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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 3476–3500 of 4002 papers

TitleStatusHype
Different Contexts Lead to Different Word Embeddings—0
Building a Monolingual Parallel Corpus for Text Simplification Using Sentence Similarity Based on Alignment between Word Embeddings—0
Structured Generative Models of Continuous Features for Word Sense Induction—0
Dialogue Act Classification in Domain-Independent Conversations Using a Deep Recurrent Neural Network—0
WordForce: Visualizing Controversial Words in Debates—0
D-GloVe: A Feasible Least Squares Model for Estimating Word Embedding Densities—0
On the contribution of word embeddings to temporal relation classification—0
Weighted Neural Bag-of-n-grams Model: New Baselines for Text ClassificationCode0
Assessing the Corpus Size vs. Similarity Trade-off for Word Embeddings in Clinical NLP—0
CogALex-V Shared Task: GHHH - Detecting Semantic Relations via Word Embeddings—0
ASU: An Experimental Study on Applying Deep Learning in Twitter Named Entity Recognition.—0
Borrow a Little from your Rich Cousin: Using Embeddings and Polarities of English Words for Multilingual Sentiment Classification—0
Extracting Social Networks from Literary Text with Word Embedding Tools—0
A Deep Fusion Model for Domain Adaptation in Phrase-based MT—0
Local-Global Vectors to Improve Unigram Terminology Extraction—0
Identity-sensitive Word Embedding through Heterogeneous Networks—0
Geometry of CompositionalityCode0
Bidirectional LSTM-CRF for Clinical Concept ExtractionCode0
Training and Evaluating Multimodal Word Embeddings with Large-scale Web Annotated Images—0
Sampled Image Tagging and Retrieval Methods on User Generated Content—0
Visualizing Linguistic Shift—0
Word and Document Embeddings based on Neural Network Approaches—0
Zero-Shot Visual Question Answering—0
A Semi-supervised Framework for Image CaptioningCode0
Multi-view Recurrent Neural Acoustic Word Embeddings—0
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