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

Author: Grafiati
Published: 3 June 2023

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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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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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Lakkad, Aditya Kamleshbhai, Rushit Dharmendrabhai Bhadaniya, Vraj Nareshkumar Shah, and Lavanya K. "Complex Events Processing on Live News Events Using Apache Kafka and Clustering Techniques." International Journal of Intelligent Information Technologies 17, no. 1 (2021): 39–52. http://dx.doi.org/10.4018/ijiit.2021010103.

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The explosive growth of news and news content generated worldwide, coupled with the expansion through online media and rapid access to data, has made trouble and screening of news tedious. An expanding need for a model that can reprocess, break down, and order main content to extract interpretable information, explicitly recognizing subjects and content-driven groupings of articles. This paper proposed automated analyzing heterogeneous news through complex event processing (CEP) and machine learning (ML) algorithms. Initially, news content streamed using Apache Kafka, stored in Apache Druid, a
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Kehl, Kenneth L., Wenxin Xu, Eva Lepisto, et al. "Natural Language Processing to Ascertain Cancer Outcomes From Medical Oncologist Notes." JCO Clinical Cancer Informatics, no. 4 (September 2020): 680–90. http://dx.doi.org/10.1200/cci.20.00020.

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PURPOSE Cancer research using electronic health records and genomic data sets requires clinical outcomes data, which may be recorded only in unstructured text by treating oncologists. Natural language processing (NLP) could substantially accelerate extraction of this information. METHODS Patients with lung cancer who had tumor sequencing as part of a single-institution precision oncology study from 2013 to 2018 were identified. Medical oncologists’ progress notes for these patients were reviewed. For each note, curators recorded whether the assessment/plan indicated any cancer, progression/wor
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BOURBAKIS, NIKOLAOS, and MICHAEL MILLS. "CONVERTING NATURAL LANGUAGE TEXT SENTENCES INTO SPN REPRESENTATIONS FOR ASSOCIATING EVENTS." International Journal of Semantic Computing 06, no. 03 (2012): 353–70. http://dx.doi.org/10.1142/s1793351x12500067.

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A better understanding of events many times requires the association and the efficient representation of multi-modal information. A good approach to this important issue is the development of a common platform for converting different modalities (such as images, text, etc.) into the same medium and associating them for efficient processing and understanding. In a previous paper we have presented a Local-Global graph model for the conversion of images into graphs with attributes and then into natural language (NL) text sentences [25]. Here, in this paper we propose the conversion of NL text sen
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Verma, Sudha, Sarah Vieweg, William Corvey, et al. "Natural Language Processing to the Rescue? Extracting "Situational Awareness" Tweets During Mass Emergency." Proceedings of the International AAAI Conference on Web and Social Media 5, no. 1 (2021): 385–92. http://dx.doi.org/10.1609/icwsm.v5i1.14119.

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In times of mass emergency, vast amounts of data are generated via computer-mediated communication (CMC) that are difficult to manually cull and organize into a coherent picture. Yet valuable information is broadcast, and can provide useful insight into time- and safety-critical situations if captured and analyzed properly and rapidly. We describe an approach for automatically identifying messages communicated via Twitter that contribute to situational awareness, and explain why it is beneficial for those seeking information during mass emergencies.We collected Twitter messages from four diffe
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Fong, Allan, Nicole Harriott, Donna M. Walters, Hanan Foley, Richard Morrissey, and Raj R. Ratwani. "Integrating natural language processing expertise with patient safety event review committees to improve the analysis of medication events." International Journal of Medical Informatics 104 (August 2017): 120–25. http://dx.doi.org/10.1016/j.ijmedinf.2017.05.005.

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Ujiie, Shogo, Shuntaro Yada, Shoko Wakamiya, and Eiji Aramaki. "Identification of Adverse Drug Event–Related Japanese Articles: Natural Language Processing Analysis." JMIR Medical Informatics 8, no. 11 (2020): e22661. http://dx.doi.org/10.2196/22661.

