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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 3751–3800 of 4002 papers

TitleStatusHype
Massively Multilingual Word EmbeddingsCode0
A Dual Embedding Space Model for Document Ranking—0
Linear Algebraic Structure of Word Senses, with Applications to PolysemyCode0
Trans-gram, Fast Cross-lingual Word-embeddings—0
From Word Embeddings to Item RecommendationCode1
The Role of Context Types and Dimensionality in Learning Word Embeddings—0
Detecting Most Frequent Sense using Word Embeddings and BabelNet—0
Word Embeddings as Metric Recovery in Semantic Spaces—0
Efficient Structured Inference for Transition-Based Parsing with Neural Networks and Error StatesCode0
Learning Semantic Similarity for Very Short Texts—0
Analysis of Word Embeddings and Sequence Features for Clinical Information Extraction—0
Detection of Multiword Expressions for Hindi Language using Word Embeddings and WordNet-based Features—0
Using Word Embeddings for Bilingual Unsupervised WSD—0
Aspect-based Opinion Summarization with Convolutional Neural Networks—0
Named Entity Recognition with Bidirectional LSTM-CNNsCode0
On the Linear Algebraic Structure of Distributed Word Representations—0
Visual Word2Vec (vis-w2v): Learning Visually Grounded Word Embeddings Using Abstract ScenesCode0
sense2vec - A Fast and Accurate Method for Word Sense Disambiguation In Neural Word EmbeddingsCode0
Multilingual Relation Extraction using Compositional Universal SchemaCode0
Compressing Word Embeddings—0
Learning Articulated Motion Models from Visual and Lingual Signals—0
Learning the Dimensionality of Word Embeddings—0
An Empirical Study on Sentiment Classification of Chinese Review using Word Embedding—0
Controlled Experiments for Word EmbeddingsCode0
Mapping Unseen Words to Task-Trained Embedding Spaces—0
Deep convolutional acoustic word embeddings using word-pair side informationCode0
Bidirectional Long Short-Term Memory Networks for Relation Classification—0
Reducing Lexical Features in Parsing by Word Embeddings—0
Bilingual Distributed Word Representations from Document-Aligned Comparable Data—0
Word, graph and manifold embedding from Markov processes—0
Splitting Compounds by Semantic Analogy—0
Take and Took, Gaggle and Goose, Book and Read: Evaluating the Utility of Vector Differences for Lexical Relation LearningCode0
Encoding Prior Knowledge with Eigenword Embeddings—0
SHEF-NN: Translation Quality Estimation with Neural Networks—0
Improving evaluation and optimization of MT systems against MEANT—0
A Linguistically Informed Convolutional Neural Network—0
Using reading behavior to predict grammatical functions—0
Towards a Model of Prediction-based Syntactic Category Acquisition: First Steps with Word Embeddings—0
Exploring Word Embedding for Drug Name Recognition—0
Multi-Perspective Sentence Similarity Modeling with Convolutional Neural Networks—0
Exploiting Debate Portals for Semi-Supervised Argumentation Mining in User-Generated Web DiscourseCode0
Summarization Based on Embedding Distributions—0
Evaluation of Word Vector Representations by Subspace AlignmentCode0
Supervised Phrase Table Triangulation with Neural Word Embeddings for Low-Resource Languages—0
Evaluation methods for unsupervised word embeddings—0
Specializing Word Embeddings for Similarity or Relatedness—0
Syntactic Dependencies and Distributed Word Representations for Analogy Detection and Mining—0
Fine-grained Opinion Mining with Recurrent Neural Networks and Word Embeddings—0
Empty Category Detection using Path Features and Distributed Case Frames—0
How to Avoid Unwanted Pregnancies: Domain Adaptation using Neural Network Models—0
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