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"

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KARTTUNEN, LAURI, KIMMO KOSKENNIEMI, and GERTJAN VAN NOORD. "Finite state methods in natural language processing." Natural Language Engineering 9, no. 1 (March 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 Computational Linguistics (Volume 26, Number 1). Inspired by these events, Lauri Karttunen, Kimmo Koskenniemi and Gertjan van Noord took the initiative for a workshop on finite state methods in NLP in Helsinki, as part of the European Summer School in Language, Logic and Information. As a related special event, the 20th anniversary of two-level morphology was celebrated. The appreciation of these events led us to believe that once again it should be possible, with some additional submissions, to compose an interesting special issue of this journal.
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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 (July 9, 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 event is proposed by combining BLSTM and conditional random field organically. Second, the Attention mechanism based on vocabulary in the field of food safety is introduced, the distance-related sequence semantic features are extracted by BLSTM, and the emotional classification of sequence semantic features is realized by using CNN. A kind of Att-BLSTM-CNN model for the analysis of public opinion and emotional tendency in the field of food safety is proposed. Finally, based on the time series, this article combines the role extraction of food safety events and the analysis of emotional tendency and constructs a net-mediated public opinion early warning model in the field of food safety according to the heat of the event and the emotional intensity of the public to food safety public opinion events.
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Ozonoff, Al, Carly E. Milliren, Kerri Fournier, Jennifer Welcher, Assaf Landschaft, Mihail Samnaliev, Mehmet Saluvan, Mark Waltzman, and Amir A. Kimia. "Electronic surveillance of patient safety events using natural language processing." Health Informatics Journal 28, no. 4 (October 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 this approach on peripheral intravenous (PIV) infiltrations and extravasations (PIVIEs). Results During Phase 1, we collected 21,362 clinical notes, of which 2342 were reviewed. We identified 125 PIV events, of which 44 cases (35%) were not captured by other patient safety systems. During Phase 2, we collected 60,735 clinical notes and identified 440 infiltrate events. Our classifier demonstrated accuracy above 90%. Conclusion Our method to identify safety events from the free text of clinical documentation offers a feasible and scalable approach to enhance existing patient safety systems. Expert reviewers, using a machine learning model, can conduct routine surveillance of patient safety events.
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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 (September 25, 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 graph is derived to show event-time relationships in the given text. Events form the nodes in a graph, and edges represent the temporal relations among the nodes. Time of an event occurrence exists in two forms namely qualitative (like before, after, duringetc) and quantitative (exact time points/periods). To build the event-time-event structure quantitative time is normalized to qualitative form. Thus obtained temporal information is used to label the edges among the events. Data set released in the shared task EvTExtract of (Forum for Information Retrieval Extraction) FIRE 2018 conference is identified to evaluate the framework. Precision and recall are used as evaluation metrics to access the performance of the proposed framework with other methods mentioned in state of the art with 85% of accuracy and 90% of precision.</p>
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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 (June 30, 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 processing approaches to identify false news specifically, false news items that come from unreliable sources. The dataset used here is ISOT dataset which contains the Real and Fake news collected from various sources. Web scraping is used here to extract the text from news website to collect the present news and is added into the dataset. Data pre-processing, feature extraction is applied on the data. It is followed by dimensionality reduction and classification using models such as Rocchio classification, Bagging classifier, Gradient Boosting classifier and Passive Aggressive classifier. To choose the best functioning model with an accurate prediction for fake news, we compared a number of algorithms.
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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 (January 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 (July 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 (March 21, 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 received a total of sixteen submissions, some of which were extended and updated versions of workshop papers, and others which were completely new. The final selection, consisting of only seven papers that could fit into one issue, is not fully representative, but complements the prior special issues in a nice way. The selected papers showcase a few areas where finite-state methods have less than obvious and sometimes even groundbreaking relevance to natural language processing (NLP) applications.
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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 (November 20, 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 event it describes relevant, we developed a natural language processing framework for automated information extraction and relevance scoring. First, we scraped relevant sources for EBS as done at the RKI (WHO Disease Outbreak News and ProMED) and automatically extracted the articles’ key data: disease, country, date, and confirmed-case count. For this, we performed named entity recognition in two steps: EpiTator, an open-source epidemiological annotation tool, suggested many different possibilities for each. We extracted the key country and disease using a heuristic with good results. We trained a naive Bayes classifier to find the key date and confirmed-case count, using the RKI’s EBS database as labels which performed modestly. Then, for relevance scoring, we defined two classes to which any article might belong: The article is relevant if it is in the EBS database and irrelevant otherwise. We compared the performance of different classifiers, using bag-of-words, document and word embeddings. The best classifier, a logistic regression, achieved a sensitivity of 0.82 and an index balanced accuracy of 0.61. Finally, we integrated these functionalities into a web application called EventEpi where relevant sources are automatically analyzed and put into a database. The user can also provide any URL or text, that will be analyzed in the same way and added to the database. Each of these steps could be improved, in particular with larger labeled datasets and fine-tuning of the learning algorithms. The overall framework, however, works already well and can be used in production, promising improvements in EBS. The source code and data are publicly available under open licenses.
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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 (December 28, 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 classification models of whether textual information is pro-Ukrainian or pro-Russian were built based on a dataset containing messages from social media users about the events of the large-scale Russian invasion of Ukraine from February 24, 2022.The problem was solved with the help of Support Vector Machines, Decision Tree, Random Forest, Naïve Bayes classifier,eXtreme Gradient BoostingandLogistic Regressionmachine learning algo-rithms, Convolutional Neural Networks, Long short-term memory and BERT neural networks, techniques for working with unbal-anced dataRandom Oversampling, Random Undersampling , SMOTE and SMOTETomek, as well as stacking ensembles of models.Amongthe machine learning algorithms, LR performed best, showing a macro F1-scorevalue of 0.7966 when features were trans-formed by TF-IDF vectorization and 0.7933 when BoW.Among neural networks, the best macro F1-scorevalue of 0.76was ob-tained using CNN and LSTM.Applying data balancing techniques failed to improve the results of machine learning algorithms.Next, ensembles of models from machine learning algorithms were determined. Two of the constructed ensembles achieved the same macro F1-scorevalue of 0.7966 as with LR. Ensembles that wasable to do so consisted of the TF-IDF vectorization, the B-NBC meta-model, and the SVC, NuSVC LR, and SVC, LR base models, respectively.Thus, three classifiers, the LR machine learning algorithmand two ensembles of models, which were defined as a combination of existing methods of solving the problem, demon-strated the largest macro F1-score value of 0.7966. The obtained models can be used for a detailed review of various news publica-tions according to the political leaning characteristic, information about which can help people identify being isolated by a filter bubble.
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Dissertations / Theses on the topic "Events in natural language processing"

