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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 3926–3950 of 4002 papers

TitleStatusHype
Text Segmentation based on Semantic Word EmbeddingsCode0
Task-Oriented Learning of Word Embeddings for Semantic Relation Classification—0
A Linear Dynamical System Model for Text—0
A Latent Variable Model Approach to PMI-based Word EmbeddingsCode0
Learning Composition Models for Phrase EmbeddingsCode0
A Sense-Topic Model for Word Sense Induction with Unsupervised Data Enrichment—0
Improving Distributional Similarity with Lessons Learned from Word Embeddings—0
Tailoring Word Embeddings for Bilexical Predictions: An Experimental Comparison—0
Efficient Exact Gradient Update for training Deep Networks with Very Large Sparse TargetsCode0
Rehabilitation of Count-based Models for Word Vector Representations—0
Merging Verb Senses of Hindi WordNet using Word Embeddings—0
The Visualization of Change in Word Meaning over Time using Temporal Word Embeddings—0
POLYGLOT-NER: Massive Multilingual Named Entity Recognition—0
Event Role Extraction using Domain-Relevant Word Representations—0
Fine-Grained Contextual Predictions for Hard Sentiment Words—0
Neural Networks Leverage Corpus-wide Information for Part-of-speech Tagging—0
A* CCG Parsing with a Supertag-factored Model—0
Word Semantic Representations using Bayesian Probabilistic Tensor Factorization—0
Using Mined Coreference Chains as a Resource for a Semantic Task—0
A Fast and Accurate Dependency Parser using Neural Networks—0
\#TagSpace: Semantic Embeddings from HashtagsCode0
The CMU Submission for the Shared Task on Language Identification in Code-Switched Data—0
Revisiting Embedding Features for Simple Semi-supervised Learning—0
Knowledge Graph and Text Jointly Embedding—0
Evaluating Neural Word Representations in Tensor-Based Compositional Settings—0
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