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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 1651–1700 of 4002 papers

TitleStatusHype
Transformer++—0
Understanding the Downstream Instability of Word EmbeddingsCode1
Squashed Shifted PMI Matrix: Bridging Word Embeddings and Hyperbolic SpacesCode0
End-to-End Entity Linking and Disambiguation leveraging Word and Knowledge Graph Embeddings—0
Semantic Relatedness for Keyword Disambiguation: Exploiting Different Embeddings—0
Language-Independent Tokenisation Rivals Language-Specific Tokenisation for Word Similarity Prediction—0
Parsing Early Modern English for Linguistic Search—0
Efficient Sentence Embedding via Semantic Subspace AnalysisCode1
Refinement of Unsupervised Cross-Lingual Word EmbeddingsCode1
Expressing Objects just like Words: Recurrent Visual Embedding for Image-Text Matching—0
Measuring Social Biases in Grounded Vision and Language EmbeddingsCode0
Variational Bayesian QuantizationCode1
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
Multilingual acoustic word embedding models for processing zero-resource languagesCode1
Fast and Robust Comparison of Probability Measures in Heterogeneous SpacesCode0
Interpretable & Time-Budget-Constrained Contextualization for Re-RankingCode1
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
Data Mining in Clinical Trial Text: Transformers for Classification and Question Answering TasksCode1
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
The POLAR Framework: Polar Opposites Enable Interpretability of Pre-Trained Word EmbeddingsCode1
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
Binary and Multitask Classification Model for Dutch Anaphora Resolution: Die/Dat Prediction—0
Multiplex Word Embeddings for Selectional Preference AcquisitionCode0
Paraphrase Generation with Latent Bag of WordsCode1
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
Two-Level Transformer and Auxiliary Coherence Modeling for Improved Text SegmentationCode1
Variable-Bitrate Neural Compression via Bayesian Arithmetic Coding—0
Learning Lexical Subspaces in a Distributional Vector SpaceCode0
Decoding Brain Activity Associated with Literal and Metaphoric Sentence Comprehension Using Distributional Semantic Models—0
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