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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 2926–2950 of 4002 papers

TitleStatusHype
Comparison of Paragram and GloVe Results for Similarity Benchmarks—0
Word2net: Deep Representations of Language—0
Detecting Cross-Lingual Plagiarism Using Simulated Word Embeddings—0
Corpus specificity in LSA and Word2vec: the role of out-of-domain documents—0
Generative Adversarial Nets for Multiple Text CorporaCode0
Finding People's Professions and Nationalities Using Distant Supervision - The FMI@SU "goosefoot" team at the WSDM Cup 2017 Triple Scoring Task—0
Unsupervised Word Mapping Using Structural Similarities in Monolingual Embeddings—0
Any-gram Kernels for Sentence Classification: A Sentiment Analysis Case Study—0
Beyond Word2Vec: Embedding Words and Phrases in Same Vector SpaceCode0
Conditional Generative Adversarial Networks for Emoji Synthesis with Word Embedding Manipulation—0
Social Emotion Mining Techniques for Facebook Posts Reaction PredictionCode0
AWE-CM Vectors: Augmenting Word Embeddings with a Clinical MetathesaurusCode0
Recognizing Plans by Learning Embeddings from Observed Action Distributions—0
Monolingual Embeddings for Low Resourced Neural Machine TranslationCode0
The Sentimental Value of Chinese Sub-Character Components—0
All-In-1 at IJCNLP-2017 Task 4: Short Text Classification with One Model for All Languages—0
SentiNLP at IJCNLP-2017 Task 4: Customer Feedback Analysis Using a Bi-LSTM-CNN Model—0
LDCCNLP at IJCNLP-2017 Task 2: Dimensional Sentiment Analysis for Chinese Phrases Using Machine Learning—0
YNU-HPCC at IJCNLP-2017 Task 5: Multi-choice Question Answering in Exams Using an Attention-based LSTM Model—0
MainiwayAI at IJCNLP-2017 Task 2: Ensembles of Deep Architectures for Valence-Arousal Prediction—0
Context Selection for Embedding ModelsCode0
Bingo at IJCNLP-2017 Task 4: Augmenting Data using Machine Translation for Cross-linguistic Customer Feedback Classification—0
Leveraging Linguistic Resources for Improving Neural Text Classification—0
Experiential, Distributional and Dependency-based Word Embeddings have Complementary Roles in Decoding Brain Activity—0
An Exploration of Word Embedding Initialization in Deep-Learning Tasks—0
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