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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 3201–3225 of 4002 papers

TitleStatusHype
LIA at SemEval-2017 Task 4: An Ensemble of Neural Networks for Sentiment Classification—0
Fermi at SemEval-2017 Task 7: Detection and Interpretation of Homographic puns in English Language—0
BB\_twtr at SemEval-2017 Task 4: Twitter Sentiment Analysis with CNNs and LSTMs—0
QLUT at SemEval-2017 Task 2: Word Similarity Based on Word Embedding and Knowledge Base—0
QLUT at SemEval-2017 Task 1: Semantic Textual Similarity Based on Word Embeddings—0
FA3L at SemEval-2017 Task 3: A ThRee Embeddings Recurrent Neural Network for Question Answering—0
LIPN-IIMAS at SemEval-2017 Task 1: Subword Embeddings, Attention Recurrent Neural Networks and Cross Word Alignment for Semantic Textual Similarity—0
What Analogies Reveal about Word Vectors and their Compositionality—0
Lump at SemEval-2017 Task 1: Towards an Interlingua Semantic Similarity—0
MayoNLP at SemEval 2017 Task 10: Word Embedding Distance Pattern for Keyphrase Classification in Scientific Publications—0
Skill2vec: Machine Learning Approach for Determining the Relevant Skills from Job DescriptionCode0
Analysis of Italian Word Embeddings—0
Temporal dynamics of semantic relations in word embeddings: an application to predicting armed conflict participants—0
From Image to Text Classification: A Novel Approach based on Clustering Word Embeddings—0
Improve Lexicon-based Word Embeddings By Word Sense Disambiguation—0
Cross-Lingual Induction and Transfer of Verb Classes Based on Word Vector Space Specialisation—0
Reconstruction of Word Embeddings from Sub-Word Parameters—0
Mimicking Word Embeddings using Subword RNNsCode0
Optimal Hyperparameters for Deep LSTM-Networks for Sequence Labeling TasksCode1
Toward Incorporation of Relevant Documents in word2vec—0
Visually Aligned Word Embeddings for Improving Zero-shot Learning—0
A Simple Language Model based on PMI Matrix Approximations—0
Automated Detection of Non-Relevant Posts on the Russian Imageboard "2ch": Importance of the Choice of Word RepresentationsCode0
Rotations and Interpretability of Word Embeddings: the Case of the Russian Language—0
Negative Sampling Improves Hypernymy Extraction Based on Projection LearningCode0
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