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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 2576–2600 of 4002 papers

TitleStatusHype
Exploring word embeddings and phonological similarity for the unsupervised correction of language learner errors—0
Transferred Embeddings for Igbo Similarity, Analogy, and Diacritic Restoration Tasks—0
Aggression Identification and Multi Lingual Word Embeddings—0
Veyn at PARSEME Shared Task 2018: Recurrent Neural Networks for VMWE IdentificationCode0
Automatically Linking Lexical Resources with Word Sense Embedding Models—0
Mumpitz at PARSEME Shared Task 2018: A Bidirectional LSTM for the Identification of Verbal Multiword Expressions—0
Textual Aggression Detection through Deep Learning—0
TRAC-1 Shared Task on Aggression Identification: IIT(ISM)@COLING'18—0
Aggressive Language Identification Using Word Embeddings and Sentiment FeaturesCode0
Word-Embedding based Content Features for Automated Oral Proficiency Scoring—0
Sub-label dependencies for Neural Morphological Tagging -- The Joint Submission of University of Colorado and University of Helsinki for VarDial 2018—0
Gender Bias in Neural Natural Language ProcessingCode0
Clustering Prominent People and Organizations in Topic-Specific Text Corpora—0
Resource-Size matters: Improving Neural Named Entity Recognition with Optimized Large CorporaCode0
Differentiable Perturb-and-Parse: Semi-Supervised Parsing with a Structured Variational Autoencoder—0
Cross-lingual Argumentation Mining: Machine Translation (and a bit of Projection) is All You Need!Code0
Deep Dialog Act Recognition using Multiple Token, Segment, and Context Information Representations—0
Imparting Interpretability to Word Embeddings while Preserving Semantic StructureCode0
Evaluating Word Embeddings in Multi-label Classification Using Fine-grained Name TypingCode0
Clinical Text Classification with Rule-based Features and Knowledge-guided Convolutional Neural Networks—0
Tracking the Evolution of Words with Time-reflective Text Representations—0
Predicting Concreteness and Imageability of Words Within and Across Languages via Word EmbeddingsCode0
Towards Better UD Parsing: Deep Contextualized Word Embeddings, Ensemble, and Treebank ConcatenationCode0
Latent Semantic Analysis Approach for Document Summarization Based on Word Embeddings—0
A Review of Different Word Embeddings for Sentiment Classification using Deep LearningCode0
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