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Word embedding

In natural language processing, a word embedding is a representation of a word. The embedding is used in text analysis. Typically, the representation is a real-valued vector that encodes the meaning of the word in such a way that the words that are closer in the vector space are expected to be similar in meaning. Word embeddings can be obtained using…

History, Development and history of the approach & Polysemy and homonymy

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Explore the main themes, entities and connections around Word embedding. Start with the topic map, then use the sections below for research and deeper semantic analysis.

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Development and history of the approach

Distributional semanticsFeature spaceJohn Rupert FirthVector space modelCurse of dimensionality
21 related topics

Polysemy and homonymy

PolysemyHomonymyClub sandwichClubhouseGolf club
14 related topics

Software

Tomáš MikolovGloVeGensimDeeplearning4jPrincipal Component Analysis
9 related topics

For biological sequences: BioVectors

DNARNAProteinsBioinformaticsProteomics
6 related topics

Topics to explore

Browse the full topic structure. Each item opens a new analysis centered on that subject.

Overview

Development and history of the approach

Polysemy and homonymy

For biological sequences: BioVectors

Game design

Sentence embeddings

Software

Ethical implications

Advanced semantic analysis

Deeper signals for content research, entity SEO and topical coverage. The plain-language headings explain what each technical view is useful for.

Map overview Semantic statistics

Word embedding

Nodes79
Edges78
Triples77
Avg. degree1.97
Density0.025316
Components1

How this topic connects Entity context

See the strongest relationship patterns around the current topic before diving into the raw triples.

Word embedding

Top relations

related to Polysemy and homonymy · 18
Word embedding → BabelNet, Based, Combining, ConceptNet, For, Historically, In, Most, Most Suitable Sense Annotation, MSSA, MSSG, Multi-Sense Skip-Gram, NLP, Non-Parametric Multi-Sense Skip-Gram, NP-MSSG, Once, The, WordNet
related to Software · 16
Word embedding → AllenNLP's ELMo, BERT, Deeplearning4j, Flair, Gensim, GN-GloVe, Indra, PCA, Principal Component Analysis, SNE, Software, Stanford University's GloVe, T-Distributed Stochastic Neighbour Embedding, Tomáš Mikolov's Word2vec, UMAP, Uniform
related to Ethical implications · 12
Word embedding → Bolukbasi, Computer Programmer, Debiasing Word Embeddings, For, Furthermore, Google News, Homemaker, Jieyu Zhao, Man, Research, Woman, Word
related to For biological sequences: BioVectors · 11
Word embedding → Asgari, BioVec, BioVectors, DNA, GeneVec, Mofrad, Named, ProtVec, RNA, The, Word
related to history · 7
Word embedding → Bengio, In, John Rupert Firth, Neural, Reducing, Such, The
related to Game design · 4
Word embedding → Cook, Rabii, The, Word
related to Examples of application · 2
Word embedding → For, Sketch Engine
is a · 1
Word embedding → representation of a word

Important terminology Word statistics

Use these terms to understand the vocabulary surrounding the topic, not as a checklist for keyword stuffing.

Important terminology

word embeddings words embedding representation used space models vectors using language approach semantic multi-sense vector analysis word2vec data learning dimensionality

Entity relationships Subject–Predicate–Object triples

SubjectPredicateObjectConfidenceSrc
Word embeddingis arepresentation of a word0.90text
syntactic parsinginstance ofhave been shown to boost the performance in NLP tasks0.80text
sentiment analysisinstance ofhave been shown to boost the performance in NLP tasks0.80text
singular value decomposition then led to the introduction of latent semantic analysis in the late 1980sinstance ofReducing the number of dimensions using linear algebraic methods0.80text
the random indexing approach for collecting word co-occurrence contextsinstance ofReducing the number of dimensions using linear algebraic methods0.80text
ELMoinstance ofcontextually-meaningful embeddings0.80text
BERT have been developedinstance ofcontextually-meaningful embeddings0.80text
Word embeddingrelated to Ethical implicationsWord0.60section
Word embeddingrelated to Ethical implicationsBolukbasi0.60section
Word embeddingrelated to Ethical implicationsMan0.60section
Word embeddingrelated to Ethical implicationsComputer Programmer0.60section
Word embeddingrelated to Ethical implicationsWoman0.60section

Related concept clusters Concept neighborhoods

These clusters group vocabulary that occurs around closely connected concepts in the source material.

    Connections between topic areas Semantic bridges

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    Min side: 3
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