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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 426–450 of 4002 papers

TitleStatusHype
Cross-lingual Feature Extraction from Monolingual Corpora for Low-resource Unsupervised Bilingual Lexicon Induction—0
Homophone Reveals the Truth: A Reality Check for Speech2VecCode1
Representing Affect Information in Word Embeddings—0
I2DFormer: Learning Image to Document Attention for Zero-Shot Image Classification—0
Unsupervised Lexical Substitution with Decontextualised EmbeddingsCode0
Learning Distinct and Representative Styles for Image CaptioningCode1
Integrating Form and Meaning: A Multi-Task Learning Model for Acoustic Word EmbeddingsCode0
Evaluation of Question Answering Systems: Complexity of judging a natural language—0
Visual Grounding of Inter-lingual Word-Embeddings—0
User recommendation system based on MIND dataset—0
Layer or Representation Space: What makes BERT-based Evaluation Metrics Robust?Code0
Knowledge-aware attentional neural network for review-based movie recommendation with explanationsCode0
Gender bias Evaluation in Luganda-English Machine Translation—0
Improving Translation of Out Of Vocabulary Words using Bilingual Lexicon Induction in Low-Resource Machine Translation—0
Do gender neutral affixes naturally reduce gender bias in static word embeddings?—0
Enhancing Semantic Understanding with Self-supervised Methods for Abstractive Dialogue Summarization—0
Debiasing Word Embeddings with Nonlinear GeometryCode0
Learning Dynamic Contextualised Word Embeddings via Template-based Temporal AdaptationCode0
Dialogue Term Extraction using Transfer Learning and Topological Data Analysis—0
Lost in Context? On the Sense-wise Variance of Contextualized Word Embeddings—0
Word-Embeddings Distinguish Denominal and Root-Derived Verbs in Semitic—0
Assessing the Unitary RNN as an End-to-End Compositional Model of Syntax—0
Where's the Learning in Representation Learning for Compositional Semantics and the Case of Thematic Fit—0
Large scale analysis of gender bias and sexism in song lyrics—0
Benchmarking zero-shot and few-shot approaches for tokenization, tagging, and dependency parsing of Tagalog text—0
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