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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 1976–2000 of 4002 papers

TitleStatusHype
Automatic Detection of Incoherent Speech for Diagnosing Schizophrenia—0
It's All in the Name: Mitigating Gender Bias with Name-Based Counterfactual Data Substitution—0
IxaMed at PharmacoNER Challenge 2019—0
DCC-Uchile at SemEval-2020 Task 1: Temporal Referencing Word Embeddings—0
Jabberwocky Parsing: Dependency Parsing with Lexical Noise—0
Japanese Lexical Simplification for Non-Native Speakers—0
Japanese Word Readability Assessment using Word Embeddings—0
JCT at SemEval-2021 Task 1: Context-aware Representation for Lexical Complexity Prediction—0
JeSemE: Interleaving Semantics and Emotions in a Web Service for the Exploration of Language Change Phenomena—0
JeuxDeLiens: Word Embeddings and Path-Based Similarity for Entity Linking using the French JeuxDeMots Lexical Semantic Network—0
JHU System Description for the MADAR Arabic Dialect Identification Shared Task—0
Job Prediction: From Deep Neural Network Models to Applications—0
Joint Embeddings of Chinese Words, Characters, and Fine-grained Subcharacter Components—0
A Neural Model for Compositional Word Embeddings and Sentence Processing—0
Joint Learning from Labeled and Unlabeled Data for Information Retrieval—0
Joint learning of frequency and word embeddings for multilingual readability assessment—0
Joint Learning of Hierarchical Word Embeddings from a Corpus and a Taxonomy—0
Joint Learning of Sense and Word Embeddings—0
Joint Learning of Word and Label Embeddings for Sequence Labelling in Spoken Language Understanding—0
Jointly Learning to Embed and Predict with Multiple Languages—0
JOINTLY LEARNING TOPIC SPECIFIC WORD AND DOCUMENT EMBEDDING—0
Jointly Learning Word Embeddings and Latent Topics—0
Jointly modelling the evolution of social structure and language in online communities—0
Dependency Parsing for Urdu: Resources, Conversions and Learning—0
Dependency Link Embeddings: Continuous Representations of Syntactic Substructures—0
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