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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 1701–1750 of 4002 papers

TitleStatusHype
Transformer++—0
Squashed Shifted PMI Matrix: Bridging Word Embeddings and Hyperbolic SpacesCode0
Semantic Relatedness for Keyword Disambiguation: Exploiting Different Embeddings—0
End-to-End Entity Linking and Disambiguation leveraging Word and Knowledge Graph Embeddings—0
Language-Independent Tokenisation Rivals Language-Specific Tokenisation for Word Similarity Prediction—0
Parsing Early Modern English for Linguistic Search—0
Expressing Objects just like Words: Recurrent Visual Embedding for Image-Text Matching—0
Measuring Social Biases in Grounded Vision and Language EmbeddingsCode0
Towards Detection of Subjective Bias using Contextualized Word EmbeddingsCode0
Supervised Phrase-boundary EmbeddingsCode0
Semantic Relatedness and Taxonomic Word Embeddings—0
Word Embeddings Inherently Recover the Conceptual Organization of the Human Mind—0
Fast and Robust Comparison of Probability Measures in Heterogeneous SpacesCode0
Twitter Bot Detection Using Bidirectional Long Short-term Memory Neural Networks and Word Embeddings—0
Detecting Fake News with Capsule Neural Networks—0
Pretrained Transformers for Simple Question Answering over Knowledge GraphsCode0
A Deep Neural Framework for Contextual Affect Detection—0
Facebook Ads Monitor: An Independent Auditing System for Political Ads on Facebook—0
Bias in word embeddings—0
Weakly Supervised Few-shot Object Segmentation using Co-Attention with Visual and Semantic Embeddings—0
A Deep Learning Approach to Behavior-Based Learner Modeling—0
Zero-Shot Activity Recognition with Videos—0
Generating Sense Embeddings for Syntactic and Semantic Analogy for PortugueseCode0
Text-based inference of moral sentiment change—0
A Common Semantic Space for Monolingual and Cross-Lingual Meta-EmbeddingsCode0
DSR: A Collection for the Evaluation of Graded Disease-Symptom Relations—0
Humpty Dumpty: Controlling Word Meanings via Corpus Poisoning—0
Balancing the composition of word embeddings across heterogenous data sets—0
On the Replicability of Combining Word Embeddings and Retrieval Models—0
Visual Storytelling via Predicting Anchor Word Embeddings in the Stories—0
Learning Cross-Context Entity Representations from Text—0
TableQnA: Answering List Intent Queries With Web Tables—0
Multiplex Word Embeddings for Selectional Preference AcquisitionCode0
Binary and Multitask Classification Model for Dutch Anaphora Resolution: Die/Dat Prediction—0
Semantic Sensitive TF-IDF to Determine Word Relevance in DocumentsCode0
Improving Entity Linking by Modeling Latent Entity Type Information—0
Question Type Classification Methods Comparison—0
Decoding Brain Activity Associated with Literal and Metaphoric Sentence Comprehension Using Distributional Semantic Models—0
Learning Lexical Subspaces in a Distributional Vector SpaceCode0
Variable-Bitrate Neural Compression via Bayesian Arithmetic Coding—0
Robust Cross-lingual Embeddings from Parallel SentencesCode0
Encoding word order in complex embeddingsCode0
Job Prediction: From Deep Neural Network Models to Applications—0
One-Shot Weakly Supervised Video Object Segmentation—0
Analyzing Structures in the Semantic Vector Space: A Framework for Decomposing Word EmbeddingsCode0
The performance evaluation of Multi-representation in the Deep Learning models for Relation Extraction Task—0
Predicting the Outcome of Judicial Decisions made by the European Court of Human Rights—0
A Comparison of Architectures and Pretraining Methods for Contextualized Multilingual Word Embeddings—0
Artificial mental phenomena: Psychophysics as a framework to detect perception biases in AI models—0
Integrating Lexical Knowledge in Word Embeddings using Sprinkling and Retrofitting—0
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