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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 2501–2550 of 4002 papers

TitleStatusHype
Transformer++—0
Vision Transformer: Vit and its Derivatives—0
Trans-gram, Fast Cross-lingual Word-embeddings—0
Transition-based Abstract Meaning Representation Parsing with Contextual Embeddings—0
Transition-based Semantic Dependency Parsing with Pointer Networks—0
Translating Dialectal Arabic as Low Resource Language using Word Embedding—0
Translating Knowledge Representations with Monolingual Word Embeddings: the Case of a Thesaurus on Corporate Non-Financial Reporting—0
Translation Invariant Word Embeddings—0
Triplet-Aware Scene Graph Embeddings—0
Triplètoile: Extraction of Knowledge from Microblogging Text—0
A Context-Sensitive Word Embedding Approach for The Detection of Troll Tweets—0
TS-HTFA: Advancing Time Series Forecasting via Hierarchical Text-Free Alignment with Large Language Models—0
TTI-COIN at SemEval-2017 Task 10: Investigating Embeddings for End-to-End Relation Extraction from Scientific Papers—0
TUDA-CCL at SemEval-2021 Task 1: Using Gradient-boosted Regression Tree Ensembles Trained on a Heterogeneous Feature Set for Predicting Lexical Complexity—0
TUE at SemEval-2020 Task 1: Detecting Semantic Change by Clustering Contextual Word Embeddings—0
T\"upa at SemEval-2019 Task1: (Almost) feature-free Semantic Parsing—0
Turku Neural Parser Pipeline: An End-to-End System for the CoNLL 2018 Shared Task—0
TurkuNLP: Delexicalized Pre-training of Word Embeddings for Dependency Parsing—0
Turning Distributional Thesauri into Word Vectors for Synonym Extraction and Expansion—0
Tweaks and Tricks for Word Embedding Disruptions—0
Tweester at SemEval-2016 Task 4: Sentiment Analysis in Twitter Using Semantic-Affective Model Adaptation—0
Tweester at SemEval-2017 Task 4: Fusion of Semantic-Affective and pairwise classification models for sentiment analysis in Twitter—0
Tweets Sentiment Analysis via Word Embeddings and Machine Learning Techniques—0
Tweety at SemEval-2018 Task 2: Predicting Emojis using Hierarchical Attention Neural Networks and Support Vector Machine—0
TwiSe at SemEval-2017 Task 4: Five-point Twitter Sentiment Classification and Quantification—0
Twitter Bot Detection Using Bidirectional Long Short-term Memory Neural Networks and Word Embeddings—0
Two Stages Approach for Tweet Engagement Prediction—0
UAlberta at SemEval-2020 Task 2: Using Translations to Predict Cross-Lingual Entailment—0
UDPipe 2.0 Prototype at CoNLL 2018 UD Shared Task—0
UINSUSKA-TiTech at SemEval-2017 Task 3: Exploiting Word Importance Levels for Similarity Features for CQA—0
UMD at SemEval-2018 Task 10: Can Word Embeddings Capture Discriminative Attributes?—0
UMD-TTIC-UW at SemEval-2016 Task 1: Attention-Based Multi-Perspective Convolutional Neural Networks for Textual Similarity Measurement—0
UMDuluth-CS8761 at SemEval-2018 Task 9: Hypernym Discovery using Hearst Patterns, Co-occurrence frequencies and Word Embeddings—0
UMDuluth-CS8761 at SemEval-2018 Task9: Hypernym Discovery using Hearst Patterns, Co-occurrence frequencies and Word Embeddings—0
UNAM at SemEval-2018 Task 10: Unsupervised Semantic Discriminative Attribute Identification in Neural Word Embedding Cones—0
UNBNLP at SemEval-2016 Task 1: Semantic Textual Similarity: A Unified Framework for Semantic Processing and Evaluation—0
UNBNLP at SemEval-2018 Task 10: Evaluating unsupervised approaches to capturing discriminative attributes—0
UnClE: Explicitly Leveraging Semantic Similarity to Reduce the Parameters of Word Embeddings—0
Undecimated Wavelet Transform for Word Embedded Semantic Marginal Autoencoder in Security improvement and Denoising different Languages—0
Understanding and Improving Multi-Sense Word Embeddings via Extended Robust Principal Component Analysis—0
Understanding Neural Machine Translation by Simplification: The Case of Encoder-free Models—0
Understanding the Stability of Medical Concept Embeddings—0
Understanding the Source of Semantic Regularities in Word Embeddings—0
Understanding Undesirable Word Embedding Associations—0
UnibucKernel: A kernel-based learning method for complex word identification—0
Uniform Discretized Integrated Gradients: An effective attribution based method for explaining large language models—0
Unifying Bayesian Inference and Vector Space Models for Improved Decipherment—0
UniMelb at SemEval-2016 Task 3: Identifying Similar Questions by combining a CNN with String Similarity Measures—0
UniMelb at SemEval-2018 Task 12: Generative Implication using LSTMs, Siamese Networks and Semantic Representations with Synonym Fuzzing—0
UniPI at SemEval-2016 Task 4: Convolutional Neural Networks for Sentiment Classification—0
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