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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 626–650 of 4002 papers

TitleStatusHype
An experimental study of the vision-bottleneck in VQA—0
Hindi/Bengali Sentiment Analysis Using Transfer Learning and Joint Dual Input Learning with Self AttentionCode0
Bench-Marking And Improving Arabic Automatic Image Captioning Through The Use Of Multi-Task Learning Paradigm—0
HistBERT: A Pre-trained Language Model for Diachronic Lexical Semantic AnalysisCode0
Fairness for Text Classification Tasks with Identity Information Data Augmentation Methods—0
L3Cube-MahaCorpus and MahaBERT: Marathi Monolingual Corpus, Marathi BERT Language Models, and Resources—0
Towards a Theoretical Understanding of Word and Relation Representation—0
Learning Representations of Entities and Relations—0
Recognition of Implicit Geographic Movement in Text—0
Taxonomy Enrichment with Text and Graph Vector Representations—0
Evaluating the timing and magnitude of semantic change in diachronic word embedding models—0
Regional Negative Bias in Word Embeddings Predicts Racial Animus--but only via Name Frequency—0
Automation of Citation Screening for Systematic Literature Reviews using Neural Networks: A Replicability StudyCode0
Evaluating Machine Common Sense via Cloze Testing—0
Tracking Legislators’ Expressed Policy Agendas in Real TimeCode0
Sectioning of Biomedical Abstracts: A Sequence of Sequence Classification Task—0
Modeling Tension in Stories via Commonsense Reasoning and Emotional Word Embeddings—0
Revisiting Additive Compositionality: AND, OR, and NOT Operations with Word Embeddings—0
Impart Contextualization to Static Word Embeddings through Semantic Relations—0
Language Models for Code-switch Detection of te reo Māori and English in a Low-resource Setting—0
Feasibility of BERT Embeddings For Domain-Specific Knowledge Mining—0
Don’t Forget Cheap Training Signals Before Building Unsupervised Bilingual Word Embeddings—0
Topic Modeling with Topological Data Analysis—0
Minimally-Supervised Relation Induction from Pre-trained Language Model—0
Cooperative Self-training of Machine Reading Comprehension—0
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