Results 31 to 40 of about 2,055,157 (200)
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
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
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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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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
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
A Survey of Active Learning for Natural Language Processing [PDF]
Conference on Empirical Methods in Natural Language Processing, 2022 In this work, we provide a literature review of active learning (AL) for its applications in natural language processing (NLP). In addition to a fine-grained categorization of query strategies, we also investigate several other important aspects of ...
Zhisong Zhang, Emma Strubell, E. Hovy
semanticscholar +1 more source
A Decade of Knowledge Graphs in Natural Language Processing: A Survey [PDF]
AACL, 2022 In pace with developments in the research field of artificial intelligence, knowledge graphs (KGs) have attracted a surge of interest from both academia and industry.
Phillip Schneider +5 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.
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HAT: Hardware-Aware Transformers for Efficient Natural Language Processing [PDF]
Annual Meeting of the Association for Computational Linguistics, 2020 Transformers are ubiquitous in Natural Language Processing (NLP) tasks, but they are difficult to be deployed on hardware due to the intensive computation.
Hanrui Wang +6 more
semanticscholar +1 more source
Applied Sciences, 2020 The modeling of fundamental frequency (F0) in speech synthesis is a critical factor affecting the intelligibility and naturalness of synthesized speech. In this paper, we focus on improving the modeling of F0 for Isarn speech synthesis. We propose the F0
Pongsathon Janyoi, Pusadee Seresangtakul
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