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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 3851–3875 of 4002 papers

TitleStatusHype
Learning Continuous Word Embedding with Metadata for Question Retrieval in Community Question Answering—0
Learning Word Representations from Scarce and Noisy Data with Embedding Subspaces—0
Question Answering over Freebase with Multi-Column Convolutional Neural Networks—0
Describing Images using Inferred Visual Dependency Representations—0
Learning Semantic Word Embeddings based on Ordinal Knowledge Constraints—0
Learning Tag Embeddings and Tag-specific Composition Functions in Recursive Neural Network—0
Transferring Coreference Resolvers with Posterior Regularization—0
Semantic Clustering and Convolutional Neural Network for Short Text Categorization—0
A Multitask Objective to Inject Lexical Contrast into Distributional Semantics—0
SensEmbed: Learning Sense Embeddings for Word and Relational Similarity—0
An analysis of the user occupational class through Twitter content—0
A Lexicalized Tree Kernel for Open Information Extraction—0
Gaussian LDA for Topic Models with Word EmbeddingsCode0
Unifying Bayesian Inference and Vector Space Models for Improved Decipherment—0
Learning Bilingual Sentiment Word Embeddings for Cross-language Sentiment Classification—0
Model-based Word Embeddings from Decompositions of Count Matrices—0
Deep Unordered Composition Rivals Syntactic Methods for Text ClassificationCode0
Aligning Opinions: Cross-Lingual Opinion Mining with Dependencies—0
Semantic Representations for Domain Adaptation: A Case Study on the Tree Kernel-based Method for Relation Extraction—0
Predicting Polarities of Tweets by Composing Word Embeddings with Long Short-Term Memory—0
Leverage Financial News to Predict Stock Price Movements Using Word Embeddings and Deep Neural Networks—0
Distilling Word Embeddings: An Encoding Approach—0
Leveraging Word Embeddings for Spoken Document Summarization—0
Learning language through picturesCode0
From Paraphrase Database to Compositional Paraphrase Model and BackCode0
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