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Patil, Supritha Basavaraj. "Analysis of Moving Events Using Tweets." Thesis, Virginia Tech, 2019. http://hdl.handle.net/10919/90884.

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The Digital Library Research Laboratory (DLRL) has collected over 3.5 billion tweets on different events for the Coordinated, Behaviorally-Aware Recovery for Transportation and Power Disruptions (CBAR-tpd), the Integrated Digital Event Archiving and Library (IDEAL), and the Global Event Trend Archive Research (GETAR) projects. The tweet collection topics include heart attack, solar eclipse, terrorism, etc. There are several collections on naturally occurring events such as hurricanes, floods, and solar eclipses. Such naturally occurring events are distributed across space and time. It would be beneficial to researchers if we can perform a spatial-temporal analysis to test some hypotheses, and to find any trends that tweets would reveal for such events. I apply an existing algorithm to detect locations from tweets by modifying it to work better with the type of datasets I work with. I use the time captured in tweets and also identify the tense of the sentences in tweets to perform the temporal analysis. I build a rule-based model for obtaining the tense of a tweet. The results from these two algorithms are merged to analyze naturally occurring moving events such as solar eclipses and hurricanes. Using the spatial-temporal information from tweets, I study if tweets can be a relevant source of information in understanding the movement of the event. I create visualizations to compare the actual path of the event with the information extracted by my algorithms. After examining the results from the analysis, I noted that Twitter can be a reliable source to identify places affected by moving events almost immediately. The locations obtained are at a more detailed level than in news-wires. We can also identify the time that an event affected a particular region by date.
Master of Science
News now travels faster on social media than through news channels. Information from social media can help retrieve minute details that might not be emphasized in news. People tend to describe their actions or sentiments in tweets. I aim at studying if such collections of tweets are dependable sources for identifying paths of moving events. In events like hurricanes, using Twitter can help in analyzing people’s reaction to such moving events. These may include actions such as dislocation or emotions during different phases of the event. The results obtained in the experiments concur with the actual path of the events with respect to the regions affected and time. The frequency of tweets increases during event peaks. The number of locations affected that are identified are significantly more than in news wires.
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Huang, Yin Jou. "Event Centric Approaches in Natural Language Processing." Doctoral thesis, Kyoto University, 2021. http://hdl.handle.net/2433/265210.

