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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 1451–1475 of 4002 papers

TitleStatusHype
Multimodal Learning for Cardiovascular Risk Prediction using EHR Data—0
Query Focused Multi-document Summarisation of Biomedical TextsCode0
Query Focused Multi-document Summarisation of Biomedical Texts: Macquarie Universiy and the Australian National University at BioASQ8bCode0
Contextualized moral inference—0
Simple Unsupervised Similarity-Based Aspect ExtractionCode0
Two Stages Approach for Tweet Engagement Prediction—0
Predicting Helpfulness of Online Reviews—0
An Experimental Study of Deep Neural Network Models for Vietnamese Multiple-Choice Reading Comprehension—0
A Survey of Active Learning for Text Classification using Deep Neural Networks—0
MICE: Mining Idioms with Contextual EmbeddingsCode0
Context Reinforced Neural Topic Modeling over Short TextsCode0
A Neural Generative Model for Joint Learning Topics and Topic-Specific Word EmbeddingsCode0
An exploration of the encoding of grammatical gender in word embeddings—0
Deep Learning based Topic Analysis on Financial Emerging Event Tweets—0
Combining Representations For Effective Citation ClassificationCode0
Unsupervised Deep Cross-modality Spectral Hashing—0
Word embedding and neural network on grammatical gender -- A case study of Swedish—0
Effect of Text Processing Steps on Twitter Sentiment Classification using Word Embedding—0
Word Embeddings: Stability and Semantic Change—0
IITK at the FinSim Task: Hypernym Detection in Financial Domain via Context-Free and Contextualized Word Embeddings—0
Predicting Job-Hopping Motive of Candidates Using Answers to Open-ended Interview Questions—0
Better Early than Late: Fusing Topics with Word Embeddings for Neural Question Paraphrase Identification—0
Morphological Skip-Gram: Using morphological knowledge to improve word representation—0
On a Novel Application of Wasserstein-Procrustes for Unsupervised Cross-Lingual LearningCode0
An Enhanced Text Classification to Explore Health based Indian Government Policy Tweets—0
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