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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 1826–1850 of 4002 papers

TitleStatusHype
A Comparative Study of Transformers on Word Sense Disambiguation—0
Count-Based and Predictive Language Models for Exploring DeReKo—0
A Twitter Corpus and Benchmark Resources for German Sentiment Analysis—0
Improving Disfluency Detection by Self-Training a Self-Attentive Model—0
Improving Distributional Similarity with Lessons Learned from Word Embeddings—0
Designing a Russian Idiom-Annotated Corpus—0
BERTrade: Using Contextual Embeddings to Parse Old French—0
BERTMap: A BERT-based Ontology Alignment System—0
Improving evaluation and optimization of MT systems against MEANT—0
Improving Implicit Discourse Relation Recognition with Discourse-specific Word Embeddings—0
Improving Interpretability of Word Embeddings by Generating Definition and Usage—0
A Typedriven Vector Semantics for Ellipsis with Anaphora using Lambek Calculus with Limited Contraction—0
Analyzing the Framing of 2020 Presidential Candidates in the News—0
Improving Low-Resource Cross-lingual Document Retrieval by Reranking with Deep Bilingual Representations—0
Cross-Document Narrative Alignment of Environmental News: A Position Paper on the Challenge of Using Event Chains to Proxy Narrative Features—0
Improving Neural Knowledge Base Completion with Cross-Lingual Projections—0
From Fully Trained to Fully Random Embeddings: Improving Neural Machine Translation with Compact Word Embedding Tables—0
Improving Neural Network Performance by Injecting Background Knowledge: Detecting Code-switching and Borrowing in Algerian texts—0
Improving neural tagging with lexical information—0
Improving Opinion-Target Extraction with Character-Level Word Embeddings—0
Improving Optimization for Models With Continuous Symmetry Breaking—0
Improving Optimization in Models With Continuous Symmetry Breaking—0
Cross-Language Question Re-Ranking—0
Analyzing the Representational Geometry of Acoustic Word Embeddings—0
Describing Images using Inferred Visual Dependency Representations—0
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