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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 641–650 of 4002 papers

TitleStatusHype
A polar coordinate system represents syntax in large language models—0
Brundlefly at SemEval-2016 Task 12: Recurrent Neural Networks vs. Joint Inference for Clinical Temporal Information Extraction—0
Brundlefly at SemEval-2016 Task 12: Recurrent Neural Networks vs. Joint Inference for Clinical Temporal Information Extraction—0
BUCC 2017 Shared Task: a First Attempt Toward a Deep Learning Framework for Identifying Parallel Sentences in Comparable Corpora—0
Clinical Named Entity Recognition using Contextualized Token Representations—0
BUCC2020: Bilingual Dictionary Induction using Cross-lingual Embedding—0
Building a Monolingual Parallel Corpus for Text Simplification Using Sentence Similarity Based on Alignment between Word Embeddings—0
Building a robust sentiment lexicon with (almost) no resource—0
Building a Web-Scale Dependency-Parsed Corpus from CommonCrawl—0
Bootstrap Domain-Specific Sentiment Classifiers from Unlabeled Corpora—0
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