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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 3651–3675 of 4002 papers

TitleStatusHype
Enhancing Automatic Wordnet Construction Using Word Embeddings—0
WOLVESAAR at SemEval-2016 Task 1: Replicating the Success of Monolingual Word Alignment and Neural Embeddings for Semantic Textual Similarity—0
HHU at SemEval-2016 Task 1: Multiple Approaches to Measuring Semantic Textual Similarity—0
English Resource Semantics—0
GWU NLP at SemEval-2016 Shared Task 1: Matrix Factorization for Crosslingual STS—0
Revisiting Supertagging and Parsing: How to Use Supertags in Transition-Based Parsing—0
Token-Level Metaphor Detection using Neural Networks—0
Tohoku at SemEval-2016 Task 6: Feature-based Model versus Convolutional Neural Network for Stance Detection—0
Supervised Metaphor Detection using Conditional Random Fields—0
Ten Pairs to Tag -- Multilingual POS Tagging via Coarse Mapping between Embeddings—0
UniMelb at SemEval-2016 Task 3: Identifying Similar Questions by combining a CNN with String Similarity Measures—0
SimiHawk at SemEval-2016 Task 1: A Deep Ensemble System for Semantic Textual Similarity—0
Overfitting at SemEval-2016 Task 3: Detecting Semantically Similar Questions in Community Question Answering Forums with Word Embeddings—0
thecerealkiller at SemEval-2016 Task 4: Deep Learning based System for Classifying Sentiment of Tweets on Two Point Scale—0
Improved Neural Network-based Multi-label Classification with Better Initialization Leveraging Label Co-occurrence—0
UNBNLP at SemEval-2016 Task 1: Semantic Textual Similarity: A Unified Framework for Semantic Processing and Evaluation—0
Sentiment Lexicon Creation using Continuous Latent Space and Neural Networks—0
LyS at SemEval-2016 Task 4: Exploiting Neural Activation Values for Twitter Sentiment Classification and Quantification—0
UMD-TTIC-UW at SemEval-2016 Task 1: Attention-Based Multi-Perspective Convolutional Neural Networks for Textual Similarity Measurement—0
CUFE at SemEval-2016 Task 4: A Gated Recurrent Model for Sentiment Classification—0
CU-NLP at SemEval-2016 Task 8: AMR Parsing using LSTM-based Recurrent Neural Networks—0
Questioning Arbitrariness in Language: a Data-Driven Study of Conventional Iconicity—0
Brundlefly at SemEval-2016 Task 12: Recurrent Neural Networks vs. Joint Inference for Clinical Temporal Information Extraction—0
USAAR at SemEval-2016 Task 13: Hyponym Endocentricity—0
SwissCheese at SemEval-2016 Task 4: Sentiment Classification Using an Ensemble of Convolutional Neural Networks with Distant Supervision—0
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