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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 1826–1850 of 4002 papers

TitleStatusHype
Investigating the Effectiveness of Representations Based on Word-Embeddings in Active Learning for Labelling Text DatasetsCode0
DialectGram: Detecting Dialectal Variation at Multiple Geographic ResolutionsCode0
Complex networks based word embeddings—0
Extracting UMLS Concepts from Medical Text Using General and Domain-Specific Deep Learning Models—0
Improving Word Embedding Factorization for Compression Using Distilled Nonlinear Neural Decomposition—0
Utilizing Word Embeddings based Features for Phylogenetic Tree Generation of Sanskrit Texts—0
Language-Agnostic Visual-Semantic EmbeddingsCode0
Improved Word Sense Disambiguation Using Pre-Trained Contextualized Word RepresentationsCode0
Specializing Word Embeddings (for Parsing) by Information BottleneckCode0
Additional Shared Decoder on Siamese Multi-view Encoders for Learning Acoustic Word Embeddings—0
Bad Form: Comparing Context-Based and Form-Based Few-Shot Learning in Distributional Semantic Models—0
A Pilot Study for Chinese SQL Semantic ParsingCode2
Learning Category Correlations for Multi-label Image Recognition with Graph Networks—0
On the Importance of Subword Information for Morphological Tasks in Truly Low-Resource Languages—0
Multi-source Multi-view Transfer Learning in Neural Topic Modeling with Pretrained Topic and Word Embeddings—0
Classification Attention for Chinese NER—0
Distilled embedding: non-linear embedding factorization using knowledge distillation—0
Learn Interpretable Word Embeddings Efficiently with von Mises-Fisher Distribution—0
On Understanding Knowledge Graph Representation—0
DeepXML: Scalable & Accurate Deep Extreme Classification for Matching User Queries to Advertiser Bid Phrases—0
Atalaya at TASS 2019: Data Augmentation and Robust Embeddings for Sentiment Analysis—0
FreeLB: Enhanced Adversarial Training for Natural Language UnderstandingCode1
Extremely Small BERT Models from Mixed-Vocabulary Training—0
Interpreting Knowledge Graph Relation Representation from Word Embeddings—0
Code-switching Language Modeling With Bilingual Word Embeddings: A Case Study for Egyptian Arabic-English—0
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