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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 2701–2750 of 4002 papers

TitleStatusHype
ECNU at SemEval-2018 Task 10: Evaluating Simple but Effective Features on Machine Learning Methods for Semantic Difference Detection—0
MIT-MEDG at SemEval-2018 Task 7: Semantic Relation Classification via Convolution Neural Network—0
Peperomia at SemEval-2018 Task 2: Vector Similarity Based Approach for Emoji Prediction—0
Predicting Word Embeddings Variability—0
UNAM at SemEval-2018 Task 10: Unsupervised Semantic Discriminative Attribute Identification in Neural Word Embedding Cones—0
ClaiRE at SemEval-2018 Task 7: Classification of Relations using Embeddings—0
Igevorse at SemEval-2018 Task 10: Exploring an Impact of Word Embeddings Concatenation for Capturing Discriminative Attributes—0
INAOE-UPV at SemEval-2018 Task 3: An Ensemble Approach for Irony Detection in Twitter—0
NTU NLP Lab System at SemEval-2018 Task 10: Verifying Semantic Differences by Integrating Distributional Information and Expert Knowledge—0
The UWNLP system at SemEval-2018 Task 7: Neural Relation Extraction Model with Selectively Incorporated Concept Embeddings—0
UniMelb at SemEval-2018 Task 12: Generative Implication using LSTMs, Siamese Networks and Semantic Representations with Synonym Fuzzing—0
TAJJEB at SemEval-2018 Task 2: Traditional Approaches Just Do the Job with Emoji Prediction—0
THU\_NGN at SemEval-2018 Task 10: Capturing Discriminative Attributes with MLP-CNN model—0
ABDN at SemEval-2018 Task 10: Recognising Discriminative Attributes using Context Embeddings and WordNet—0
LIS at SemEval-2018 Task 2: Mixing Word Embeddings and Bag of Features for Multilingual Emoji Prediction—0
LightRel at SemEval-2018 Task 7: Lightweight and Fast Relation Classification—0
GHH at SemEval-2018 Task 10: Discovering Discriminative Attributes in Distributional Semantics—0
TeamDL at SemEval-2018 Task 8: Cybersecurity Text Analysis using Convolutional Neural Network and Conditional Random Fields—0
Talla at SemEval-2018 Task 7: Hybrid Loss Optimization for Relation Classification using Convolutional Neural Networks—0
Tweety at SemEval-2018 Task 2: Predicting Emojis using Hierarchical Attention Neural Networks and Support Vector Machine—0
NEUROSENT-PDI at SemEval-2018 Task 7: Discovering Textual Relations With a Neural Network Model—0
NEUROSENT-PDI at SemEval-2018 Task 3: Understanding Irony in Social Networks Through a Multi-Domain Sentiment Model—0
TakeLab at SemEval-2018 Task12: Argument Reasoning Comprehension with Skip-Thought Vectors—0
ADAPT at SemEval-2018 Task 9: Skip-Gram Word Embeddings for Unsupervised Hypernym Discovery in Specialised Corpora—0
Random Decision Syntax Trees at SemEval-2018 Task 3: LSTMs and Sentiment Scores for Irony Detection—0
Towards Qualitative Word Embeddings Evaluation: Measuring Neighbors Variation—0
attr2vec: Jointly Learning Word and Contextual Attribute Embeddings with Factorization Machines—0
Evaluating bilingual word embeddings on the long tailCode0
T\"ubingen-Oslo at SemEval-2018 Task 2: SVMs perform better than RNNs in Emoji Prediction—0
Solving Data Sparsity for Aspect Based Sentiment Analysis Using Cross-Linguality and Multi-Linguality—0
NEUROSENT-PDI at SemEval-2018 Task 1: Leveraging a Multi-Domain Sentiment Model for Inferring Polarity in Micro-blog Text—0
EMA at SemEval-2018 Task 1: Emotion Mining for Arabic—0
Igbo Diacritic Restoration using Embedding Models—0
DMCB at SemEval-2018 Task 1: Transfer Learning of Sentiment Classification Using Group LSTM for Emotion Intensity prediction—0
DL Team at SemEval-2018 Task 1: Tweet Affect Detection using Sentiment Lexicons and Embeddings—0
The Word Analogy Testing Caveat—0
Discriminating between Lexico-Semantic Relations with the Specialization Tensor ModelCode0
Directional Skip-Gram: Explicitly Distinguishing Left and Right Context for Word Embeddings—0
Inducing a Lexicon of Abusive Words – a Feature-Based ApproachCode0
DeepMiner at SemEval-2018 Task 1: Emotion Intensity Recognition Using Deep Representation Learning—0
Key2Vec: Automatic Ranked Keyphrase Extraction from Scientific Articles using Phrase Embeddings—0
Multimodal Frame Identification with Multilingual Evaluation—0
Context Sensitive Neural Lemmatization with Lematus—0
Pay-Per-Request Deployment of Neural Network Models Using Serverless Architectures—0
Stacking with Auxiliary Features for Visual Question Answering—0
Unsupervised Induction of Linguistic Categories with Records of Reading, Speaking, and Writing—0
Specialising Word Vectors for Lexical EntailmentCode0
Learning Word Embeddings for Data Sparse and Sentiment Rich Data Sets—0
Learning Word Embeddings for Low-Resource Languages by PU Learning—0
Querying Word Embeddings for Similarity and Relatedness—0
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