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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 3271–3280 of 4002 papers

TitleStatusHype
Bilingual Word Embeddings with Bucketed CNN for Parallel Sentence Extraction—0
Bi-LSTM Neural Networks for Chinese Grammatical Error Diagnosis—0
Binary and Multitask Classification Model for Dutch Anaphora Resolution: Die/Dat Prediction—0
Binary Encoded Word Mover’s Distance—0
Bingo at IJCNLP-2017 Task 4: Augmenting Data using Machine Translation for Cross-linguistic Customer Feedback Classification—0
BioAMA: Towards an End to End BioMedical Question Answering System—0
Bio-inspired Structure Identification in Language Embeddings—0
Biomedical Event Extraction Using Convolutional Neural Networks and Dependency Parsing—0
Biomedical Question Answering via Weighted Neural Network Passage Retrieval—0
BioReddit: Word Embeddings for User-Generated Biomedical NLP—0
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