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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 2851–2875 of 4002 papers

TitleStatusHype
NTUA-SLP at SemEval-2018 Task 2: Predicting Emojis using RNNs with Context-aware AttentionCode0
NTUA-SLP at SemEval-2018 Task 3: Tracking Ironic Tweets using Ensembles of Word and Character Level Attentive RNNsCode0
NTUA-SLP at SemEval-2018 Task 1: Predicting Affective Content in Tweets with Deep Attentive RNNs and Transfer LearningCode0
When and Why are Pre-trained Word Embeddings Useful for Neural Machine Translation?Code0
A Deeper Look into Dependency-Based Word Embeddings—0
Frustratingly Easy Meta-Embedding -- Computing Meta-Embeddings by Averaging Source Word EmbeddingsCode0
Amobee at SemEval-2018 Task 1: GRU Neural Network with a CNN Attention Mechanism for Sentiment Classification—0
Exploiting Task-Oriented Resources to Learn Word Embeddings for Clinical Abbreviation Expansion—0
Evaluating Word Embedding Hyper-Parameters for Similarity and Analogy Tasks—0
Word2Vec applied to Recommendation: Hyperparameters MatterCode0
Few-Shot Text Classification with Pre-Trained Word Embeddings and a Human in the LoopCode0
Not just about size - A Study on the Role of Distributed Word Representations in the Analysis of Scientific Publications—0
Clinical Concept Embeddings Learned from Massive Sources of Multimodal Medical DataCode0
Incorporating Word Embeddings into Open Directory Project based Large-scale Classification—0
Robust Cross-lingual Hypernymy Detection using Dependency ContextCode0
Interpretable and Globally Optimal Prediction for Textual Grounding using Image Concepts—0
Universal Sentence EncoderCode1
The Geometry of Culture: Analyzing Meaning through Word EmbeddingsCode0
Near-lossless Binarization of Word EmbeddingsCode0
Speech2Vec: A Sequence-to-Sequence Framework for Learning Word Embeddings from SpeechCode1
Learning Eligibility in Cancer Clinical Trials using Deep Neural NetworksCode0
Word sense induction using word embeddings and community detection in complex networks—0
UnibucKernel: A kernel-based learning method for complex word identification—0
Hierarchical Learning of Cross-Language Mappings through Distributed Vector Representations for CodeCode0
Enhanced Word Representations for Bridging Anaphora Resolution—0
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