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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 1726–1750 of 4002 papers

TitleStatusHype
Des repr\'esentations continues de mots pour l'analyse d'opinions en arabe: une \'etude qualitative (Word embeddings for Arabic sentiment analysis : a qualitative study)—0
HIN-RNN: A Graph Representation Learning Neural Network for Fraudster Group Detection With No Handcrafted Features—0
Analysis of Gender Bias in Social Perception and Judgement Using Chinese Word Embeddings—0
HIT-SCIR at MRP 2019: A Unified Pipeline for Meaning Representation Parsing via Efficient Training and Effective Encoding—0
An evaluation of Czech word embeddings—0
Igevorse at SemEval-2018 Task 10: Exploring an Impact of Word Embeddings Concatenation for Capturing Discriminative Attributes—0
Hostility Detection and Covid-19 Fake News Detection in Social Media—0
Contextual and Non-Contextual Word Embeddings: an in-depth Linguistic Investigation—0
A Survey on Word Meta-Embedding Learning—0
Contextual and Position-Aware Factorization Machines for Sentiment Classification—0
How COVID-19 Is Changing Our Language : Detecting Semantic Shift in Twitter Word Embeddings—0
How Cute is Pikachu? Gathering and Ranking Pokémon Properties from Data with Pokémon Word Embeddings—0
How does a Multilingual LM Handle Multiple Languages?—0
Contextual Document Embeddings—0
Asymmetric Proxy Loss for Multi-View Acoustic Word Embeddings—0
How Do Source-side Monolingual Word Embeddings Impact Neural Machine Translation?—0
Contextual Embeddings: When Are They Worth It?—0
IITK at the FinSim Task: Hypernym Detection in Financial Domain via Context-Free and Contextualized Word Embeddings—0
How much does a word weigh? Weighting word embeddings for word sense induction—0
How Much Does Tokenization Affect Neural Machine Translation?—0
How much do word embeddings encode about syntax?—0
How Robust Are Character-Based Word Embeddings in Tagging and MT Against Wrod Scramlbing or Randdm Nouse?—0
How Self-Attention Improves Rare Class Performance in a Question-Answering Dialogue Agent—0
IITPB at SemEval-2017 Task 5: Sentiment Prediction in Financial Text—0
Implicit Discourse Relation Detection via a Deep Architecture with Gated Relevance Network—0
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