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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 3651–3700 of 4002 papers

TitleStatusHype
Enhancing Automatic Wordnet Construction Using Word Embeddings—0
Token-Level Metaphor Detection using Neural Networks—0
LIMSI-COT at SemEval-2016 Task 12: Temporal relation identification using a pipeline of classifiers—0
CU-NLP at SemEval-2016 Task 8: AMR Parsing using LSTM-based Recurrent Neural Networks—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
HHU at SemEval-2016 Task 1: Multiple Approaches to Measuring Semantic Textual Similarity—0
BIT at SemEval-2016 Task 1: Sentence Similarity Based on Alignments and Vector with the Weight of Information Content—0
thecerealkiller at SemEval-2016 Task 4: Deep Learning based System for Classifying Sentiment of Tweets on Two Point Scale—0
AUEB-ABSA at SemEval-2016 Task 5: Ensembles of Classifiers and Embeddings for Aspect Based Sentiment Analysis—0
Tweester at SemEval-2016 Task 4: Sentiment Analysis in Twitter Using Semantic-Affective Model Adaptation—0
KeLP at SemEval-2016 Task 3: Learning Semantic Relations between Questions and Answers—0
UMD-TTIC-UW at SemEval-2016 Task 1: Attention-Based Multi-Perspective Convolutional Neural Networks for Textual Similarity Measurement—0
Overfitting at SemEval-2016 Task 3: Detecting Semantically Similar Questions in Community Question Answering Forums with Word Embeddings—0
VectorWeavers at SemEval-2016 Task 10: From Incremental Meaning to Semantic Unit (phrase by phrase)—0
UNBNLP at SemEval-2016 Task 1: Semantic Textual Similarity: A Unified Framework for Semantic Processing and Evaluation—0
CUFE at SemEval-2016 Task 4: A Gated Recurrent Model for Sentiment Classification—0
ASOBEK at SemEval-2016 Task 1: Sentence Representation with Character N-gram Embeddings for Semantic Textual Similarity—0
UniMelb at SemEval-2016 Task 3: Identifying Similar Questions by combining a CNN with String Similarity Measures—0
UniPI at SemEval-2016 Task 4: Convolutional Neural Networks for Sentiment Classification—0
SwissCheese at SemEval-2016 Task 4: Sentiment Classification Using an Ensemble of Convolutional Neural Networks with Distant Supervision—0
Amrita\_CEN at SemEval-2016 Task 1: Semantic Relation from Word Embeddings in Higher Dimension—0
LIPN-IIMAS at SemEval-2016 Task 1: Random Forest Regression Experiments on Align-and-Differentiate and Word Embeddings penalizing strategies—0
INESC-ID at SemEval-2016 Task 4-A: Reducing the Problem of Out-of-Embedding Words—0
FBK HLT-MT at SemEval-2016 Task 1: Cross-lingual Semantic Similarity Measurement Using Quality Estimation Features and Compositional Bilingual Word Embeddings—0
UWB at SemEval-2016 Task 7: Novel Method for Automatic Sentiment Intensity Determination—0
YZU-NLP Team at SemEval-2016 Task 4: Ordinal Sentiment Classification Using a Recurrent Convolutional Network—0
AI-KU at SemEval-2016 Task 11: Word Embeddings and Substring Features for Complex Word Identification—0
Finki at SemEval-2016 Task 4: Deep Learning Architecture for Twitter Sentiment Analysis—0
UTA DLNLP at SemEval-2016 Task 1: Semantic Textual Similarity: A Unified Framework for Semantic Processing and Evaluation—0
WOLVESAAR at SemEval-2016 Task 1: Replicating the Success of Monolingual Word Alignment and Neural Embeddings for Semantic Textual Similarity—0
SimiHawk at SemEval-2016 Task 1: A Deep Ensemble System for Semantic Textual Similarity—0
Tohoku at SemEval-2016 Task 6: Feature-based Model versus Convolutional Neural Network for Stance Detection—0
aueb.twitter.sentiment at SemEval-2016 Task 4: A Weighted Ensemble of SVMs for Twitter Sentiment Analysis—0
GWU NLP at SemEval-2016 Shared Task 1: Matrix Factorization for Crosslingual STS—0
NUIG-UNLP at SemEval-2016 Task 1: Soft Alignment and Deep Learning for Semantic Textual Similarity—0
SENSEI-LIF at SemEval-2016 Task 4: Polarity embedding fusion for robust sentiment analysis—0
LyS at SemEval-2016 Task 4: Exploiting Neural Activation Values for Twitter Sentiment Classification and Quantification—0
ICL-HD at SemEval-2016 Task 10: Improving the Detection of Minimal Semantic Units and their Meanings with an Ontology and Word Embeddings—0
Right-truncatable Neural Word Embeddings—0
Analogy-based detection of morphological and semantic relations with word embeddings: what works and what doesn't.—0
K-Embeddings: Learning Conceptual Embeddings for Words using Context—0
Multimodal Semantic Learning from Child-Directed Input—0
Deconstructing Complex Search Tasks: a Bayesian Nonparametric Approach for Extracting Sub-tasks—0
Dependency Based Embeddings for Sentence Classification Tasks—0
Improved Neural Network-based Multi-label Classification with Better Initialization Leveraging Label Co-occurrence—0
Improve Chinese Word Embeddings by Exploiting Internal StructureCode0
Drop-out Conditional Random Fields for Twitter with Huge Mined Gazetteer—0
Bilingual Word Embeddings from Parallel and Non-parallel Corpora for Cross-Language Text Classification—0
Questioning Arbitrariness in Language: a Data-Driven Study of Conventional Iconicity—0
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