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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 2051–2075 of 4002 papers

TitleStatusHype
Classification and Clustering of Arguments with Contextualized Word EmbeddingsCode0
Language Modelling Makes Sense: Propagating Representations through WordNet for Full-Coverage Word Sense DisambiguationCode1
Variational Sequential Labelers for Semi-Supervised LearningCode0
Exploiting Entity BIO Tag Embeddings and Multi-task Learning for Relation Extraction with Imbalanced Data—0
Learning Bilingual Word Embeddings Using Lexical Definitions—0
An Open-World Extension to Knowledge Graph Completion ModelsCode0
Considerations for the Interpretation of Bias Measures of Word Embeddings—0
Measuring Bias in Contextualized Word RepresentationsCode1
KaWAT: A Word Analogy Task Dataset for IndonesianCode0
A Structured Distributional Model of Sentence Meaning and Processing—0
Conceptor Debiasing of Word Representations Evaluated on WEAT—0
Semantic Change and Semantic Stability: Variation is Key—0
Character n-gram Embeddings to Improve RNN Language Models—0
Learning Predicates as Functions to Enable Few-shot Scene Graph Prediction—0
Putting words in context: LSTM language models and lexical ambiguityCode0
Analyzing the Limitations of Cross-lingual Word Embedding Mappings—0
A Systematic Comparison of English Noun Compound RepresentationsCode0
Word-level Speech Recognition with a Letter to Word Encoder—0
Modeling Noisiness to Recognize Named Entities using Multitask Neural Networks on Social Media—0
Probing for Semantic Classes: Diagnosing the Meaning Content of Word EmbeddingsCode0
Improving Low-Resource Cross-lingual Document Retrieval by Reranking with Deep Bilingual Representations—0
Word Embeddings for the Armenian Language: Intrinsic and Extrinsic EvaluationCode0
Shared-Private Bilingual Word Embeddings for Neural Machine Translation—0
Learning Word Embeddings with Domain AwarenessCode0
GCDT: A Global Context Enhanced Deep Transition Architecture for Sequence LabelingCode0
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