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Background Medical articles covering adverse drug events (ADEs) are systematically reported by pharmaceutical companies for drug safety information purposes. Although policies governing reporting to regulatory bodies vary among countries and regions, all medical article reporting may be categorized as precision or recall based. Recall-based reporting, which is implemented in Japan, requires the reporting of any possible ADE. Therefore, recall-based reporting can introduce numerous false negatives or substantial amounts of noise, a problem that is difficult to address using limited manual labor
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Ryciak, Piotr, Katarzyna Wasielewska, and Artur Janicki. "Anomaly Detection in Log Files Using Selected Natural Language Processing Methods." Applied Sciences 12, no. 10 (2022): 5089. http://dx.doi.org/10.3390/app12105089.

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In this article, we address the problem of detecting anomalies in system log files. Computer systems generate huge numbers of events, which are noted in event log files. While most of them report normal actions, an unusual entry may inform about a failure or malware infection. A human operator may easily miss such an entry; therefore, anomaly detection methods are used for this purpose. In our work, we used an approach known from the natural language processing (NLP) domain, which operates on so-called embeddings, that is vector representations of words or phrases. We describe an improved vers
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Mashima, Yukinori, Takashi Tamura, Jun Kunikata, et al. "Using Natural Language Processing Techniques to Detect Adverse Events From Progress Notes Due to Chemotherapy." Cancer Informatics 21 (January 2022): 117693512210850. http://dx.doi.org/10.1177/11769351221085064.

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Objective: In recent years, natural language processing (NLP) techniques have progressed, and their application in the medical field has been tested. However, the use of NLP to detect symptoms from medical progress notes written in Japanese, remains limited. We aimed to detect 2 gastrointestinal symptoms that interfere with the continuation of chemotherapy—nausea/vomiting and diarrhea—from progress notes using NLP, and then to analyze factors affecting NLP. Materials and methods: In this study, 200 patients were randomly selected from 5277 patients who received intravenous injections of cytoto
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Llorens, Hector, Estela Saquete, and Borja Navarro-Colorado. "Applying semantic knowledge to the automatic processing of temporal expressions and events in natural language." Information Processing & Management 49, no. 1 (2013): 179–97. http://dx.doi.org/10.1016/j.ipm.2012.05.005.

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Claus, Stefan, and Massimo Stella. "Natural Language Processing and Cognitive Networks Identify UK Insurers’ Trends in Investor Day Transcripts." Future Internet 14, no. 10 (2022): 291. http://dx.doi.org/10.3390/fi14100291.

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The ability to spot key ideas, trends, and relationships between them in documents is key to financial services, such as banks and insurers. Identifying patterns across vast amounts of domain-specific reports is crucial for devising efficient and targeted supervisory plans, subsequently allocating limited resources where most needed. Today, insurance supervisory planning primarily relies on quantitative metrics based on numerical data (e.g., solvency financial returns). The purpose of this work is to assess whether Natural Language Processing (NLP) and cognitive networks can highlight events a
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Li, Liuqing, Jack Geissinger, William A. Ingram, and Edward A. Fox. "Teaching Natural Language Processing through Big Data Text Summarization with Problem-Based Learning." Data and Information Management 4, no. 1 (2020): 18–43. http://dx.doi.org/10.2478/dim-2020-0003.

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AbstractNatural language processing (NLP) covers a large number of topics and tasks related to data and information management, leading to a complex and challenging teaching process. Meanwhile, problem-based learning is a teaching technique specifically designed to motivate students to learn efficiently, work collaboratively, and communicate effectively. With this aim, we developed a problem-based learning course for both undergraduate and graduate students to teach NLP. We provided student teams with big data sets, basic guidelines, cloud computing resources, and other aids to help different
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Sprugnoli, Rachele, and Sara Tonelli. "Novel Event Detection and Classification for Historical Texts." Computational Linguistics 45, no. 2 (2019): 229–65. http://dx.doi.org/10.1162/coli_a_00347.

