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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 1876–1900 of 4002 papers

TitleStatusHype
Definition Frames: Using Definitions for Hybrid Concept RepresentationsCode0
Follow the Leader: Documents on the Leading Edge of Semantic Change Get More CitationsCode0
Reverse Transfer Learning: Can Word Embeddings Trained for Different NLP Tasks Improve Neural Language Models?—0
Composing Knowledge Graph Embeddings via Word Embeddings—0
Distributed Training of Embeddings using Graph Analytics—0
Specializing Unsupervised Pretraining Models for Word-Level Semantic SimilarityCode0
Semantics-aware BERT for Language UnderstandingCode0
Cross-Lingual Dependency Parsing Using Code-Mixed TreeBank—0
Fusing Vector Space Models for Domain-Specific Applications—0
Examining Gender Bias in Languages with Grammatical GenderCode0
Differentiable Disentanglement Filter: an Application Agnostic Core Concept Discovery Probe—0
Empirical Study of Diachronic Word Embeddings for Scarce Data—0
Affect Enriched Word Embeddings for News Information Retrieval—0
Interpretable Word Embeddings via Informative Priors—0
Aspect Detection using Word and Char Embeddings with (Bi)LSTM and CRFCode0
On the Downstream Performance of Compressed Word EmbeddingsCode0
How Contextual are Contextualized Word Representations? Comparing the Geometry of BERT, ELMo, and GPT-2 EmbeddingsCode0
It's All in the Name: Mitigating Gender Bias with Name-Based Counterfactual Data Substitution—0
Rotate King to get Queen: Word Relationships as Orthogonal Transformations in Embedding Space—0
Toward Multilingual Identification of Online Registers—0
Political Stance in DanishCode0
Unsupervised Inference of Object Affordance from Text Corpora—0
Comparing the Performance of Feature Representations for the Categorization of the Easy-to-Read Variety vs Standard Language—0
Named-Entity Recognition for NorwegianCode0
Towards High Accuracy Named Entity Recognition for Icelandic—0
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