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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 3601–3650 of 4002 papers

TitleStatusHype
On the Role of Seed Lexicons in Learning Bilingual Word Embeddings—0
Semantics-Driven Recognition of Collocations Using Word Embeddings—0
Implicit Discourse Relation Detection via a Deep Architecture with Gated Relevance Network—0
Character-based Neural Machine Translation—0
Morphological Smoothing and Extrapolation of Word Embeddings—0
Neural Networks For Negation Scope Detection—0
Recognizing Salient Entities in Shopping Queries—0
Learning Word Meta-Embeddings—0
Jointly Learning to Embed and Predict with Multiple Languages—0
On Approximately Searching for Similar Word Embeddings—0
Multi-Modal Representations for Improved Bilingual Lexicon Learning—0
Effects of Creativity and Cluster Tightness on Short Text Clustering Performance—0
Deep Neural Networks for Syntactic Parsing of Morphologically Rich Languages—0
Visual Relationship Detection with Language Priors—0
Cseq2seq: Cyclic Sequence-to-Sequence Learning—0
Man is to Computer Programmer as Woman is to Homemaker? Debiasing Word EmbeddingsCode0
An Empirical Evaluation of doc2vec with Practical Insights into Document Embedding GenerationCode0
Language classification from bilingual word embedding graphs—0
Enriching Word Vectors with Subword InformationCode0
Mapping distributional to model-theoretic semantic spaces: a baselineCode0
The Benefits of Word Embeddings Features for Active Learning in Clinical Information Extraction—0
Actionable and Political Text Classification using Word Embeddings and LSTM—0
Representation learning for very short texts using weighted word embedding aggregationCode0
Evaluating Unsupervised Dutch Word Embeddings as a Linguistic ResourceCode0
Learning Crosslingual Word Embeddings without Bilingual CorporaCode0
Exact gradient updates in time independent of output size for the spherical loss family—0
Using Word Embeddings for Automatic Query Expansion—0
Modelling User Preferences using Word Embeddings for Context-Aware Venue Recommendation—0
Toward Word Embedding for Personalized Information Retrieval—0
Using Word Embeddings in Twitter Election Classification—0
Neural Morphological Tagging from Characters for Morphologically Rich Languages—0
An empirical study on large scale text classification with skip-gram embeddings—0
Quantifying and Reducing Stereotypes in Word Embeddings—0
Siamese CBOW: Optimizing Word Embeddings for Sentence RepresentationsCode0
Learning Word Sense Embeddings from Word Sense Definitions—0
Active Discriminative Text Representation Learning—0
PSDVec: a Toolbox for Incremental and Scalable Word Embedding—0
Sentence Similarity Measures for Fine-Grained Estimation of Topical Relevance in Learner Essays—0
Inducing Domain-Specific Sentiment Lexicons from Unlabeled CorporaCode0
A Joint Model for Word Embedding and Word Morphology—0
Multilingual Visual Sentiment Concept Matching—0
Automated Image Captioning for Rapid Prototyping and Resource Constrained Environments—0
Brundlefly at SemEval-2016 Task 12: Recurrent Neural Networks vs. Joint Inference for Clinical Temporal Information Extraction—0
Revisiting Supertagging and Parsing: How to Use Supertags in Transition-Based Parsing—0
Supervised Metaphor Detection using Conditional Random Fields—0
Self-Reflective Sentiment Analysis—0
Learning Cross-lingual Representations with Matrix Factorization—0
Fashioning Data - A Social Media Perspective on Fast Fashion Brands—0
Sentiment Lexicon Creation using Continuous Latent Space and Neural Networks—0
Automatic Triage of Mental Health Forum Posts—0
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