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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 3151–3160 of 4002 papers

TitleStatusHype
From Raw Text to Universal Dependencies - Look, No Tags!—0
A non-DNN Feature Engineering Approach to Dependency Parsing -- FBAML at CoNLL 2017 Shared Task—0
Universal Joint Morph-Syntactic Processing: The Open University of Israel's Submission to The CoNLL 2017 Shared Task—0
A System for Multilingual Dependency Parsing based on Bidirectional LSTM Feature Representations—0
Modeling Context Words as Regions: An Ordinal Regression Approach to Word Embedding—0
Cross-language Learning with Adversarial Neural Networks—0
A Semi-universal Pipelined Approach to the CoNLL 2017 UD Shared Task—0
TurkuNLP: Delexicalized Pre-training of Word Embeddings for Dependency Parsing—0
Learning Stock Market Sentiment Lexicon and Sentiment-Oriented Word Vector from StockTwits—0
PurdueNLP at SemEval-2017 Task 1: Predicting Semantic Textual Similarity with Paraphrase and Event Embeddings—0
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