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Event processing is an active area of research in the Natural Language Processing community, but resources and automatic systems developed so far have mainly addressed contemporary texts. However, the recognition and elaboration of events is a crucial step when dealing with historical texts Particularly in the current era of massive digitization of historical sources: Research in this domain can lead to the development of methodologies and tools that can assist historians in enhancing their work, while having an impact also on the field of Natural Language Processing. Our work aims at shedding
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GLAVAŠ, GORAN, and JAN ŠNAJDER. "Construction and evaluation of event graphs." Natural Language Engineering 21, no. 4 (2014): 607–52. http://dx.doi.org/10.1017/s1351324914000060.

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AbstractEvents play an important role in natural language processing and information retrieval due to numerous event-oriented texts and information needs. Many natural language processing and information retrieval applications could benefit from a structured event-oriented document representation. In this paper, we proposeevent graphsas a novel way of structuring event-based information from text. Nodes in event graphs represent the individual mentions of events, whereas edges represent the temporal and coreference relations between mentions. Contrary to previous natural language processing re
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Bridgelall, Raj. "An Application of Natural Language Processing to Classify What Terrorists Say They Want." Social Sciences 11, no. 1 (2022): 23. http://dx.doi.org/10.3390/socsci11010023.

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Knowing what perpetrators want can inform strategies to achieve safe, secure, and sustainable societies. To help advance the body of knowledge in counterterrorism, this research applied natural language processing and machine learning techniques to a comprehensive database of terrorism events. A specially designed empirical topic modeling technique provided a machine-aided human decision process to glean six categories of perpetrator aims from the motive text narrative. Subsequently, six different machine learning models validated the aim categories based on the accuracy of their association w
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Psarologou, Adamantia, and Nikolaos Bourbakis. "Glossa — A Formal Language as a Mapping Mechanism of NL Sentences into SPN State Machine for Actions/Events Association." International Journal on Artificial Intelligence Tools 26, no. 02 (2017): 1750012. http://dx.doi.org/10.1142/s0218213017500129.

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Natural Language Understanding (NLU) is an old and really challenging field with a variety of research work published on it. In this paper we present a formal language methodology based on a state machine for efficiently representing natural language events/actions and their associations in well-written documents. The methodology consists of the following steps. We firstly apply Anaphora Resolution (AR) to the pre-processing natural language text. Then we extract the kernel(s) of each sentence. These kernels are formally represented using a formal language, (Glossa) to map the language express
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Wang, Chun Ping. "Design and Implementation of Network Events Monitoring System." Applied Mechanics and Materials 568-570 (June 2014): 1430–33. http://dx.doi.org/10.4028/www.scientific.net/amm.568-570.1430.

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The model of network monitoring system proposed in this paper, the use of user modeling techniques and event detection techniques. Preclude the use of dynamic modeling and dynamic model of the method of combining inferred more detailed user interest model to optimize the results, event detection method for the introduction of natural language processing, the system automatically send the text to identify hot topics and events advertising. In the system design, considering the intersection of the two, to obtain a better user experience.
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Bai, Sun, Zang, et al. "Identification Technology of Grid Monitoring Alarm Event Based on Natural Language Processing and Deep Learning in China." Energies 12, no. 17 (2019): 3258. http://dx.doi.org/10.3390/en12173258.

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Power dispatching systems currently receive massive, complicated, and irregular monitoring alarms during their operation, which prevents the controllers from making accurate judgments on the alarm events that occur within a short period of time. In view of the current situation with the low efficiency of monitoring alarm information, this paper proposes a method based on natural language processing (NLP) and a hybrid model that combines long short-term memory (LSTM) and convolutional neural network (CNN) for the identification of grid monitoring alarm events. Firstly, the characteristics of th
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Son, Ji-eun, and Yu-nam Cheong. "A study on the Korean event named entity for natural language processing." Journal of Yeongju Language & Literature 53 (October 31, 2022): 29–63. http://dx.doi.org/10.30774/yjll.2023.02.53.29.