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Nothman, Joel. "Grounding event references in news." Thesis, The University of Sydney, 2013. http://hdl.handle.net/2123/10609.

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Events are frequently discussed in natural language, and their accurate identification is central to language understanding. Yet they are diverse and complex in ontology and reference; computational processing hence proves challenging. News provides a shared basis for communication by reporting events. We perform several studies into news event reference. One annotation study characterises each news report in terms of its update and topic events, but finds that topic is better consider through explicit references to background events. In this context, we propose the event linking task which—analogous to named entity linking or disambiguation—models the grounding of references to notable events. It defines the disambiguation of an event reference as a link to the archival article that first reports it. When two references are linked to the same article, they need not be references to the same event. Event linking hopes to provide an intuitive approximation to coreference, erring on the side of over-generation in contrast with the literature. The task is also distinguished in considering event references from multiple perspectives over time. We diagnostically evaluate the task by first linking references to past, newsworthy events in news and opinion pieces to an archive of the Sydney Morning Herald. The intensive annotation results in only a small corpus of 229 distinct links. However, we observe that a number of hyperlinks targeting online news correspond to event links. We thus acquire two large corpora of hyperlinks at very low cost. From these we learn weights for temporal and term overlap features in a retrieval system. These noisy data lead to significant performance gains over a bag-of-words baseline. While our initial system can accurately predict many event links, most will require deep linguistic processing for their disambiguation.
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Lindén, Johannes. "Huvudtitel: Understand and Utilise Unformatted Text Documents by Natural Language Processing algorithms." Thesis, Mittuniversitetet, Avdelningen för informationssystem och -teknologi, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:miun:diva-31043.

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News companies have a need to automate and make the editors process of writing about hot and new events more effective. Current technologies involve robotic programs that fills in values in templates and website listeners that notifies the editors when changes are made so that the editor can read up on the source change at the actual website. Editors can provide news faster and better if directly provided with abstracts of the external sources. This study applies deep learning algorithms to automatically formulate abstracts and tag sources with appropriate tags based on the context. The study is a full stack solution, which manages both the editors need for speed and the training, testing and validation of the algorithms. Decision Tree, Random Forest, Multi Layer Perceptron and phrase document vectors are used to evaluate the categorisation and Recurrent Neural Networks is used to paraphrase unformatted texts. In the evaluation a comparison between different models trained by the algorithms with a variation of parameters are done based on the F-score. The results shows that the F-scores are increasing the more document the training has and decreasing the more categories the algorithm needs to consider. The Multi-Layer Perceptron perform best followed by Random Forest and finally Decision Tree. The document length matters, when larger documents are considered during training the score is increasing considerably. A user survey about the paraphrase algorithms shows the paraphrase result is insufficient to satisfy editors need. It confirms a need for more memory to conduct longer experiments.
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Sanagavarapu, Krishna Chaitanya. "Determining Whether and When People Participate in the Events They Tweet About." Thesis, University of North Texas, 2017. https://digital.library.unt.edu/ark:/67531/metadc984235/.

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This work describes an approach to determine whether people participate in the events they tweet about. Specifically, we determine whether people are participants in events with respect to the tweet timestamp. We target all events expressed by verbs in tweets, including past, present and events that may occur in future. We define event participant as people directly involved in an event regardless of whether they are the agent, recipient or play another role. We present an annotation effort, guidelines and quality analysis with 1,096 event mentions. We discuss the label distributions and event behavior in the annotated corpus. We also explain several features used and a standard supervised machine learning approach to automatically determine if and when the author is a participant of the event in the tweet. We discuss trends in the results obtained and devise important conclusions.
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Sakaguchi, Tomohiro. "Anchoring Events to the Time Axis toward Storyline Construction." Kyoto University, 2019. http://hdl.handle.net/2433/242437.

