation of ELMo on Keras

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2018-11-14 A propos

Word Mover's Embedding: From Word2Vec to Document Embedding (2018)

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2018-11-10 A propos

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

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2018-11-05 A propos

[1710.06632] Towards a Seamless Integration of Word Senses into Downstream NLP Applications

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2018-10-09 A propos

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

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2018-10-08 A propos

Can Global Semantic Context Improve Neural Language Models? - Apple (2018)

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2018-09-27 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

AI Can Recognize Images, But Text Has Been Tricky—Until Now | WIRED

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2018-09-08 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

zalandoresearch/flair: A very simple framework for state-of-the-art NLP

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2018-08-24 A propos

Contextual String Embeddings for Sequence Labeling (2018)

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Word embeddings

2018-08-24 A propos

Learning to Understand Phrases by Embedding the Dictionary (2016)

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2018-08-23 A propos

what are the pros and cons of the various unsupervised word and sentence/ document embedding models? - Quora

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2018-08-19 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

How sampling works in Word2vec? Can someone please make me understand NCE and negative sampling? - Cross Validated

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2018-07-07 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

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2018-06-25 A propos

[1806.06259] Evaluation of sentence embeddings in downstream and linguistic probing tasks

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2018-06-19 A propos

Sanjeev Arora on "A theoretical approach to semantic representations" - YouTube (2016)

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2018-06-10 A propos

A Tri-Partite Neural Document Language Model for Semantic Information Retrieval (2018 - ESWC conference)

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2018-06-08 A propos

A Word Embedding Approach to Predicting the Compositionality of Multiword Expressions (2015)

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2018-06-08 A propos

The Current Best of Universal Word Embeddings and Sentence Embeddings (2018)

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2018-05-30 A propos

Deep Unordered Composition Rivals Syntactic Methods for Text Classification (2015)

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2018-05-29 A propos

A Theoretical Approach to Semantic Coding and Hashing | Simons Institute for the Theory of Computing (2016)

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2018-05-26 A propos

A simple spell checker built from word vectors – Ed Rushton – Medium

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2018-05-25 A propos

[1404.5367] Lexicon Infused Phrase Embeddings for Named Entity Resolution

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2018-05-22 A propos

[1706.04902] A Survey Of Cross-lingual Word Embedding Models

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2018-05-20 A propos

NLP: Any libraries/dictionaries out there for fixing common spelling errors? - Part 2 & Alumni - Deep Learning Course Forums

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2018-05-18 A propos

An Exploration of Embeddings for Generalized Phrases (2014)

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2018-05-12 A propos

Under the hood: Multilingual embeddings | Engineering Blog | Facebook Code

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2018-05-11 A propos

GitHub - Babylonpartners/fastText_multilingual: Multilingual word vectors

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2018-05-11 A propos

NLP using Word Vectors with Spacy - CLDSPN | Kaggle

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2018-05-11 A propos

Dict2vec : Learning Word Embeddings using Lexical Dictionaries

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2018-05-11 A propos

A Simple but Tough-to-Beat Baseline for Sentence Embeddings (2017)

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2018-05-10 A propos

Improving Word Embedding Compositionality using Lexicographic Definitions

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2018-05-10 A propos

That Makes Sense: Joint Sense Retrofitting from Contextual and Ontological Information

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post-processing method for generating low-dimensional sense embedding. Emploies the ontological and contextual information simultaneously.

(Poster at the Web Conf) [Github](https://github.com/y95847frank/Joint-Retrofitting)

Calcule des "sense embeddings", en partant de word embeddings pré-calculés (par ex avec word2vec), et de données de type lexicographiques (ex wordnet), en contraignant, pour un sens, la distance entre sense et word embedding.

Abstract:

> While recent word embedding models demonstrate their abilities to capture syntactic and semantic information, the demand for sense level embedding is getting higher. In this study, we propose a novel joint sense embedding learning model that retrofits the word representation into sense representation from contextual and ontological information. The experiments show the effectiveness and robustness of our model that outperforms previous approaches in four public available benchmark datasets.