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Rose, Rodrigo L., Tejas G. Puranik, and Dimitri N. Mavris. "Natural Language Processing Based Method for Clustering and Analysis of Aviation Safety Narratives." Aerospace 7, no. 10 (2020): 143. http://dx.doi.org/10.3390/aerospace7100143.

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The complexity of commercial aviation operations has grown substantially in recent years, together with a diversification of techniques for collecting and analyzing flight data. As a result, data-driven frameworks for enhancing flight safety have grown in popularity. Data-driven techniques offer efficient and repeatable exploration of patterns and anomalies in large datasets. Text-based flight safety data presents a unique challenge in its subjectivity, and relies on natural language processing tools to extract underlying trends from narratives. In this paper, a methodology is presented for th
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Murphy, Rachel M., Joanna E. Klopotowska, Nicolette F. de Keizer, et al. "Adverse drug event detection using natural language processing: A scoping review of supervised learning methods." PLOS ONE 18, no. 1 (2023): e0279842. http://dx.doi.org/10.1371/journal.pone.0279842.

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To reduce adverse drug events (ADEs), hospitals need a system to support them in monitoring ADE occurrence routinely, rapidly, and at scale. Natural language processing (NLP), a computerized approach to analyze text data, has shown promising results for the purpose of ADE detection in the context of pharmacovigilance. However, a detailed qualitative assessment and critical appraisal of NLP methods for ADE detection in the context of ADE monitoring in hospitals is lacking. Therefore, we have conducted a scoping review to close this knowledge gap, and to provide directions for future research an
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Zhao, Yiqing, Sunyang Fu, Suzette J. Bielinski, et al. "Natural Language Processing and Machine Learning for Identifying Incident Stroke From Electronic Health Records: Algorithm Development and Validation." Journal of Medical Internet Research 23, no. 3 (2021): e22951. http://dx.doi.org/10.2196/22951.

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Background Stroke is an important clinical outcome in cardiovascular research. However, the ascertainment of incident stroke is typically accomplished via time-consuming manual chart abstraction. Current phenotyping efforts using electronic health records for stroke focus on case ascertainment rather than incident disease, which requires knowledge of the temporal sequence of events. Objective The aim of this study was to develop a machine learning–based phenotyping algorithm for incident stroke ascertainment based on diagnosis codes, procedure codes, and clinical concepts extracted from clinic
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Mao, Jialin, Art Sedrakyan, Tianyi Sun, et al. "Assessing adverse event reports of hysteroscopic sterilization device removal using natural language processing." Pharmacoepidemiology and Drug Safety 31, no. 4 (2021): 442–51. http://dx.doi.org/10.1002/pds.5402.

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Nguyen, M., E. J. Woo, S. Winiecki, et al. "Can Natural Language Processing Improve the Efficiency of Vaccine Adverse Event Report Review?" Methods of Information in Medicine 55, no. 02 (2016): 144–50. http://dx.doi.org/10.3414/me14-01-0066.

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SummaryBackground: Individual case review of spontaneous adverse event (AE) reports remains a cornerstone of medical product safety surveil-lance for industry and regulators. Previously we developed the Vaccine Adverse Event Text Miner (VaeTM) to offer automated information extraction and potentially accelerate the evaluation of large volumes of unstructured data and facilitate signal detection.Objective: To assess how the information extraction performed by VaeTM impacts the accuracy of a medical expert’s review of the vaccine adverse event report.Methods: The “outcome of interest” (diagnosis
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Anand, Sarabjot Singh, Arshad Jhumka, and Kimberley Wade. "Towards the Ordering of Events from Multiple Textual Evidence Sources." International Journal of Digital Crime and Forensics 3, no. 2 (2011): 16–34. http://dx.doi.org/10.4018/jdcf.2011040102.

