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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 3226–3250 of 4002 papers

TitleStatusHype
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
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
Fast Amortized Inference and Learning in Log-linear Models with Randomly Perturbed Nearest Neighbor Search—0
Detecting Policy Preferences and Dynamics in the UN General Debate with Neural Word Embeddings—0
Weakly Supervised Cross-Lingual Named Entity Recognition via Effective Annotation and Representation Projection—0
Efficient Vector Representation for Documents through CorruptionCode0
On the Role of Text Preprocessing in Neural Network Architectures: An Evaluation Study on Text Categorization and Sentiment AnalysisCode0
A Simple Approach to Learn Polysemous Word EmbeddingsCode0
Visually Grounded Word Embeddings and Richer Visual Features for Improving Multimodal Neural Machine Translation—0
DAG-based Long Short-Term Memory for Neural Word Segmentation—0
Efficient Extraction of Pseudo-Parallel Sentences from Raw Monolingual Data Using Word Embeddings—0
Improving Implicit Discourse Relation Recognition with Discourse-specific Word Embeddings—0
Sentence Alignment Methods for Improving Text Simplification Systems—0
Improved Word Representation Learning with SememesCode0
Semantic Word Clusters Using Signed Spectral Clustering—0
Information-Theory Interpretation of the Skip-Gram Negative-Sampling Objective Function—0
Adversarial Training for Unsupervised Bilingual Lexicon Induction—0
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