> Given a trained word embedding and a lexical ontology that contains sense level relationships (e.g., synonym, hypernym, etc.), our model generates new sense vectors via constraining the distance between the sense vector and its word form vector, its sense neighbors and its contextual neighbors

[Influenced by](/doc/?uri=https%3A%2F%2Farxiv.org%2Fabs%2F1411.4166) (which post-processes and modifies word vectors to incorporate knowledge from semantic lexicons, while this creates new sense vectors)

2018-05-10 A propos

[1804.01486] Clinical Concept Embeddings Learned from Massive Sources of Multimodal Medical Data

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2018-04-14 A propos

GitHub - ijkilchenko/Fuzbal: Chrome extension: Gives Ctrl+F like find results which include non-exact (fuzzy) matches using string edit-distance and GloVe/Word2Vec. Also searches by regular expressions.

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2018-03-29 A propos

Semantic hashing using tags and topic modeling (2013)

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2018-03-22 A propos

[1803.05651] Word2Bits - Quantized Word Vectors

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2018-03-20 A propos

GitHub - anvaka/word2vec-graph: Exploring word2vec embeddings as a graph of nearest neighbors

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2018-03-12 A propos

Deep learning with word embeddings improves biomedical named entity recognition | Bioinformatics | Oxford Academic (2017)

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2018-03-05 A propos

[1411.4166] Retrofitting Word Vectors to Semantic Lexicons

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2018-02-25 A propos

ELMo: Deep contextualized word representations (2018)

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ELMo

2018-02-16 A propos

A gentle introduction to Doc2Vec – ScaleAbout – Medium

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Doc2Vec

2018-02-14 A propos

Explanation for Doc2Vec - Quora

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Doc2Vec

2018-02-14 A propos

[1710.04099] Wembedder: Wikidata entity embedding web service

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2018-02-13 A propos

Improving the Compositionality of Word Embeddings (2017)

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(MS thesis, a [paper at TheWebConf 2018](/doc/?uri=https%3A%2F%2Fdoi.org%2F10.1145%2F3178876.3186007))

> This thesis explores a method to find better encodings of meaning a computer can work with. We specifically want to combine encodings of word meanings in such a way that a good encoding of their joint meaning is created. The act of combining multiple representations of meaning into a new representation of meaning is called semantic composition.

Analysis of four word embeddings (Word2Vec, GloVe, fastText and Paragram)  in terms of their semantic compositionality. A method to tune these embeddings towards better compositionality, using a simple neural network architecture with definitions and lemmas from WordNet.

> Since dictionary definitions are semantically similar to their associated lemmas, they are the ideal candidate for our tuning method, as well as evaluating for compositionality. Our architecture allows for the embeddings to be composed using simple arithmetic operations, which makes these embeddings specifically suitable for production applications such as web search and data mining. We also explore more elaborate and involved compositional models, such as recurrent composition and convolutional composition.

2018-02-13 A propos

Modèle Neuronal de Recherche d'Information Augmenté par une Ressource Sémantique (2017)

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2018-02-12 A propos

[1412.6623] Word Representations via Gaussian Embedding

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2018-01-28 A propos

Evaluating the Impact of Word Embeddings on Similarity Scoring in Practical Information Retrieval (2017)

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> Transferring the success of word embeddings to Information Retrieval (IR) task is currently an active research topic. While embedding-based retrieval models could tackle the vocabulary mismatch problem by making use of the embedding’s inherent similarity between distinct words, most of them struggle to compete with the prevalent strong baselines such as TF-IDF and BM25.

Considering a practical ad-hoc IR task composed of two steps, matching and scoring, compares the performance of several techniques that leverage word embeddings in the retrieval models to compute the similarity between the query and the documents (namely word centroid similarity, paragraph vectors, Word Mover’s distance, as well as a novel inverse document frequency (IDF) re-weighted word centroid similarity).

> We confirm that word embeddings can be successfully employed in a practical information retrieval setting. The proposed cosine similarity of IDF re-weighted, aggregated word vectors is competitive to the TF-IDF baseline.