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In any criminal investigation, two important problems have to be addressed: (1) integration of multiple data sources to build a concise picture of the events leading up to and/or during the execution of a crime, and (2) determining the order in which these events occurred. This paper focuses on the integration of multiple textual data sources, each providing a recollection of events observed by eyewitnesses. From these textual documents, using text mining and natural language processing techniques the authors identify events (across the document corpus) associated with the crime and infer temp
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Chen, Long, Yu Gu, Xin Ji, et al. "Extracting medications and associated adverse drug events using a natural language processing system combining knowledge base and deep learning." Journal of the American Medical Informatics Association 27, no. 1 (2019): 56–64. http://dx.doi.org/10.1093/jamia/ocz141.

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Abstract Objective Detecting adverse drug events (ADEs) and medications related information in clinical notes is important for both hospital medical care and medical research. We describe our clinical natural language processing (NLP) system to automatically extract medical concepts and relations related to ADEs and medications from clinical narratives. This work was part of the 2018 National NLP Clinical Challenges Shared Task and Workshop on Adverse Drug Events and Medication Extraction. Materials and Methods The authors developed a hybrid clinical NLP system that employs a knowledge-based g
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Moon, Junhyung, Gyuyoung Park, and Jongpil Jeong. "POP-ON: Prediction of Process Using One-Way Language Model Based on NLP Approach." Applied Sciences 11, no. 2 (2021): 864. http://dx.doi.org/10.3390/app11020864.

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In business process management, the monitoring service is an important element that can prevent various problems in advance from before they occur in companies and industries. Execution log is created in an information system that is aware of the enterprise process, which helps predict the process. The ultimate goal of the proposed method is to predict the process following the running process instance and predict events based on previously completed event log data. Companies can flexibly respond to unwanted deviations in their workflow. When solving the next event prediction problem, we use a
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Schäfer, Martin. "Tagungsbericht / Conference report : “Wen wurmt der Ohrwurm? An Interdisciplinary, Cross-Lingual Perspective on the Role of Constituents in Multi-Word Expressions” (Workshop at the 39th DGfS Annual Conference ,,Information und sprachliche Kodierung“, Saarbrücken, 08.10.03.2017)." Zeitschrift für Wortbildung / Journal of Word Formation 1, no. 2 (2017): 91–97. http://dx.doi.org/10.3726/zwjw.2017.02.04.

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Abstract Organized by Sabine Schulte im Walde (University of Stuttgart) and Eva Smolka (University of Konstanz) as part of the 39th Annual Conference of the German Linguistic Society (DGfS) held at the Saarland University in Saarbrücken, Germany, the workshop aimed “to shed light on the interaction of constituent properties and compound transparency across languages and disciplines integrating linguistic, psycholinguistic, corpus-based and computational studies”. The workshop brought together researchers from linguistics, psycholinguistics, and natural language processing and comprised 11 cont
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Bashmal, Laila, Yakoub Bazi, Mohamad Mahmoud Al Rahhal, Mansour Zuair, and Farid Melgani. "CapERA: Captioning Events in Aerial Videos." Remote Sensing 15, no. 8 (2023): 2139. http://dx.doi.org/10.3390/rs15082139.

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In this paper, we introduce the CapERA dataset, which upgrades the Event Recognition in Aerial Videos (ERA) dataset to aerial video captioning. The newly proposed dataset aims to advance visual–language-understanding tasks for UAV videos by providing each video with diverse textual descriptions. To build the dataset, 2864 aerial videos are manually annotated with a caption that includes information such as the main event, object, place, action, numbers, and time. More captions are automatically generated from the manual annotation to take into account as much as possible the variation in descr
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Woller, Bela, Austin Daw, Valerie Aston, et al. "Natural Language Processing Performance for the Identification of Venous Thromboembolism in an Integrated Healthcare System." Clinical and Applied Thrombosis/Hemostasis 27 (January 1, 2021): 107602962110131. http://dx.doi.org/10.1177/10760296211013108.

