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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 2576–2600 of 4002 papers

TitleStatusHype
A Review on Deep Learning Techniques Applied to Answer Selection—0
A LSTM Approach with Sub-Word Embeddings for Mongolian Phrase Break Prediction—0
Sub-label dependencies for Neural Morphological Tagging -- The Joint Submission of University of Colorado and University of Helsinki for VarDial 2018—0
TRAC-1 Shared Task on Aggression Identification: IIT(ISM)@COLING'18—0
Learning Diachronic Analogies to Analyze Concept ChangeCode0
A New Approach to Animacy Detection—0
Transition-based Neural RST Parsing with Implicit Syntax FeaturesCode0
Modeling with Recurrent Neural Networks for Open Vocabulary Slots—0
From Text to Lexicon: Bridging the Gap between Word Embeddings and Lexical ResourcesCode0
Textual Aggression Detection through Deep Learning—0
Identifying Aggression and Toxicity in Comments using Capsule Network—0
Encoding Sentiment Information into Word Vectors for Sentiment Analysis—0
Joint Learning from Labeled and Unlabeled Data for Information Retrieval—0
Toward Better Loanword Identification in Uyghur Using Cross-lingual Word Embeddings—0
Transferred Embeddings for Igbo Similarity, Analogy, and Diacritic Restoration Tasks—0
What's in Your Embedding, And How It Predicts Task Performance—0
JeSemE: Interleaving Semantics and Emotions in a Web Service for the Exploration of Language Change Phenomena—0
Using Word Embeddings for Unsupervised Acronym Disambiguation—0
Veyn at PARSEME Shared Task 2018: Recurrent Neural Networks for VMWE IdentificationCode0
Automatically Linking Lexical Resources with Word Sense Embedding Models—0
Recognizing Humour using Word Associations and Humour Anchor Extraction—0
Aggressive Language Identification Using Word Embeddings and Sentiment FeaturesCode0
Learning Word Meta-Embeddings by AutoencodingCode0
RuSentiment: An Enriched Sentiment Analysis Dataset for Social Media in Russian—0
Improving Named Entity Recognition by Jointly Learning to Disambiguate Morphological TagsCode0
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