2018-01-28 A propos

RESEARCH TRACK: Web Content Analysis, Semantics and Knowledge

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[CFP](https://www2018.thewebconf.org/call-for-papers/research-tracks-cfp/web-content-analysis/)

> In previous years, ‘content analysis’ and ‘semantic and knowledge’ were in separate track. This year, we combined these tracks to emphasize the close relationship between these topics; **the use of content to curate knowledge and the use of knowledge to guide content analysis and intelligent usage**.

Some of the accepted papers:
### [Large-Scale Hierarchical Text Classification with Recursively Regularized Deep Graph-CNN](https://doi.org/10.1145/3178876.3186005)

[Hierarchical Text Classification](/tag/nlp_hierarchical_text_classification): Text classification to a hierarchical taxonomy of topics, using graph representation of text, and CNN over this graph

Renvoie à ce qui a été vu dans le tutorial "Graph-based Text Representations"

from the abstract:

> a graph-CNN based deep learning model to first convert texts to graph-of-words, and then use graph convolution operations to convolve the word graph. Graph-of-words representation of texts has the advantage of capturing non-consecutive and long-distance semantics. CNN models have the advantage of learning different level of semantics. To further leverage the hierarchy of labels, we regularize the deep architecture with the dependency among labels

Conversion of text to graph: potentially given a single document

### [Weakly-supervised Relation Extraction by Pattern-enhanced Embedding Learning](https://doi.org/10.1145/3178876.3186024 )

Extraction de relations de corpus de textes de façon semi-supervisée, dans un contexte où on a peu de données labellisées décrivant les relations.

Par exemple, des données labellisées indique que le texte "Beijing, capital of China" correspond à la relation entre entités : ("Beijing", "Capital Of", "China), et on voudrait pouvoir extraire les entités et relations pertinentes à partir de texte tel que "Paris, France's capital,..."

Le papier décrit une méthode qui combine deux modules, l'un basé sur l'extraction automatique de patterns (par ex "[Head], Capital Of [Tail]") et l'autre sur la "sémantique distributionnelle" (du type "word embeddings"). Ces deux modules collaborent, le premier permettant de créer des instances de relations augmentant la base de connaissance sur lequel entrainer le second, et le second aidant le premier à déterminer des patterns informatifs ("co-entrainement")

### [Scalable Instance Reconstruction in Knowledge Bases via Relatedness Affiliated Embedding](https://doi.org/10.1145/3178876.3186017)

Knowledge base completion problem: usually, it is formulated as a link prediction problem, but not here. A novel knowledge embedding model ("Joint Modelling and Learning of Relatedness and Embedding")

### [Improving Word Embedding Compositionality using Lexicographic Definitions](https://doi.org/10.1145/3178876.3186007)

comment obtenir les meilleures représentations de texte à partir de représentations de mots (word embeddings) ? L'auteur utilise des ressources lexicographiques (wordnet) pour ses tests : l'embedding obtenu pour la définition d'un mot est-il proche de celui du mot ?

Le papier s'appuie sur une [thèse du même auteur](https://esc.fnwi.uva.nl/thesis/centraal/files/f1554608041.pdf), claire et bien écrite.

### [CESI: Canonicalizing Open Knowledge Bases using Embeddings and Side Information](https://doi.org/10.1145/3178876.3186030)

Amélioration de l'extraction de triplets (nom phrase, property, nom phrase) à partir de texte en calculant des embeddings pour les "nom phrases" (~entités)

### [Short-Text Topic Modeling via Non-negative Matrix Factorization Enriched with Local Word-Context Correlations](https://doi.org/10.1145/3178876.3186009)

Topic modeling for short texts, leveraging the word-context semantic correlations in the training

### [Towards Annotating Relational Data on the Web with Language Models](https://doi.org/10.1145/3178876.3186029)

### A paper by [David Blei](/tag/david_blei): (Dynamic Embeddings for Language Evolution)

2018-01-27 A propos

Knowledge Graph and Text Jointly Embedding (2014)

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2018-01-05 A propos

Learning Concept-Driven Document Embeddings for Medical Information Search (2017)

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2018-01-03 A propos

Combining word and entity embeddings for entity linking (ESWC 2017)

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2017-12-30 A propos

[1712.09405] Advances in Pre-Training Distributed Word Representations

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2017-12-29 A propos

gensim/WMD_tutorial.ipynb

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2017-12-23 A propos

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

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2017-12-22 A propos

LDA2vec: Word Embeddings in Topic Models – Towards Data Science

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LDA2vec

2017-12-11 A propos

A Latent Variable Model Approach to PMI-basedWord Embeddings

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2017-12-07 A propos

(fastText) Euclidean distance instead of cosine-similarity?

