Results 31 to 40 of about 3,185,640 (378)
TweetNLP: Cutting-Edge Natural Language Processing for Social Media [PDF]
Conference on Empirical Methods in Natural Language Processing, 2022 In this paper we present TweetNLP, an integrated platform for Natural Language Processing (NLP) in social media. TweetNLP supports a diverse set of NLP tasks, including generic focus areas such as sentiment analysis and named entity recognition, as well ...
José Camacho-Collados +13 more
semanticscholar +1 more source
How we do things with words: Analyzing text as social and cultural data [PDF]
, 2019 In this article we describe our experiences with computational text analysis. We hope to achieve three primary goals. First, we aim to shed light on thorny issues not always at the forefront of discussions about computational text analysis methods ...
Dedeo, Simon +8 more
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Applied Sciences, 2023 The development of conversational agents that can generate relevant and meaningful replies is a challenging task in the field of natural language processing.
Quoc-Dai Luong Tran, Anh-Cuong Le
doaj +1 more source
Natural Language Processing for Policymaking
, 2022 AbstractLanguage is the medium for many political activities, from campaigns to news reports. Natural language processing (NLP) uses computational tools to parse text into key information that is needed for policymaking. In this chapter, we introduce common methods of NLP, including text classification, topic modelling, event extraction, and text ...
Jin, Zhijing, Mihalcea, Rada
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COVID-Twitter-BERT: A natural language processing model to analyse COVID-19 content on Twitter [PDF]
Frontiers in Artificial Intelligence, 2020 Introduction This study presents COVID-Twitter-BERT (CT-BERT), a transformer-based model that is pre-trained on a large corpus of COVID-19 related Twitter messages. CT-BERT is specifically designed to be used on COVID-19 content, particularly from social
Martin Müller +2 more
semanticscholar +1 more source
Readers and Reading in the First World War [PDF]
, 2015 This essay consists of three individually authored and interlinked sections. In ‘A Digital Humanities Approach’, Francesca Benatti looks at datasets and databases (including the UK Reading Experience Database) and shows how a systematic, macro-analytical
Edmund G. C. King, +4 more
core +1 more source
A Survey of the Usages of Deep Learning for Natural Language Processing
IEEE Transactions on Neural Networks and Learning Systems, 2020 Over the last several years, the field of natural language processing has been propelled forward by an explosion in the use of deep learning models.
Dan Otter, Julian R. Medina, J. Kalita
semanticscholar +1 more source
A large annotated corpus for learning natural language inference [PDF]
Conference on Empirical Methods in Natural Language Processing, 2015 Understanding entailment and contradiction is fundamental to understanding natural language, and inference about entailment and contradiction is a valuable testing ground for the development of semantic representations. However, machine learning research
Samuel R. Bowman +3 more
semanticscholar +1 more source
Efficient Methods for Natural Language Processing: A Survey [PDF]
Transactions of the Association for Computational Linguistics, 2022 Recent work in natural language processing (NLP) has yielded appealing results from scaling model parameters and training data; however, using only scale to improve performance means that resource consumption also grows. Such resources include data, time,
Marcos Vinícius Treviso +17 more
semanticscholar +1 more source
Putting Natural in Natural Language Processing
Findings of the Association for Computational Linguistics: ACL 2023, 2023 Human language is firstly spoken and only secondarily written. Text, however, is a very convenient and efficient representation of language, and modern civilization has made it ubiquitous. Thus the field of NLP has overwhelmingly focused on processing written rather than spoken language.
openaire +2 more sources