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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 2876–2900 of 4002 papers

TitleStatusHype
Not just about size - A Study on the Role of Distributed Word Representations in the Analysis of Scientific Publications—0
Few-Shot Text Classification with Pre-Trained Word Embeddings and a Human in the LoopCode0
Clinical Concept Embeddings Learned from Massive Sources of Multimodal Medical DataCode0
Incorporating Word Embeddings into Open Directory Project based Large-scale Classification—0
Robust Cross-lingual Hypernymy Detection using Dependency ContextCode0
Interpretable and Globally Optimal Prediction for Textual Grounding using Image Concepts—0
The Geometry of Culture: Analyzing Meaning through Word EmbeddingsCode0
Near-lossless Binarization of Word EmbeddingsCode0
Word sense induction using word embeddings and community detection in complex networks—0
Learning Eligibility in Cancer Clinical Trials using Deep Neural NetworksCode0
UnibucKernel: A kernel-based learning method for complex word identification—0
Enhanced Word Representations for Bridging Anaphora Resolution—0
Hierarchical Learning of Cross-Language Mappings through Distributed Vector Representations for CodeCode0
Neural Fine-Grained Entity Type Classification with Hierarchy-Aware LossCode0
Improving Optimization for Models With Continuous Symmetry Breaking—0
The emergent algebraic structure of RNNs and embeddings in NLP—0
Query and Output: Generating Words by Querying Distributed Word Representations for Paraphrase GenerationCode0
Concatenated Power Mean Word Embeddings as Universal Cross-Lingual Sentence RepresentationsCode0
Understanding and Improving Multi-Sense Word Embeddings via Extended Robust Principal Component Analysis—0
Exploring Word Sense Disambiguation Abilities of Neural Machine Translation Systems (Non-archival Extended Abstract)—0
A Fast Deep Learning Model for Textual Relevance in Biomedical Information Retrieval—0
URLNet: Learning a URL Representation with Deep Learning for Malicious URL DetectionCode0
A Neurobiologically Motivated Analysis of Distributional Semantic Models—0
Semantic projection: recovering human knowledge of multiple, distinct object features from word embeddings—0
Disunited Nations? A Multiplex Network Approach to Detecting Preference Affinity Blocs using Texts and Votes—0
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