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Relevant bibliographies by topics / Events in natural language processing

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Academic literature on the topic 'Events in natural language processing'

Author: Grafiati

Published: 3 June 2023

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Journal articles on the topic "Events in natural language processing"

1

KARTTUNEN, LAURI, KIMMO KOSKENNIEMI, and GERTJAN VAN NOORD. "Finite state methods in natural language processing." Natural Language Engineering 9, no. 1 (2003): 1–3. http://dx.doi.org/10.1017/s1351324903003139.

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Finite state methods have been in common use in various areas of natural language processing (NLP) for many years. A series of specialized workshops in this area illustrates this. In 1996, András Kornai organized a very successful workshop entitled Extended Finite State Models of Language. One of the results of that workshop was a special issue of Natural Language Engineering (Volume 2, Number 4). In 1998, Kemal Oflazer organized a workshop called Finite State Methods in Natural Language Processing. A selection of submissions for this workshop were later included in a special issue of Computat

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2

Li, Yong, Xiaojun Yang, Min Zuo, Qingyu Jin, Haisheng Li, and Qian Cao. "Deep Structured Learning for Natural Language Processing." ACM Transactions on Asian and Low-Resource Language Information Processing 20, no. 3 (2021): 1–14. http://dx.doi.org/10.1145/3433538.

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The real-time and dissemination characteristics of network information make net-mediated public opinion become more and more important food safety early warning resources, but the data of petabyte (PB) scale growth also bring great difficulties to the research and judgment of network public opinion, especially how to extract the event role of network public opinion from these data and analyze the sentiment tendency of public opinion comment. First, this article takes the public opinion of food safety network as the research point, and a BLSTM-CRF model for automatically marking the role of eve

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Ozonoff, Al, Carly E. Milliren, Kerri Fournier, et al. "Electronic surveillance of patient safety events using natural language processing." Health Informatics Journal 28, no. 4 (2022): 146045822211324. http://dx.doi.org/10.1177/14604582221132429.

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Objective We describe our approach to surveillance of reportable safety events captured in hospital data including free-text clinical notes. We hypothesize that a) some patient safety events are documented only in the clinical notes and not in any other accessible source; and b) large-scale abstraction of event data from clinical notes is feasible. Materials and Methods We use regular expressions to generate a training data set for a machine learning model and apply this model to the full set of clinical notes and conduct further review to identify safety events of interest. We demonstrate thi

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Guda, Vanitha, and SureshKumar Sanampudi. "Event Time Relationship in Natural Language Text." International Journal of Recent Contributions from Engineering, Science & IT (iJES) 7, no. 3 (2019): 4. http://dx.doi.org/10.3991/ijes.v7i3.10985.

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<p>Due to the numerous information needs, retrieval of events from a given natural language text is inevitable. In natural language processing (NLP) perspective, "Events" are situations, occurrences, real-world entities or facts. Extraction of events and arranging them on a timeline is helpful in various NLP application like building the summary of news articles, processing health records, and Question Answering System (QA) systems. This paper presents a framework for identifying the events and times from a given document and representing them using a graph data structure. As a result, a

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Balgi, Sanjana Madhav. "Fake News Detection using Natural Language Processing." International Journal for Research in Applied Science and Engineering Technology 10, no. 6 (2022): 4790–95. http://dx.doi.org/10.22214/ijraset.2022.45095.

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Abstract: Fake news is information that is false or misleading but is reported as news. The tendency for people to spread false information is influenced by human behaviour; research indicates that people are drawn to unexpected fresh events and information, which increases brain activity. Additionally, it was found that motivated reasoning helps spread incorrect information. This ultimately encourages individuals to repost or disseminate deceptive content, which is frequently identified by click-bait and attention-grabbing names. The proposed study uses machine learning and natural language p

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Hkiri, Emna, Souheyl Mallat, and Mounir Zrigui. "Events Automatic Extraction from Arabic Texts." International Journal of Information Retrieval Research 6, no. 1 (2016): 36–51. http://dx.doi.org/10.4018/ijirr.2016010103.

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The event extraction task consists in determining and classifying events within an open-domain text. It is very new for the Arabic language, whereas it attained its maturity for some languages such as English and French. Events extraction was also proved to help Natural Language Processing tasks such as Information Retrieval and Question Answering, text mining, machine translation etc… to obtain a higher performance. In this article, we present an ongoing effort to build a system for event extraction from Arabic texts using Gate platform and other tools.

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Melton, Genevieve B., and George Hripcsak. "Automated Detection of Adverse Events Using Natural Language Processing of Discharge Summaries." Journal of the American Medical Informatics Association 12, no. 4 (2005): 448–57. http://dx.doi.org/10.1197/jamia.m1794.

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YLI-JYRÄ, ANSSI, ANDRÁS KORNAI, and JACQUES SAKAROVITCH. "Finite-state methods and models in natural language processing." Natural Language Engineering 17, no. 2 (2011): 141–44. http://dx.doi.org/10.1017/s1351324911000015.

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For the past two decades, specialised events on finite-state methods have been successful in presenting interesting studies on natural language processing to the public through journals and collections. The FSMNLP workshops have become well-known among researchers and are now the main forum of the Association for Computational Linguistics' (ACL) Special Interest Group on Finite-State Methods (SIGFSM). The current issue on finite-state methods and models in natural language processing was planned in 2008 in this context as a response to a call for special issue proposals. In 2010, the issue rec

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Abbood, Auss, Alexander Ullrich, Rüdiger Busche, and Stéphane Ghozzi. "EventEpi—A natural language processing framework for event-based surveillance." PLOS Computational Biology 16, no. 11 (2020): e1008277. http://dx.doi.org/10.1371/journal.pcbi.1008277.

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According to the World Health Organization (WHO), around 60% of all outbreaks are detected using informal sources. In many public health institutes, including the WHO and the Robert Koch Institute (RKI), dedicated groups of public health agents sift through numerous articles and newsletters to detect relevant events. This media screening is one important part of event-based surveillance (EBS). Reading the articles, discussing their relevance, and putting key information into a database is a time-consuming process. To support EBS, but also to gain insights into what makes an article and the eve

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Kosiv, Yurii A., and Vitaliy S. Yakovyna. "Three language political leaning text classification using natural language processing methods." Applied Aspects of Information Technology 5, no. 4 (2022): 359–70. http://dx.doi.org/10.15276/aait.05.2022.24.

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In this article, the problem of political leaning classificationof the text resource is solved. First, a detailed analysis of ten stud-ies on the work’s topicwas performed in the form of comparative characteristicsof the used methodologies.Literary sources were compared according to the problem-solvingmethods,the learning that was carried out, the evaluation metrics, and according to the vectorizations.Thus, it was determined that machine learning algorithms and neural networks, as well as vectorizationmethods TF-IDF and Word2Vec, were most often used to solve the problem.Next, various classif

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