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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 3901–3950 of 4002 papers

TitleStatusHype
ECNU: Using Traditional Similarity Measurements and Word Embedding for Semantic Textual Similarity Estimation—0
A Simple Word Embedding Model for Lexical Substitution—0
Neural word embeddings with multiplicative feature interactions for tensor-based compositions—0
Neural context embeddings for automatic discovery of word senses—0
INESC-ID: A Regression Model for Large Scale Twitter Sentiment Lexicon Induction—0
Improved Relation Extraction with Feature-Rich Compositional Embedding ModelsCode0
Ontologically Grounded Multi-sense Representation Learning for Semantic Vector Space Models—0
Random Walks and Neural Network Language Models on Knowledge Bases—0
Deep Multilingual Correlation for Improved Word Embeddings—0
Morphological Word-Embeddings—0
Simple task-specific bilingual word embeddings—0
A Word Embedding Approach to Predicting the Compositionality of Multiword Expressions—0
Combining Word Embeddings and Feature Embeddings for Fine-grained Relation ExtractionCode0
Document-Level Machine Translation with Word Vector Models—0
Adapting word2vec to Named Entity Recognition—0
Word Embedding-based Antonym Detection using Thesauri and Distributional Information—0
Unsupervised Morphology Induction Using Word Embeddings—0
Unsupervised Most Frequent Sense Detection using Word Embeddings—0
Classifying Relations by Ranking with Convolutional Neural NetworksCode0
Learning Dictionaries for Named Entity Recognition using Minimal Supervision—0
Efficient Non-parametric Estimation of Multiple Embeddings per Word in Vector Space—0
Big Data Small Data, In Domain Out-of Domain, Known Word Unknown Word: The Impact of Word Representation on Sequence Labelling Tasks—0
Semi-supervised Convolutional Neural Networks for Text Categorization via Region Embedding—0
Automatic Noun Compound Interpretation using Deep Neural Networks and Word Embeddings—0
Unsupervised POS Induction with Word Embeddings—0
Text Segmentation based on Semantic Word EmbeddingsCode0
Task-Oriented Learning of Word Embeddings for Semantic Relation Classification—0
A Linear Dynamical System Model for Text—0
A Latent Variable Model Approach to PMI-based Word EmbeddingsCode0
Learning Composition Models for Phrase EmbeddingsCode0
A Sense-Topic Model for Word Sense Induction with Unsupervised Data Enrichment—0
Improving Distributional Similarity with Lessons Learned from Word Embeddings—0
Tailoring Word Embeddings for Bilexical Predictions: An Experimental Comparison—0
Efficient Exact Gradient Update for training Deep Networks with Very Large Sparse TargetsCode0
Rehabilitation of Count-based Models for Word Vector Representations—0
Merging Verb Senses of Hindi WordNet using Word Embeddings—0
The Visualization of Change in Word Meaning over Time using Temporal Word Embeddings—0
POLYGLOT-NER: Massive Multilingual Named Entity Recognition—0
Event Role Extraction using Domain-Relevant Word Representations—0
Fine-Grained Contextual Predictions for Hard Sentiment Words—0
Neural Networks Leverage Corpus-wide Information for Part-of-speech Tagging—0
A* CCG Parsing with a Supertag-factored Model—0
Word Semantic Representations using Bayesian Probabilistic Tensor Factorization—0
Using Mined Coreference Chains as a Resource for a Semantic Task—0
A Fast and Accurate Dependency Parser using Neural Networks—0
\#TagSpace: Semantic Embeddings from HashtagsCode0
The CMU Submission for the Shared Task on Language Identification in Code-Switched Data—0
Revisiting Embedding Features for Simple Semi-supervised Learning—0
Knowledge Graph and Text Jointly Embedding—0
Evaluating Neural Word Representations in Tensor-Based Compositional Settings—0
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