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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 3276–3300 of 4002 papers

TitleStatusHype
End-to-End Neural Ad-hoc Ranking with Kernel PoolingCode0
A Mixture Model for Learning Multi-Sense Word Embeddings—0
A Survey Of Cross-lingual Word Embedding Models—0
Neural Domain Adaptation for Biomedical Question AnsweringCode0
Scientific document summarization via citation contextualization and scientific discourse—0
Query-by-Example Search with Discriminative Neural Acoustic Word EmbeddingsCode0
Context encoders as a simple but powerful extension of word2vecCode0
Insights into Analogy Completion from the Biomedical DomainCode0
Learning Structured Semantic Embeddings for Visual Recognition—0
Order embeddings and character-level convolutions for multimodal alignment—0
Wordsurf : un outil pour naviguer dans un espace de « Word Embeddings » (Wordsurf : a tool to surf in a ``word embeddings'' space)—0
Deep Learning for Hate Speech Detection in TweetsCode0
Learning to Compute Word Embeddings On the Fly—0
The Mixing method: low-rank coordinate descent for semidefinite programming with diagonal constraintsCode0
Does the Geometry of Word Embeddings Help Document Classification? A Case Study on Persistent Homology Based Representations—0
Character Composition Model with Convolutional Neural Networks for Dependency Parsing on Morphologically Rich LanguagesCode0
The Importance of Automatic Syntactic Features in Vietnamese Named Entity Recognition—0
ASR error management for improving spoken language understanding—0
Contextualizing Citations for Scientific Summarization using Word Embeddings and Domain Knowledge—0
Second-Order Word Embeddings from Nearest Neighbor Topological FeaturesCode0
Lightweight Efficient Multi-keyword Ranked Search over Encrypted Cloud Data using Dual Word Embeddings—0
Learning Semantic Relatedness From Human Feedback Using Metric Learning—0
Mixed Membership Word Embeddings for Computational Social Science—0
Utility of General and Specific Word Embeddings for Classifying Translational Stages of Research—0
Evaluating vector-space models of analogy—0
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