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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 2126–2150 of 4002 papers

TitleStatusHype
STUFIIT at SemEval-2019 Task 5: Multilingual Hate Speech Detection on Twitter with MUSE and ELMo Embeddings—0
Subword-based Compact Reconstruction of Word EmbeddingsCode0
SWOW-8500: Word Association task for Intrinsic Evaluation of Word Embeddings—0
Ensemble Methods to Distinguish Mainland and Taiwan Chinese—0
SWAP at SemEval-2019 Task 3: Emotion detection in conversations through Tweets, CNN and LSTM deep neural networksCode0
Stance Classification, Outcome Prediction, and Impact Assessment: NLP Tasks for Studying Group Decision-Making—0
Word Embedding-Based Automatic MT Evaluation Metric using Word Position Information—0
Using time series and natural language processing to identify viral moments in the 2016 U.S. Presidential Debate—0
Beyond Context: A New Perspective for Word Embeddings—0
Transfer Learning in Natural Language Processing—0
GL at SemEval-2019 Task 5: Identifying hateful tweets with a deep learning approach.—0
Medical Word Embeddings for Spanish: Development and Evaluation—0
Suicide Risk Assessment on Social Media: USI-UPF at the CLPsych 2019 Shared Task—0
An Analysis of Deep Contextual Word Embeddings and Neural Architectures for Toponym Mention Detection in Scientific Publications—0
Multilingual segmentation based on neural networks and pre-trained word embeddings—0
Naive Bayes and BiLSTM Ensemble for Discriminating between Mainland and Taiwan Variation of Mandarin Chinese—0
Measuring and Modeling Language Change—0
Learning Bilingual Sentiment-Specific Word Embeddings without Cross-lingual Supervision—0
GSI-UPM at SemEval-2019 Task 5: Semantic Similarity and Word Embeddings for Multilingual Detection of Hate Speech Against Immigrants and Women on Twitter—0
GWU NLP Lab at SemEval-2019 Task 3 : EmoContext: Effectiveness ofContextual Information in Models for Emotion Detection inSentence-level at Multi-genre Corpus—0
SINAI-DL at SemEval-2019 Task 5: Recurrent networks and data augmentation by paraphrasing—0
Text Similarity Estimation Based on Word Embeddings and Matrix Norms for Targeted Marketing—0
Atalaya at SemEval 2019 Task 5: Robust Embeddings for Tweet Classification—0
JU\_ETCE\_17\_21 at SemEval-2019 Task 6: Efficient Machine Learning and Neural Network Approaches for Identifying and Categorizing Offensive Language in TweetsCode0
T\"upa at SemEval-2019 Task1: (Almost) feature-free Semantic Parsing—0
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