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Real-time identification of venous thromboembolism (VTE), defined as deep vein thrombosis (DVT) and pulmonary embolism (PE), can inform a healthcare organization’s understanding of these events and be used to improve care. In a former publication, we reported the performance of an electronic medical record (EMR) interrogation tool that employs natural language processing (NLP) of imaging studies for the diagnosis of venous thromboembolism. Because we transitioned from the legacy electronic medical record to the Cerner product, iCentra, we now report the operating characteristics of the NLP EMR
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Buselli, Irene, Luca Oneto, Carlo Dambra, et al. "Natural language processing for aviation safety: extracting knowledge from publicly-available loss of separation reports." Open Research Europe 1 (September 23, 2021): 110. http://dx.doi.org/10.12688/openreseurope.14040.1.

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Background: The air traffic management (ATM) system has historically coped with a global increase in traffic demand ultimately leading to increased operational complexity. When dealing with the impact of this increasing complexity on system safety it is crucial to automatically analyse the loss of separation (LoS) using tools able to extract meaningful and actionable information from safety reports. Current research in this field mainly exploits natural language processing (NLP) to categorise the reports, with the limitations that the considered categories need to be manually annotated by expe
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Buselli, Irene, Luca Oneto, Carlo Dambra, et al. "Natural language processing for aviation safety: extracting knowledge from publicly-available loss of separation reports." Open Research Europe 1 (February 18, 2022): 110. http://dx.doi.org/10.12688/openreseurope.14040.2.

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Background: The air traffic management (ATM) system has historically coped with a global increase in traffic demand ultimately leading to increased operational complexity. When dealing with the impact of this increasing complexity on system safety it is crucial to automatically analyse the losses of separation (LoSs) using tools able to extract meaningful and actionable information from safety reports. Current research in this field mainly exploits natural language processing (NLP) to categorise the reports,with the limitations that the considered categories need to be manually annotated by ex
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Shahbazi, Zeinab, and Yung-Cheol Byun. "Blockchain-Based Event Detection and Trust Verification Using Natural Language Processing and Machine Learning." IEEE Access 10 (2022): 5790–800. http://dx.doi.org/10.1109/access.2021.3139586.

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Ma, Meng, Kyeryoung Lee, Yun Mai, et al. "Extracting longitudinal anticancer treatments at scale using deep natural language processing and temporal reasoning." Journal of Clinical Oncology 39, no. 15_suppl (2021): e18747-e18747. http://dx.doi.org/10.1200/jco.2021.39.15_suppl.e18747.

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e18747 Background: Accurate longitudinal cancer treatments are vital for establishing primary endpoints such as outcome as well as for the investigation of adverse events. However, many longitudinal therapeutic regimens are not well captured in structured electronic health records (EHRs). Thus, their recognition in unstructured data such as clinical notes is critical to gain an accurate description of the real-world patient treatment journey. Here, we demonstrate a scalable approach to extract high-quality longitudinal cancer treatments from lung cancer patients' clinical notes using a Bidirec
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Quaresma, Paulo, Vítor Beires Nogueira, Kashyap Raiyani, and Roy Bayot. "Event Extraction and Representation: A Case Study for the Portuguese Language." Information 10, no. 6 (2019): 205. http://dx.doi.org/10.3390/info10060205.

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Text information extraction is an important natural language processing (NLP) task, which aims to automatically identify, extract, and represent information from text. In this context, event extraction plays a relevant role, allowing actions, agents, objects, places, and time periods to be identified and represented. The extracted information can be represented by specialized ontologies, supporting knowledge-based reasoning and inference processes. In this work, we will describe, in detail, our proposal for event extraction from Portuguese documents. The proposed approach is based on a pipelin
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Vanegas, Jorge A., Sérgio Matos, Fabio González, and José L. Oliveira. "An Overview of Biomolecular Event Extraction from Scientific Documents." Computational and Mathematical Methods in Medicine 2015 (2015): 1–19. http://dx.doi.org/10.1155/2015/571381.