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付記する学位プログラム名: デザイン学大学院連携プログラム
Kyoto University (京都大学)
0048
新制・課程博士
博士(情報学)
甲第21912号
情博第695号
新制||情||119(附属図書館)
京都大学大学院情報学研究科知能情報学専攻
(主査)教授 黒橋 禎夫, 教授 西田 豊明, 教授 楠見 孝
学位規則第4条第1項該当
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Baier, Thomas, Ciccio Claudio Di, Jan Mendling, and Mathias Weske. "Matching events and activities by integrating behavioral aspects and label analysis." Springer Berlin Heidelberg, 2018. http://dx.doi.org/10.1007/s10270-017-0603-z.

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Nowadays, business processes are increasingly supported by IT services that produce massive amounts of event data during the execution of a process. These event data can be used to analyze the process using process mining techniques to discover the real process, measure conformance to a given process model, or to enhance existing models with performance information. Mapping the produced events to activities of a given process model is essential for conformance checking, annotation and understanding of process mining results. In order to accomplish this mapping with low manual effort, we developed a semi-automatic approach that maps events to activities using insights from behavioral analysis and label analysis. The approach extracts Declare constraints from both the log and the model to build matching constraints to efficiently reduce the number of possible mappings. These mappings are further reduced using techniques from natural language processing, which allow for a matching based on labels and external knowledge sources. The evaluation with synthetic and real-life data demonstrates the effectiveness of the approach and its robustness toward non-conforming execution logs.
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Mills, Michael Thomas. "Natural Language Document and Event Association Using Stochastic Petri Net Modeling." Wright State University / OhioLINK, 2013. http://rave.ohiolink.edu/etdc/view?acc_num=wright1369408524.

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Mehta, Sneha. "Towards Explainable Event Detection and Extraction." Diss., Virginia Tech, 2021. http://hdl.handle.net/10919/104359.

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Event extraction refers to extracting specific knowledge of incidents from natural language text and consolidating it into a structured form. Some important applications of event extraction include search, retrieval, question answering and event forecasting. However, before events can be extracted it is imperative to detect events i.e. identify which documents from a large collection contain events of interest and from those extracting the sentences that might contain the event related information. This task is challenging because it is easier to obtain labels at the document level than finegrained annotations at the sentence level. Current approaches for this task are suboptimal because they directly aggregate sentence probabilities estimated by a classifier to obtain document probabilities resulting in error propagation. To alleviate this problem we propose to leverage recent advances in representation learning by using attention mechanisms. Specifically, for event detection we propose a method to compute document embeddings from sentence embeddings by leveraging attention and training a document classifier on those embeddings to mitigate the error propagation problem. However, we find that existing attention mechanisms are inept for this task, because either they are suboptimal or they use a large number of parameters. To address this problem we propose a lean attention mechanism which is effective for event detection. Current approaches for event extraction rely on finegrained labels in specific domains. Extending extraction to new domains is challenging because of difficulty of collecting finegrained data. Machine reading comprehension(MRC) based approaches, that enable zero-shot extraction struggle with syntactically complex sentences and long-range dependencies. To mitigate this problem, we propose a syntactic sentence simplification approach that is guided by MRC model to improve its performance on event extraction.
Doctor of Philosophy
Event extraction is the task of extracting events of societal importance from natural language texts. The task has a wide range of applications from search, retrieval, question answering to forecasting population level events like civil unrest, disease occurrences with reasonable accuracy. Before events can be extracted it is imperative to identify the documents that are likely to contain the events of interest and extract the sentences that mention those events. This is termed as event detection. Current approaches for event detection are suboptimal. They assume that events are neatly partitioned into sentences and obtain document level event probabilities directly from predicted sentence level probabilities. In this dissertation, under the same assumption by leveraging representation learning we mitigate some of the shortcomings of the previous event detection methods. Current approaches to event extraction are only limited to restricted domains and require finegrained labeled corpora for their training. One way to extend event extraction to new domains in by enabling zero-shot extraction. Machine reading comprehension(MRC) based approach provides a promising way forward for zero-shot extraction. However, this approach suffers from the long-range dependency problem and faces difficulty in handling syntactically complex sentences with multiple clauses. To mitigate this problem we propose a syntactic sentence simplification algorithm that is guided by the MRC system to improves its performance.
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Veladas, Rute Gomes. "Classificação automática de eventos na linha de saúde SNS24." Master's thesis, Universidade de Évora, 2021. http://hdl.handle.net/10174/29055.

