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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 3976–4000 of 4002 papers

TitleStatusHype
Bilingually-constrained Phrase Embeddings for Machine Translation—0
Improving Lexical Embeddings with Semantic KnowledgeCode0
Use of unsupervised word classes for entity recognition: Application to the detection of disorders in clinical reports—0
Lexicon Infused Phrase Embeddings for Named Entity Resolution—0
Extractive Summarization using Continuous Vector Space Models—0
Improving Vector Space Word Representations Using Multilingual Correlation—0
Word Embeddings through Hellinger PCA—0
word2vec Explained: deriving Mikolov et al.'s negative-sampling word-embedding methodCode0
Learning Multilingual Word Representations using a Bag-of-Words Autoencoder—0
Improved CCG Parsing with Semi-supervised Supertagging—0
Word Emdeddings through Hellinger PCA—0
Learning word embeddings efficiently with noise-contrastive estimation—0
The AI-KU System at the SPMRL 2013 Shared Task : Unsupervised Features for Dependency ParsingCode0
Bilingual Word Embeddings for Phrase-Based Machine Translation—0
Re-embedding words—0
Aggregating Continuous Word Embeddings for Information Retrieval—0
Better Word Representations with Recursive Neural Networks for Morphology—0
A Structured Distributional Semantic Model for Event Co-reference—0
A Structured Distributional Semantic Model : Integrating Structure with Semantics—0
Polyglot: Distributed Word Representations for Multilingual NLP—0
Compound Embedding Features for Semi-supervised Learning—0
Tagging a Morphologically Complex Language Using an Averaged Perceptron Tagger: The Case of Icelandic—0
The Expressive Power of Word Embeddings—0
Learning Effective and Interpretable Semantic Models using Non-Negative Sparse Embedding—0
Improving Word Representations via Global Context and Multiple Word Prototypes—0
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