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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 3401–3450 of 4002 papers

TitleStatusHype
Comparative Analysis of Static and Contextual Embeddings for Analyzing Semantic Changes in Medieval Latin Charters—0
Comparative study of LSA vs Word2vec embeddings in small corpora: a case study in dreams database—0
Comparing Approaches for Automatic Question Identification—0
Comparing CNN and LSTM character-level embeddings in BiLSTM-CRF models for chemical and disease named entity recognition—0
Comparing Contextual and Static Word Embeddings with Small Data—0
Comparing Feature-Engineering and Feature-Learning Approaches for Multilingual Translationese Classification—0
Comparing in context: Improving cosine similarity measures with a metric tensor—0
Comparing Performance of Different Linguistically-Backed Word Embeddings for Cyberbullying Detection—0
Comparing Recurrent and Convolutional Architectures for English-Hindi Neural Machine Translation—0
Comparing the Intrinsic Performance of Clinical Concept Embeddings by Their Field of Medicine—0
Comparing the Performance of Feature Representations for the Categorization of the Easy-to-Read Variety vs Standard Language—0
Comparing Word Representations for Implicit Discourse Relation Classification—0
Comparison between Voting Classifier and Deep Learning methods for Arabic Dialect Identification—0
Comparison of Paragram and GloVe Results for Similarity Benchmarks—0
Comparison of Representations of Named Entities for Document Classification—0
Comparison of Short-Text Sentiment Analysis Methods for Croatian—0
Compiling a Highly Accurate Bilingual Lexicon by Combining Different Approaches—0
Complementary Strategies for Low Resourced Morphological Modeling—0
Complex networks based word embeddings—0
Complex Ontology Matching with Large Language Model Embeddings—0
Component-Enhanced Chinese Character Embeddings—0
Composing Knowledge Graph Embeddings via Word Embeddings—0
Composing Noun Phrase Vector Representations—0
Composing Word Vectors for Japanese Compound Words Using Bilingual Word Embeddings—0
Compositional and Lexical Semantics in RoBERTa, BERT and DistilBERT: A Case Study on CoQA—0
Compositional Fusion of Signals in Data Embedding—0
Compositional Morpheme Embeddings with Affixes as Functions and Stems as Arguments—0
Compound Embedding Features for Semi-supervised Learning—0
Compound or Term Features? Analyzing Salience in Predicting the Difficulty of German Noun Compounds across Domains—0
Comprehensive Analysis of Aspect Term Extraction Methods using Various Text Embeddings—0
Compressing Word Embeddings—0
Compressing Word Embeddings Using Syllables—0
Compression of Generative Pre-trained Language Models via Quantization—0
Computational Detection of Intertextual Parallels in Biblical Hebrew: A Benchmark Study Using Transformer-Based Language Models—0
Computationally Constructed Concepts: A Machine Learning Approach to Metaphor Interpretation Using Usage-Based Construction Grammatical Cues—0
Conceptor Debiasing of Word Representations Evaluated on WEAT—0
Concept Space Alignment in Multilingual LLMs—0
Conceptual Cognitive Maps Formation with Neural Successor Networks and Word Embeddings—0
Conditional Generative Adversarial Networks for Emoji Synthesis with Word Embedding Manipulation—0
Conditional Random Fields for Metaphor Detection—0
Conditional Word Embedding and Hypothesis Testing via Bayes-by-Backprop—0
Connecting Supervised and Unsupervised Sentence Embeddings—0
Considerations for the Interpretation of Bias Measures of Word Embeddings—0
Consistency and Variation in Kernel Neural Ranking Model—0
Consistent Structural Relation Learning for Zero-Shot Segmentation—0
Constrained Sequence-to-sequence Semitic Root Extraction for Enriching Word Embeddings—0
Constructing High Quality Sense-specific Corpus and Word Embedding via Unsupervised Elimination of Pseudo Multi-sense—0
Constructing Vec-tionaries to Extract Message Features from Texts: A Case Study of Moral Appeals—0
Content-Aware Speaker Embeddings for Speaker Diarisation—0
Content Selection through Paraphrase Detection: Capturing different Semantic Realisations of the Same Idea—0
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