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Word Embeddings

Word embedding is the collective name for a set of language modeling and feature learning techniques in natural language processing (NLP) where words or phrases from the vocabulary are mapped to vectors of real numbers.

Techniques for learning word embeddings can include Word2Vec, GloVe, and other neural network-based approaches that train on an NLP task such as language modeling or document classification.

( Image credit: Dynamic Word Embedding for Evolving Semantic Discovery )

Papers

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Showing 1976–2000 of 4002 papers

TitleStatusHype
Investigating Sub-Word Embedding Strategies for the Morphologically Rich and Free Phrase-Order Hungarian—0
Improving Word Embeddings Using Kernel PCA—0
Relating Word Embedding Gender Biases to Gender Gaps: A Cross-Cultural Analysis—0
Give It a Shot: Few-shot Learning to Normalize ADR Mentions in Social Media Posts—0
Composing Noun Phrase Vector Representations—0
What does Neural Bring? Analysing Improvements in Morphosyntactic Annotation and Lemmatisation of Slovenian, Croatian and Serbian—0
The Role of Protected Class Word Lists in Bias Identification of Contextualized Word Representations—0
Exploring the Use of Lexicons to aid Deep Learning towards the Detection of Abusive Language—0
Equalizing Gender Bias in Neural Machine Translation with Word Embeddings Techniques—0
Learning Word Embeddings without Context Vectors—0
NLP@UNED at SMM4H 2019: Neural Networks Applied to Automatic Classifications of Adverse Effects Mentions in Tweets—0
A Platform Agnostic Dual-Strand Hate Speech Detector—0
MoRTy: Unsupervised Learning of Task-specialized Word Embeddings by AutoencodingCode0
Effective Dimensionality Reduction for Word EmbeddingsCode0
Measuring Gender Bias in Word Embeddings across Domains and Discovering New Gender Bias Word CategoriesCode0
A Hierarchically-Labeled Portuguese Hate Speech DatasetCode0
Detection of Adverse Drug Reaction in Tweets Using a Combination of Heterogeneous Word Embeddings—0
SenseFitting: Sense Level Semantic Specialization of Word Embeddings for Word Sense Disambiguation—0
One-to-X analogical reasoning on word embeddings: a case for diachronic armed conflict prediction from news textsCode0
Hybrid Code Networks using a convolutional neural network as an input layer achieves higher turn accuracy—0
Representation Degeneration Problem in Training Natural Language Generation ModelsCode0
LINSPECTOR WEB: A Multilingual Probing Suite for Word RepresentationsCode0
Distributional Analysis of Polysemous Function Words—0
Bilingual Lexicon Induction through Unsupervised Machine TranslationCode0
Learning dynamic word embeddings with drift regularisation—0
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