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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 1901–1925 of 4002 papers

TitleStatusHype
Toward Multilingual Identification of Online Registers—0
Cross-Lingual Word Embeddings for Morphologically Rich Languages—0
A study of semantic augmentation of word embeddings for extractive summarization—0
SenZi: A Sentiment Analysis Lexicon for the Latinised Arabic (Arabizi)—0
Neural Feature Extraction for Contextual Emotion Detection—0
A Classification-Based Approach to Cognate Detection Combining Orthographic and Semantic Similarity Information—0
Sparse Victory -- A Large Scale Systematic Comparison of count-based and prediction-based vectorizers for text classificationCode0
Whom to Learn From? Graph- vs. Text-based Word Embeddings—0
Exploring Adequacy Errors in Neural Machine Translation with the Help of Cross-Language Aligned Word Embeddings—0
Tweaks and Tricks for Word Embedding Disruptions—0
Evaluation of Stacked Embeddings for Bulgarian on the Downstream Tasks POS and NERC—0
Classification of Micro-Texts Using Sub-Word Embeddings—0
Evaluating the Consistency of Word Embeddings from Small Data—0
Enhancing Unsupervised Sentence Similarity Methods with Deep Contextualised Word Representations—0
Speculation and Negation detection in French biomedical corpora—0
Multilingual Complex Word Identification: Convolutional Neural Networks with Morphological and Linguistic Features—0
Utilizing Pre-Trained Word Embeddings to Learn Classification Lexicons with Little Supervision—0
Leveraging Just a Few Keywords for Fine-Grained Aspect Detection Through Weakly Supervised Co-TrainingCode0
An Improved Neural Baseline for Temporal Relation Extraction—0
Adversarial Learning with Contextual Embeddings for Zero-resource Cross-lingual Classification and NER—0
Open Named Entity Modeling from Embedding Distribution—0
Named Entity Recognition Only from Word Embeddings—0
Single Training Dimension Selection for Word Embedding with PCA—0
Encoders Help You Disambiguate Word Senses in Neural Machine Translation—0
Adversarial Representation Learning for Text-to-Image Matching—0
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