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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 551–575 of 4002 papers

TitleStatusHype
Building Web-Interfaces for Vector Semantic Models with the WebVectors Toolkit—0
Case Studies on using Natural Language Processing Techniques in Customer Relationship Management Software—0
ArbEngVec : Arabic-English Cross-Lingual Word Embedding Model—0
AraWEAT: Multidimensional Analysis of Biases in Arabic Word Embeddings—0
All-In-1 at IJCNLP-2017 Task 4: Short Text Classification with One Model for All Languages—0
A Rank-Based Similarity Metric for Word Embeddings—0
Arabic Textual Entailment with Word Embeddings—0
A Linguistically Informed Convolutional Neural Network—0
Adapting Neural Machine Translation with Parallel Synthetic Data—0
Arabic POS Tagging: Don't Abandon Feature Engineering Just Yet—0
Arabic aspect sentiment polarity classification using BERT—0
A Linear Dynamical System Model for Text—0
A Question Answering Approach for Emotion Cause Extraction—0
Adapted Sentiment Similarity Seed Words For French Tweets' Polarity Classification—0
Misinforming LLMs: vulnerabilities, challenges and opportunities—0
Brundlefly at SemEval-2016 Task 12: Recurrent Neural Networks vs. Joint Inference for Clinical Temporal Information Extraction—0
Alignment-free Cross-lingual Semantic Role Labeling—0
News and Load: A Quantitative Exploration of Natural Language Processing Applications for Forecasting Day-ahead Electricity System Demand—0
A Case Study to Reveal if an Area of Interest has a Trend in Ongoing Tweets Using Word and Sentence Embeddings—0
A Progressive Learning Approach to Chinese SRL Using Heterogeneous Data—0
A Process for Topic Modelling Via Word Embeddings—0
ADAPT at SemEval-2018 Task 9: Skip-Gram Word Embeddings for Unsupervised Hypernym Discovery in Specialised Corpora—0
Bridging the Modality Gap: Enhancing Channel Prediction with Semantically Aligned LLMs and Knowledge Distillation—0
A Probabilistic Model for Learning Multi-Prototype Word Embeddings—0
A Probabilistic Model for Joint Learning of Word Embeddings from Texts and Images—0
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