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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 3521–3530 of 4002 papers

TitleStatusHype
Compressing Neural Language Models by Sparse Word RepresentationsCode0
Language Models with Pre-Trained (GloVe) Word EmbeddingsCode0
A Dynamic Window Neural Network for CCG Supertagging—0
Neural-based Noise Filtering from Word EmbeddingsCode0
Comparative study of LSA vs Word2vec embeddings in small corpora: a case study in dreams database—0
Neural Structural Correspondence Learning for Domain AdaptationCode0
Chinese Event Extraction Using DeepNeural Network with Word Embedding—0
Are Word Embedding-based Features Useful for Sarcasm Detection?—0
Sentence Segmentation in Narrative Transcripts from Neuropsychological Tests using Recurrent Convolutional Neural Networks—0
基於相依詞向量的剖析結果重估與排序(N-best Parse Rescoring Based on Dependency-Based Word Embeddings)—0
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