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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 3901–3925 of 4002 papers

TitleStatusHype
ECNU: Using Traditional Similarity Measurements and Word Embedding for Semantic Textual Similarity Estimation—0
A Simple Word Embedding Model for Lexical Substitution—0
Neural word embeddings with multiplicative feature interactions for tensor-based compositions—0
Neural context embeddings for automatic discovery of word senses—0
INESC-ID: A Regression Model for Large Scale Twitter Sentiment Lexicon Induction—0
Improved Relation Extraction with Feature-Rich Compositional Embedding ModelsCode0
Ontologically Grounded Multi-sense Representation Learning for Semantic Vector Space Models—0
Random Walks and Neural Network Language Models on Knowledge Bases—0
Deep Multilingual Correlation for Improved Word Embeddings—0
Morphological Word-Embeddings—0
Simple task-specific bilingual word embeddings—0
A Word Embedding Approach to Predicting the Compositionality of Multiword Expressions—0
Combining Word Embeddings and Feature Embeddings for Fine-grained Relation ExtractionCode0
Document-Level Machine Translation with Word Vector Models—0
Adapting word2vec to Named Entity Recognition—0
Word Embedding-based Antonym Detection using Thesauri and Distributional Information—0
Unsupervised Morphology Induction Using Word Embeddings—0
Unsupervised Most Frequent Sense Detection using Word Embeddings—0
Classifying Relations by Ranking with Convolutional Neural NetworksCode0
Learning Dictionaries for Named Entity Recognition using Minimal Supervision—0
Efficient Non-parametric Estimation of Multiple Embeddings per Word in Vector Space—0
Big Data Small Data, In Domain Out-of Domain, Known Word Unknown Word: The Impact of Word Representation on Sequence Labelling Tasks—0
Semi-supervised Convolutional Neural Networks for Text Categorization via Region Embedding—0
Automatic Noun Compound Interpretation using Deep Neural Networks and Word Embeddings—0
Unsupervised POS Induction with Word Embeddings—0
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