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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 576–600 of 4002 papers

TitleStatusHype
A Probabilistic Framework for Learning Domain Specific Hierarchical Word Embeddings—0
A Primer on Word Embeddings: AI Techniques for Text Analysis in Social Work—0
Aligning Very Small Parallel Corpora Using Cross-Lingual Word Embeddings and a Monogamy Objective—0
Adaptation of Hierarchical Structured Models for Speech Act Recognition in Asynchronous Conversation—0
Positional Artefacts Propagate Through Masked Language Model Embeddings—0
Censorship of Online Encyclopedias: Implications for NLP Models—0
A Preliminary Study on a Conceptual Game Feature Generation and Recommendation System—0
A Precisely Xtreme-Multi Channel Hybrid Approach For Roman Urdu Sentiment Analysis—0
Aligning Opinions: Cross-Lingual Opinion Mining with Dependencies—0
Aligning Open IE Relations and KB Relations using a Siamese Network Based on Word Embedding—0
Apprentissage de plongements lexicaux par une approche r\'eseaux complexes (Complex networks based word embeddings)—0
Active Discriminative Text Representation Learning—0
Text2Node: a Cross-Domain System for Mapping Arbitrary Phrases to a Taxonomy—0
Apprentissage de plongements de mots sur des corpus en langue de sp\'ecialit\'e : une \'etude d'impact (Learning word embeddings on domain specific corpora : an impact study )—0
Apprentissage de plongements de mots dynamiques avec r\'egularisation de la d\'erive (Learning dynamic word embeddings with drift regularisation)—0
Action Assembly: Sparse Imitation Learning for Text Based Games with Combinatorial Action Spaces—0
Applying Word Embeddings to Measure Valence in Information Operations Targeting Journalists in Brazil—0
Applying Occam’s Razor to Transformer-Based Dependency Parsing: What Works, What Doesn’t, and What is Really Necessary—0
A Lexicalized Tree Kernel for Open Information Extraction—0
A Call for More Rigor in Unsupervised Cross-lingual Learning—0
Case Studies on using Natural Language Processing Techniques in Customer Relationship Management Software—0
Applying Multi-Sense Embeddings for German Verbs to Determine Semantic Relatedness and to Detect Non-Literal Language—0
Application of Clinical Concept Embeddings for Heart Failure Prediction in UK EHR data—0
A Latent Concept Topic Model for Robust Topic Inference Using Word Embeddings—0
Regionalized models for Spanish language variations based on Twitter—0
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