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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 1901–1925 of 4002 papers

TitleStatusHype
Initial Experiments in Data-Driven Morphological Analysis for Finnish—0
Injecting Wiktionary to improve token-level contextual representations using contrastive learning—0
Clickbait detection using word embeddings—0
Inline Detection of Domain Generation Algorithms with Context-Sensitive Word Embeddings—0
Are Word Embeddings Really a Bad Fit for the Estimation of Thematic Fit?—0
Alzheimer Disease Classification through ASR-based Transcriptions: Exploring the Impact of Punctuation and Pauses—0
Event Role Labelling using a Neural Network Model (\'Etiquetage en r\^oles \'ev\'enementiels fond\'e sur l'utilisation d'un mod\`ele neuronal) [in French]—0
Event Role Extraction using Domain-Relevant Word Representations—0
CLFD: A Novel Vectorization Technique and Its Application in Fake News Detection—0
Inspecting the concept knowledge graph encoded by modern language models—0
Instantiation—0
Integrating Dictionary Feature into A Deep Learning Model for Disease Named Entity Recognition—0
Integrating Distributional Lexical Contrast into Word Embeddings for Antonym-Synonym Distinction—0
Event Prominence Extraction Combining a Knowledge-Based Syntactic Parser and a BERT Classifier for Dutch—0
Event Ordering with a Generalized Model for Sieve Prediction Ranking—0
Integrating Pause Information with Word Embeddings in Language Models for Alzheimer's Disease Detection from Spontaneous Speech—0
Integrating Reviews into Personalized Ranking for Cold Start Recommendation—0
CLCL (Geneva) DINN Parser: a Neural Network Dependency Parser Ten Years Later—0
Are Word Embedding-based Features Useful for Sarcasm Detection?—0
Event extraction from Twitter using Non-Parametric Bayesian Mixture Model with Word Embeddings—0
Integrating Topic Modeling with Word Embeddings by Mixtures of vMFs—0
Integration of Domain Knowledge using Medical Knowledge Graph Deep Learning for Cancer Phenotyping—0
Event Detection Using Frame-Semantic Parser—0
Classifying Text-Based Conspiracy Tweets related to COVID-19 using Contextualized Word Embeddings—0
Classifying Semantic Clause Types: Modeling Context and Genre Characteristics with Recurrent Neural Networks and Attention—0
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