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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 3026–3050 of 4002 papers

TitleStatusHype
An Empirical Study on the Fairness of Pre-trained Word Embeddings—0
An Enhanced Text Classification to Explore Health based Indian Government Policy Tweets—0
A Neural Local Coherence Model for Text Quality Assessment—0
A Neural Model for Compositional Word Embeddings and Sentence Processing—0
A Neural Virtual Anchor Synthesizer based on Seq2Seq and GAN Models—0
A Neurobiologically Motivated Analysis of Distributional Semantic Models—0
An evaluation of Czech word embeddings—0
A New Approach to Animacy Detection—0
Word Embedding based New Corpus for Low-resourced Language: Sindhi—0
An Experimental Study of Deep Neural Network Models for Vietnamese Multiple-Choice Reading Comprehension—0
An experimental study of the vision-bottleneck in VQA—0
An Explanatory Query-Based Framework for Exploring Academic Expertise—0
An exploration of the encoding of grammatical gender in word embeddings—0
An Exploration of Word Embedding Initialization in Deep-Learning Tasks—0
An Exploratory Analysis on the Explanatory Potential of Embedding-Based Measures of Semantic Transparency for Malay Word Recognition—0
An Exploratory Study on Temporally Evolving Discussion around Covid-19 using Diachronic Word Embeddings—0
An Exploratory Study on Utilising the Web of Linked Data for Product Data Mining—0
Angular-Based Word Meta-Embedding Learning—0
An Improved Crowdsourcing Based Evaluation Technique for Word Embedding Methods—0
An Improved Deep Learning Model for Word Embeddings Based Clustering for Large Text Datasets—0
An Improved Historical Embedding without Alignment—0
An Improved Neural Baseline for Temporal Relation Extraction—0
An Improved Single Step Non-autoregressive Transformer for Automatic Speech Recognition—0
An Interpretable Deep-Learning Framework for Predicting Hospital Readmissions From Electronic Health Records—0
An Intrinsic Nearest Neighbor Analysis of Neural Machine Translation Architectures—0
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