Representation Learning for Natural Language Processing
This book aims to review and present the recent advances of distributed representation learning for NLP, including why representation learning can improve NLP, how representation learning takes part in various important topics of NLP, and what challenges are still not well addressed by distributed representation.
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Representation LearningSimilar Papers 제목 키워드 기반
Multilingual and Interlingual Semantic Representations for Natural Language Processing: A Brief Introduction
We introduce the Computational Linguistics special issue on Multilingual and Interlingual Semantic Representations for Natural Language Processing. We situate the special issue{'}s five articles in the context of our fas…
ArticlesExploring Chemical Space using Natural Language Processing Methodologies for Drug Discovery
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Large language models (LLMs) have become increasingly useful computational models of human language processing, but it remains unclear whether vision-language learning makes text representations more human-like during na…
Ad Text Classification with Transformer-Based Natural Language Processing Methods
In this study, a natural language processing-based (NLP-based) method is proposed for the sector-wise automatic classification of ad texts created on online advertising platforms. Our data set consists of approximately 2…
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Natural language is inherently a discrete symbolic representation of human knowledge. Recent advances in machine learning (ML) and in natural language processing (NLP) seem to contradict the above intuition: discrete sym…