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2017-12-07 A propos

machine learning - Text categorization: combining different kind of features - Data Science Stack Exchange

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2017-12-06 A propos

How can we get the vector of a paragraph? · Issue #26 · facebookresearch/fastText

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2017-11-25 A propos

How to use pre-trained word vectors from Facebook’s fastText

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2017-11-25 A propos

DS Toolbox - Topic Models - DS lore

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2017-11-21 A propos

Semantic Word Embeddings – Off the convex path

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2017-11-21 A propos

Gaussian LDA for Topic Models with Word Embeddings (2015)

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2017-11-21 A propos

Calculates Word Mover's Distance Insanely Fast

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2017-11-12 A propos

Word Mover’s Distance in Python

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Word Mover’s Distance

2017-11-12 A propos

Monolingual and Cross-Lingual Information Retrieval Models Based on (Bilingual) Word Embeddings (2015)

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2017-11-12 A propos

A Comparison of Vector-based Representations for Semantic Composition (Blacoe and Lapata - 2012)

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2017-11-12 A propos

Improving Distributional Similarity with Lessons Learned from Word Embeddings (O Levy - 2015)

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2017-11-11 A propos

Semantics with Dense Vectors

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2017-11-11 A propos

Dependency-Based Word Embeddings | Omer Levy

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2017-11-08 A propos

Word embeddings in 2017: Trends and future directions

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2017-11-06 A propos

Spell Checker using Word2vec | Kaggle

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2017-11-03 A propos

LSTM with word2vec embeddings | Kaggle

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2017-10-25 A propos

Using Gensim Word2Vec Embeddings in Keras | Ben Bolte's Blog

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2017-10-23 A propos

A Word2Vec Keras tutorial

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2017-10-23 A propos

Using pre-trained word embeddings in a Keras model

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2017-10-23 A propos

Towards a Seamless Integration of Word Senses into Downstream NLP Applications (2017)

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2017-10-21 A propos

[1710.04087] Word Translation Without Parallel Data

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2017-10-14 A propos

Enriching Word Embeddings Using Knowledge Graph for Semantic Tagging in Conversational Dialog Systems - Microsoft Research (2015)

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> new simple, yet effective approaches to
learn domain specific word embeddings.

## Intro

> Adapting word embeddings, such as jointly capturing
syntactic and semantic information, can further enrich semantic
word representations for several tasks, e.g., sentiment
analysis (Tang et al. 2014), named entity recognition
(Lebret, Legrand, and Collobert 2013), entity-relation extraction
(Weston et al. 2013), etc. (Yu and Dredze 2014)
has introduced a lightly supervised word embedding learning
extending word2vec. They incorporate prior information to the objective
function as a regularization term considering synonymy relations
between words from Wordnet (Fellbaum 1999).

> In this work, we go one step further and investigate if
enriching the word2vec word embeddings trained on unstructured/
unlabeled text with domain specific semantic relations
obtained from knowledge sources (e.g., knowledge
graphs, search query logs, etc.) can help to discover relation
aware word embeddings. Unlike earlier work, **we encode the
information about the relations between phrases, thereby,
entities and relation mentions are all embedded into a low dimensional
vector space**.

## Related work (Learning Word Embeddings with Priors)

- word2vec
- Relational Constrained Model (RTM) (Yu and Dredze 2014)
While CBOW learns lexical word embeddings from provided text, the RTM learns embeddings of words based on their similarity to other words provided by a knowledge resource (eg. wordnet)
- Joint model  (Yu and Dredze 2014)
combines CBOW and RTM through linear combination

2017-10-02 A propos

Named Entity Recognition using Word Embedding as a Feature (2016)

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