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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 3651–3660 of 4002 papers

TitleStatusHype
Enhancing Automatic Wordnet Construction Using Word Embeddings—0
Token-Level Metaphor Detection using Neural Networks—0
LIMSI-COT at SemEval-2016 Task 12: Temporal relation identification using a pipeline of classifiers—0
CU-NLP at SemEval-2016 Task 8: AMR Parsing using LSTM-based Recurrent Neural Networks—0
Brundlefly at SemEval-2016 Task 12: Recurrent Neural Networks vs. Joint Inference for Clinical Temporal Information Extraction—0
USAAR at SemEval-2016 Task 13: Hyponym Endocentricity—0
HHU at SemEval-2016 Task 1: Multiple Approaches to Measuring Semantic Textual Similarity—0
BIT at SemEval-2016 Task 1: Sentence Similarity Based on Alignments and Vector with the Weight of Information Content—0
thecerealkiller at SemEval-2016 Task 4: Deep Learning based System for Classifying Sentiment of Tweets on Two Point Scale—0
AUEB-ABSA at SemEval-2016 Task 5: Ensembles of Classifiers and Embeddings for Aspect Based Sentiment Analysis—0
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