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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 2526–2550 of 4002 papers

TitleStatusHype
Affordance Extraction and Inference based on Semantic Role Labeling—0
Why is unsupervised alignment of English embeddings from different algorithms so hard?—0
Gromov-Wasserstein Alignment of Word Embedding Spaces—0
Beyond Weight Tying: Learning Joint Input-Output Embeddings for Neural Machine TranslationCode0
Skip-gram word embeddings in hyperbolic spaceCode0
Neural Cross-Lingual Named Entity Recognition with Minimal ResourcesCode0
Learning Gender-Neutral Word EmbeddingsCode0
Card-660: Cambridge Rare Word Dataset - a Reliable Benchmark for Infrequent Word Representation Models—0
A Quantum Many-body Wave Function Inspired Language Modeling ApproachCode0
Adapting Word Embeddings to New Languages with Morphological and Phonological Subword RepresentationsCode0
WiC: the Word-in-Context Dataset for Evaluating Context-Sensitive Meaning Representations—0
Learning Multilingual Word Embeddings in Latent Metric Space: A Geometric ApproachCode0
Predefined Sparseness in Recurrent Sequence ModelsCode0
An Investigation of the Interactions Between Pre-Trained Word Embeddings, Character Models and POS Tags in Dependency Parsing—0
Improving Cross-Lingual Word Embeddings by Meeting in the MiddleCode0
Unsupervised Multilingual Word EmbeddingsCode0
Dissecting Contextual Word Embeddings: Architecture and Representation—0
Generating Text through Adversarial Training using Skip-Thought VectorsCode0
Churn Intent Detection in Multilingual Chatbot Conversations and Social MediaCode0
Comparing CNN and LSTM character-level embeddings in BiLSTM-CRF models for chemical and disease named entity recognition—0
Mapping Text to Knowledge Graph Entities using Multi-Sense LSTMs—0
Reducing Gender Bias in Abusive Language Detection—0
The Influence of Down-Sampling Strategies on SVD Word Embedding Stability—0
SeVeN: Augmenting Word Embeddings with Unsupervised Relation VectorsCode0
Combining time-series and textual data for taxi demand prediction in event areas: a deep learning approach—0
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