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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 3801–3825 of 4002 papers

TitleStatusHype
Sentence Compression by Deletion with LSTMs—0
Distributed Representations for Unsupervised Semantic Role Labeling—0
Semi-supervised Dependency Parsing using Bilexical Contextual Features from Auto-Parsed Data—0
Semi-Supervised Bootstrapping of Relationship Extractors with Distributional Semantics—0
Sarcastic or Not: Word Embeddings to Predict the Literal or Sarcastic Meaning of Words—0
Convolutional Sentence Kernel from Word Embeddings for Short Text Categorization—0
Translation Invariant Word Embeddings—0
Comparing Word Representations for Implicit Discourse Relation Classification—0
Reinforcing the Topic of Embeddings with Theta Pure Dependence for Text Classification—0
Bilingual Correspondence Recursive Autoencoder for Statistical Machine Translation—0
Pre-Computable Multi-Layer Neural Network Language Models—0
What's in an Embedding? Analyzing Word Embeddings through Multilingual Evaluation—0
Any-language frame-semantic parsing—0
A Model of Zero-Shot Learning of Spoken Language Understanding—0
Neural Networks for Open Domain Targeted SentimentCode0
Online Learning of Interpretable Word EmbeddingsCode0
Component-Enhanced Chinese Character Embeddings—0
Better Summarization Evaluation with Word Embeddings for ROUGECode0
Learning Meta-Embeddings by Using Ensembles of Embedding SetsCode0
Syntax-Aware Multi-Sense Word Embeddings for Deep Compositional Models of Meaning—0
Cross-Lingual Dependency Parsing with Universal Dependencies and Predicted PoS Labels—0
Document Embedding with Paragraph VectorsCode0
Reasoning about Linguistic Regularities in Word Embeddings using Matrix Manifolds—0
Clustering is Efficient for Approximate Maximum Inner Product Search—0
How to Generate a Good Word Embedding?Code0
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