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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 3141–3150 of 4002 papers

TitleStatusHype
Learning Bilingual Projections of Embeddings for Vocabulary Expansion in Machine Translation—0
Tracing armed conflicts with diachronic word embedding models—0
Evaluating Feature Extraction Methods for Knowledge-based Biomedical Word Sense Disambiguation—0
BUCC 2017 Shared Task: a First Attempt Toward a Deep Learning Framework for Identifying Parallel Sentences in Comparable Corpora—0
Multilingual Semantic Parsing And Code-SwitchingCode0
An Artificial Language Evaluation of Distributional Semantic Models—0
Multi-Model and Crosslingual Dependency Analysis—0
Learning Word Representations with Regularization from Prior Knowledge—0
CLCL (Geneva) DINN Parser: a Neural Network Dependency Parser Ten Years Later—0
Parsing with Context Embeddings—0
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