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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 2426–2450 of 4002 papers

TitleStatusHype
The UWNLP system at SemEval-2018 Task 7: Neural Relation Extraction Model with Selectively Incorporated Concept Embeddings—0
The Visualization of Change in Word Meaning over Time using Temporal Word Embeddings—0
The Word Analogy Testing Caveat—0
ThisIsCompetition at SemEval-2019 Task 9: BERT is unstable for out-of-domain samples—0
Threshold-Based Retrieval and Textual Entailment Detection on Legal Bar Exam Questions—0
THU\_NGN at SemEval-2018 Task 10: Capturing Discriminative Attributes with MLP-CNN model—0
THU\_NGN at SemEval-2019 Task 12: Toponym Detection and Disambiguation on Scientific Papers—0
THU\_NGN at SemEval-2019 Task 3: Dialog Emotion Classification using Attentional LSTM-CNN—0
Time-Aware and Corpus-Specific Entity Relatedness—0
Times Are Changing: Investigating the Pace of Language Change in Diachronic Word Embeddings—0
TIME: Text and Image Mutual-Translation Adversarial Networks—0
Tiny Word Embeddings Using Globally Informed Reconstruction—0
TLDR at SemEval-2022 Task 1: Using Transformers to Learn Dictionaries and Representations—0
TNE: A Latent Model for Representation Learning on Networks—0
Tohoku at SemEval-2016 Task 6: Feature-based Model versus Convolutional Neural Network for Stance Detection—0
Token Level Identification of Multiword Expressions Using Contextual Information—0
Token-Level Metaphor Detection using Neural Networks—0
Too Many Claims to Fact-Check: Prioritizing Political Claims Based on Check-Worthiness—0
Topical Phrase Extraction from Clinical Reports by Incorporating both Local and Global Context—0
Topic-aware Contextualized Transformers—0
Topic-aware latent models for representation learning on networks—0
Topic Based Sentiment Analysis Using Deep Learning—0
Topic Modeling Using Distributed Word Embeddings—0
Topic Modeling with Contextualized Word Representation Clusters—0
Topic Modeling with Topological Data Analysis—0
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