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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 1771–1780 of 4002 papers

TitleStatusHype
A Causal Inference Method for Reducing Gender Bias in Word Embedding RelationsCode0
Visual Summarization of Scholarly Videos using Word Embeddings and Keyphrase Extraction—0
Causally Denoise Word Embeddings Using Half-Sibling RegressionCode0
Enhancing Out-Of-Domain Utterance Detection with Data Augmentation Based on Word Embeddings—0
Anaphora Resolution in Dialogue Systems for South Asian Languages—0
Topical Phrase Extraction from Clinical Reports by Incorporating both Local and Global Context—0
Multilingual Culture-Independent Word Analogy Datasets—0
Empirical Autopsy of Deep Video Captioning Frameworks—0
SemanticZ at SemEval-2016 Task 3: Ranking Relevant Answers in Community Question Answering Using Semantic Similarity Based on Fine-tuned Word EmbeddingsCode0
Error Analysis for Vietnamese Named Entity Recognition on Deep Neural Network Models—0
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