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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 1926–1950 of 4002 papers

TitleStatusHype
CLCL (Geneva) DINN Parser: a Neural Network Dependency Parser Ten Years Later—0
Are Word Embedding-based Features Useful for Sarcasm Detection?—0
Interpretable Adversarial Training for Text—0
Interpretable and Globally Optimal Prediction for Textual Grounding using Image Concepts—0
Interpretable embeddings to understand computing careers—0
Interpretable Neural Embeddings with Sparse Self-Representation—0
Event extraction from Twitter using Non-Parametric Bayesian Mixture Model with Word Embeddings—0
Event Detection Using Frame-Semantic Parser—0
Interpretable Word Embeddings via Informative Priors—0
Classifying Text-Based Conspiracy Tweets related to COVID-19 using Contextualized Word Embeddings—0
Interpreting Pretrained Contextualized Representations via Reductions to Static Embeddings—0
Classifying Semantic Clause Types: Modeling Context and Genre Characteristics with Recurrent Neural Networks and Attention—0
Interpreting Word-Level Hidden State Behaviour of Character-Level LSTM Language Models—0
A LSTM Approach with Sub-Word Embeddings for Mongolian Phrase Break Prediction—0
Inter-Weighted Alignment Network for Sentence Pair Modeling—0
Evaluation of Word Embeddings for the Social Sciences—0
Intrinsic Bias Metrics Do Not Correlate with Application Bias—0
Intrinsic Evaluations of Word Embeddings: What Can We Do Better?—0
Intrinsic Image Captioning Evaluation—0
Evaluation of word embeddings against cognitive processes: primed reaction times in lexical decision and naming tasks—0
Evaluation of Taxonomy Enrichment on Diachronic WordNet Versions—0
Introducing Syllable Tokenization for Low-resource Languages: A Case Study with Swahili—0
Invariance and identifiability issues for word embeddings—0
InvBERT: Reconstructing Text from Contextualized Word Embeddings by inverting the BERT pipeline—0
Evaluation of Stacked Embeddings for Bulgarian on the Downstream Tasks POS and NERC—0
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