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This paper presents a review of state-of-the-art approaches to automatic extraction of biomolecular events from scientific texts. Events involving biomolecules such as genes, transcription factors, or enzymes, for example, have a central role in biological processes and functions and provide valuable information for describing physiological and pathogenesis mechanisms. Event extraction from biomedical literature has a broad range of applications, including support for information retrieval, knowledge summarization, and information extraction and discovery. However, automatic event extraction i
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Christanno, Ivan, Priscilla Priscilla, Jody Johansyah Maulana, Derwin Suhartono, and Rini Wongso. "Eve: An Automated Question Answering System for Events Information." ComTech: Computer, Mathematics and Engineering Applications 8, no. 1 (2017): 15. http://dx.doi.org/10.21512/comtech.v8i1.3781.

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The objective of this research was to create a closed-domain of automated question answering system specifically for events called Eve. Automated Question Answering System (QAS) is a system that accepts question input in the form of natural language. The question will be processed through modules to finally return the most appropriate answer to the corresponding question instead of returning a full document as an output. Thescope of the events was those which were organized by Students Association of Computer Science (HIMTI) in Bina Nusantara University. It consisted of 3 main modules namely q
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Senders, Joeky T., David J. Cote, Alireza Mehrtash, et al. "Deep learning for natural language processing of free-text pathology reports: a comparison of learning curves." BMJ Innovations 6, no. 4 (2020): 192–98. http://dx.doi.org/10.1136/bmjinnov-2019-000410.

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IntroductionAlthough clinically derived information could improve patient care, its full potential remains unrealised because most of it is stored in a format unsuitable for traditional methods of analysis, free-text clinical reports. Various studies have already demonstrated the utility of natural language processing algorithms for medical text analysis. Yet, evidence on their learning efficiency is still lacking. This study aimed to compare the learning curves of various algorithms and develop an open-source framework for text mining in healthcare.MethodsDeep learning and regressions-based m
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Agyeman, Nana Ama. "Documenting Simpa: Advances in language documentation." Legon Journal of the Humanities 30, no. 2 (2019): 167–90. http://dx.doi.org/10.4314/ljh.v30i2.8.

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Documentary linguistics, also known as language documentation, a relatively new branch of Linguistics, advocates for the fundamental need to collect records of language use and practices in various forms from diverse genres for multiple purposes. Such purposes include language description, language development, language maintenance, and language revitalisation. Such a record of a language serves to feed not only linguistic research but also research in other disciplines, such as anthropology, history, and ethnography. Language documentation is recognized as an ultimate response to language end
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Uspenskij, Mikhail B. "Log mining and knowledge-based models in data storage systems diagnostics." E3S Web of Conferences 140 (2019): 03006. http://dx.doi.org/10.1051/e3sconf/201914003006.

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Modern data storage systems have a sophisticated hardware and software architecture, including multiple storage processors, storage fabrics, network equipment and storage media and contain information, which can be damaged or lost because of hardware or software fault. Approach to storage software diagnostics, presented in current paper, combines a log mining algorithms for fault detection based on natural language processing text classification methods, and usage of the diagnostic model for a task of fault source detection. Currently existing approaches to computational systems diagnostics ar
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Abud, Abdi, and Damon E. Houghton. "Derivation and Validation of Natural Language Processing Algorithms to Identify and Classify Venous Thrombotic Events from Lower Extremity Duplex Ultrasound Reports." Blood 138, Supplement 1 (2021): 831. http://dx.doi.org/10.1182/blood-2021-144961.

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Abstract Background: The precise anatomic location and extent of most venous thromboembolism (VTE) is not captured through diagnostic codes and is a major limitation to research. Correctly identifying the specific site of thrombosis, whether proximal or distal deep vein thrombosis (DVT) or superficial vein thrombosis (SVT) is critical.An accurate natural language processing (NLP) tool would make analysis of large datasets from electronic medical records possible and could be an significant improvement compared to analyses using the International Classification of Diseases codes. Using an open-
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