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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 3851–3900 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
Unveiling the Dreams of Word Embeddings: Towards Language-Driven Image Generation—0
WordRank: Learning Word Embeddings via Robust RankingCode0
Modeling Order in Neural Word Embeddings at Scale—0
Relation Extraction: Perspective from Convolutional Neural Networks—0
Dependency Link Embeddings: Continuous Representations of Syntactic Substructures—0
Semantic Information Extraction for Improved Word Embeddings—0
Word Embeddings vs Word Types for Sequence Labeling: the Curious Case of CV Parsing—0
Neural word embeddings with multiplicative feature interactions for tensor-based compositions—0
DeepNL: a Deep Learning NLP pipeline—0
Neural context embeddings for automatic discovery of word senses—0
Distributional Representations of Words for Short Text Classification—0
Learning Distributed Representations for Multilingual Text Sequences—0
A Word-Embedding-based Sense Index for Regular Polysemy Representation—0
A Simple Word Embedding Model for Lexical Substitution—0
Short Text Clustering via Convolutional Neural NetworksCode1
INESC-ID: A Regression Model for Large Scale Twitter Sentiment Lexicon Induction—0
AZMAT: Sentence Similarity Using Associative Matrices—0
USAAR-WLV: Hypernym Generation with Deep Neural Nets—0
INESC-ID: Sentiment Analysis without Hand-Coded Features or Linguistic Resources using Embedding Subspaces—0
ExB Themis: Extensive Feature Extraction from Word Alignments for Semantic Textual Similarity—0
WarwickDCS: From Phrase-Based to Target-Specific Sentiment Recognition—0
DCU: Using Distributional Semantics and Domain Adaptation for the Semantic Textual Similarity SemEval-2015 Task 2—0
UNITN: Training Deep Convolutional Neural Network for Twitter Sentiment Classification—0
ECNU: Leveraging Word Embeddings to Boost Performance for Paraphrase in Twitter—0
Dependency-Based Semantic Role Labeling using Convolutional Neural Networks—0
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