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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 2326–2350 of 4002 papers

TitleStatusHype
Symmetric Pattern Based Word Embeddings for Improved Word Similarity Prediction—0
Symmetric Patterns and Coordinations: Fast and Enhanced Representations of Verbs and Adjectives—0
Synergizing Unsupervised and Supervised Learning: A Hybrid Approach for Accurate Natural Language Task Modeling—0
Synonym Discovery with Etymology-based Word Embeddings—0
Syntactic and semantic classification of verb arguments using dependency-based and rich semantic features—0
Syntactic and Semantic Features For Code-Switching Factored Language Models—0
Syntactic Dependencies and Distributed Word Representations for Analogy Detection and Mining—0
Syntax-Aware Multi-Sense Word Embeddings for Deep Compositional Models of Meaning—0
Syntax Encoding with Application in Authorship Attribution—0
Syntax-Enhanced Neural Machine Translation with Syntax-Aware Word Representations—0
SyntaxFest 2019 Invited talk - Quantitative Computational Syntax: dependencies, intervention effects and word embeddings—0
Syntax Helps ELMo Understand Semantics: Is Syntax Still Relevant in a Deep Neural Architecture for SRL?—0
Syntax Representation in Word Embeddings and Neural Networks -- A Survey—0
Synthetic Data for English Lexical Normalization: How Close Can We Get to Manually Annotated Data?—0
Synthetic, yet natural: Properties of WordNet random walk corpora and the impact of rare words on embedding performance—0
Syntree2Vec - An algorithm to augment syntactic hierarchy into word embeddings—0
SZTE-NLP at SemEval-2017 Task 10: A High Precision Sequence Model for Keyphrase Extraction Utilizing Sparse Coding for Feature Generation—0
T\"ubingen-Oslo at SemEval-2018 Task 2: SVMs perform better than RNNs in Emoji Prediction—0
TableQnA: Answering List Intent Queries With Web Tables—0
Tackling COVID-19 Infodemic using Deep Learning—0
Tagging a Morphologically Complex Language Using an Averaged Perceptron Tagger: The Case of Icelandic—0
Tailoring Word Embeddings for Bilexical Predictions: An Experimental Comparison—0
TAJJEB at SemEval-2018 Task 2: Traditional Approaches Just Do the Job with Emoji Prediction—0
TakeLab at SemEval-2017 Task 4: Recent Deaths and the Power of Nostalgia in Sentiment Analysis in Twitter—0
TakeLab at SemEval-2017 Task 5: Linear aggregation of word embeddings for fine-grained sentiment analysis of financial news—0
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