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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 401–410 of 4002 papers

TitleStatusHype
ASR error management for improving spoken language understanding—0
Assessing multiple word embeddings for named entity recognition of professions and occupations in health-related social media—0
Assessing Polyseme Sense Similarity through Co-predication Acceptability and Contextualised Embedding Distance—0
A multi-level approach for hierarchical Ticket Classification—0
Assessing State-of-the-Art Sentiment Models on State-of-the-Art Sentiment Datasets—0
Assessing the Corpus Size vs. Similarity Trade-off for Word Embeddings in Clinical NLP—0
A Multiplicative Model for Learning Distributed Text-Based Attribute Representations—0
Assessing the Unitary RNN as an End-to-End Compositional Model of Syntax—0
A Multi-Resolution Word Embedding for Document Retrieval from Large Unstructured Knowledge Bases—0
Automated Discovery of Mathematical Definitions in Text—0
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