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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 2476–2500 of 4002 papers

TitleStatusHype
A Challenge Set and Methods for Noun-Verb Ambiguity—0
Syntax Encoding with Application in Authorship Attribution—0
Synthetic Data Made to Order: The Case of ParsingCode0
Phrase-level Self-Attention Networks for Universal Sentence Encoding—0
Cross-lingual Lexical Sememe PredictionCode0
Ternary Twitter Sentiment Classification with Distant Supervision and Sentiment-Specific Word Embeddings—0
Transfer and Multi-Task Learning for Noun--Noun Compound Interpretation—0
An Unsupervised System for Parallel Corpus Filtering—0
Part-of-Speech Tagging for Code-Switched, Transliterated Texts without Explicit Language Identification—0
Vectorial Semantic Spaces Do Not Encode Human Judgments of Intervention Similarity—0
EmoNLP at IEST 2018: An Ensemble of Deep Learning Models and Gradient Boosting Regression Tree for Implicit Emotion Prediction in Tweets—0
Neural Machine Translation of Logographic Language Using Sub-character Level Information—0
The Lazy Encoder: A Fine-Grained Analysis of the Role of Morphology in Neural Machine Translation—0
The LMU Munich Unsupervised Machine Translation Systems—0
The RWTH Aachen University English-German and German-English Unsupervised Neural Machine Translation Systems for WMT 2018—0
A Probabilistic Model for Joint Learning of Word Embeddings from Texts and Images—0
Deep Learning for Social Media Health Text Classification—0
NL-FIIT at IEST-2018: Emotion Recognition utilizing Neural Networks and Multi-level PreprocessingCode0
NORMA: Neighborhood Sensitive Maps for Multilingual Word Embeddings—0
Deep Pivot-Based Modeling for Cross-language Cross-domain Transfer with Minimal GuidanceCode0
Disambiguated skip-gram model—0
Parameter-free Sentence Embedding via Orthogonal BasisCode0
Consistency and Variation in Kernel Neural Ranking Model—0
BLISS in Non-Isometric Embedding Spaces—0
Semi-supervised Learning with Multi-Domain Sentiment Word Embeddings—0
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