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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 2701–2750 of 4002 papers

TitleStatusHype
WEAC: Word embeddings for anomaly classification from event logs—0
Weakly-Supervised Concept-based Adversarial Learning for Cross-lingual Word Embeddings—0
Weakly Supervised Cross-Lingual Named Entity Recognition via Effective Annotation and Representation Projection—0
Weakly Supervised Few-shot Object Segmentation using Co-Attention with Visual and Semantic Embeddings—0
WECHSEL: Effective initialization of subword embeddings for cross-lingual transfer of monolingual language models—0
Well-calibrated Confidence Measures for Multi-label Text Classification with a Large Number of Labels—0
WEmbSim: A Simple yet Effective Metric for Image Captioning—0
What Analogies Reveal about Word Vectors and their Compositionality—0
What are the biases in my word embedding?—0
What can you do with a rock? Affordance extraction via word embeddings—0
What company do words keep? Revisiting the distributional semantics of J.R. Firth & Zellig Harris—0
What does Neural Bring? Analysing Improvements in Morphosyntactic Annotation and Lemmatisation of Slovenian, Croatian and Serbian—0
What Does This Word Mean? Explaining Contextualized Embeddings with Natural Language Definition—0
What do we need to know about an unknown word when parsing German—0
What do you mean, BERT? Assessing BERT as a Distributional Semantics Model—0
What makes multilingual BERT multilingual?—0
What's in a Name? Reducing Bias in Bios without Access to Protected Attributes—0
What's in an Embedding? Analyzing Word Embeddings through Multilingual Evaluation—0
What's in Your Embedding, And How It Predicts Task Performance—0
What the Vec? Towards Probabilistically Grounded Embeddings—0
When Hyperparameters Help: Beneficial Parameter Combinations in Distributional Semantic Models—0
When Polysemy Matters: Modeling Semantic Categorization with Word Embeddings—0
When Specialization Helps: Using Pooled Contextualized Embeddings to Detect Chemical and Biomedical Entities in Spanish—0
When Word Embeddings Become Endangered—0
Where exactly does contextualization in a PLM happen?—0
Where's the Learning in Representation Learning for Compositional Semantics and the Case of Thematic Fit—0
Which Evaluations Uncover Sense Representations that Actually Make Sense?—0
Which Student is Best? A Comprehensive Knowledge Distillation Exam for Task-Specific BERT Models—0
Whom to Learn From? Graph- vs. Text-based Word Embeddings—0
Why does PairDiff work? - A Mathematical Analysis of Bilinear Relational Compositional Operators for Analogy Detection—0
Why is unsupervised alignment of English embeddings from different algorithms so hard?—0
Why Overfitting Isn't Always Bad: Retrofitting Cross-Lingual Word Embeddings to Dictionaries—0
Why PairDiff works? -- A Mathematical Analysis of Bilinear Relational Compositional Operators for Analogy Detection—0
WiC: the Word-in-Context Dataset for Evaluating Context-Sensitive Meaning Representations—0
“Wikily” Supervised Neural Translation Tailored to Cross-Lingual Tasks—0
Wild Devs' at SemEval-2017 Task 2: Using Neural Networks to Discover Word Similarity—0
With More Contexts Comes Better Performance: Contextualized Sense Embeddings for All-Round Word Sense Disambiguation—0
WLV-RIT at HASOC-Dravidian-CodeMix-FIRE2020: Offensive Language Identification in Code-switched YouTube Comments—0
WMDO: Fluency-based Word Mover's Distance for Machine Translation Evaluation—0
WOLVESAAR at SemEval-2016 Task 1: Replicating the Success of Monolingual Word Alignment and Neural Embeddings for Semantic Textual Similarity—0
Word2net: Deep Representations of Language—0
Word2rate: training and evaluating multiple word embeddings as statistical transitions—0
Word2Sense: Sparse Interpretable Word Embeddings—0
Word-Alignment-Based Segment-Level Machine Translation Evaluation using Word Embeddings—0
Word and Document Embeddings based on Neural Network Approaches—0
Word and Phrase Features in Graph Convolutional Network for Automatic Question Classification—0
Word associations and the distance properties of context-aware word embeddings—0
Word-Context Character Embeddings for Chinese Word Segmentation—0
WordDecipher: Enhancing Digital Workspace Communication with Explainable AI for Non-native English Speakers—0
Word Definitions from Large Language Models—0
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