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Nesta dissertação apresentamos uma nova ferramenta de suporte à decisão a ser implementada no Serviço de Triagem, Aconselhamento e Encaminhamento (TAE) do Centro de Contacto do Serviço Nacional de Saúde - SNS24. Atualmente a seleção do algoritmo clínico mais adequado a cada situação é efetuada manualmente pelo enfermeiro que atende a chamada. Esta seleção deve ser feita de entre um conjunto de 59 algoritmos clínicos, sendo que esta implementação vem responder à necessidade de reduzir a duração das chamadas recebidas pela linha e consequentemente maximizar o número de chamadas atendidas por unidade de tempo. Este será um modelo baseado em metodologias de Inteligência Artificial, com foco em abordagens de Aprendizagem automática e Processamento de língua natural. O modelo apresentado representa o modelo inicial que foi desenvolvido com um conjunto de dados com os registos de três meses de chamadas, equivalente a cerca de 270.000 registos, mas o modelo final será futuramente desenvolvido a partir de um conjunto de dados com cerca de 4 milhões de chamadas registadas ao longo de três anos pela linha de saúde. O modelo inicial permitiu atingir uma exatidão de 78,80% e medida-F de 78,45% para a classificação da classe do top 1, enquanto que a classificação para o top 3 e top 5 de classes atingiu valores de exatidão superiores a 90%; Abstract: Automatic event classification on the health phone line SNS24 In this dissertation we present a new decision support tool to be implemented in the Screening, Counseling and Referral Service (TAE) of the Contact Center of the National Health Service - SNS24. Currently, the selection of the most appropriate clinical algorithm for each situation is done manually by the nurse who answers the call. This selection must be made from a set of 59 clinical algorithms, and this implementation responds to the need to reduce the duration of calls received by the line and consequently maximize the number of calls answered per unit of time. This will be a model based on Artificial Intelligence methodologies, focusing on Machine Learning and Natural Language Processing approaches. The model presented represents the initial model that was developed with a set of data with the records of three months of calls, equivalent to about 270.000 records, but the final model will be developed in the future from a data set with about 4 million calls registered over three years by the health line. The initial model reached an accuracy of 78.80% and F-measure of 78.45% for the classification of the top 1 class, while the classification for the top 3 and top 5 classes reached values of accuracy greater than 90%.
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Books on the topic "Events in natural language processing"

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Frank, Schilder, Katz Graham, and Pustejovsky J, eds. Annotating, extracting and reasoning about time and events: International seminar, Dagstuhl Castle, Germany, April 10-15, 2005 : revised papers. Berlin: Springer, 2007.

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Peterson, Philip L. Fact proposition event. Dordrecht: Kluwer Academic Publishers, 1997.

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1961-, Allan James, ed. Topic detection and tracking: Event-based information organization. Boston: Kluwer Academic Publishers, 2002.

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Filgueiras, M., L. Damas, N. Moreira, and A. P. Tomás, eds. Natural Language Processing. Berlin, Heidelberg: Springer Berlin Heidelberg, 1991. http://dx.doi.org/10.1007/3-540-53678-7.

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Natural language processing. Oxford, OX: Blackwell Scientific Publications, 1988.

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Topic Detection and Tracking: Event-based Information Organization. Boston, MA: Springer US, 2002.

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Kulkarni, Akshay, and Adarsha Shivananda. Natural Language Processing Recipes. Berkeley, CA: Apress, 2021. http://dx.doi.org/10.1007/978-1-4842-7351-7.

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Kulkarni, Akshay, Adarsha Shivananda, and Anoosh Kulkarni. Natural Language Processing Projects. Berkeley, CA: Apress, 2022. http://dx.doi.org/10.1007/978-1-4842-7386-9.

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Søgaard, Anders. Explainable Natural Language Processing. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-031-02180-0.

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Tapsai, Chalermpol, Herwig Unger, and Phayung Meesad. Thai Natural Language Processing. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-56235-9.

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Book chapters on the topic "Events in natural language processing"

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Jean-Louis, Ludovic, Romaric Besançon, and Olivier Ferret. "Using Temporal Cues for Segmenting Texts into Events." In Advances in Natural Language Processing, 150–61. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-14770-8_18.

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Vanetik, Natalia, Marina Litvak, and Efi Levi. "Real-World Events Discovering with TWIST." In Natural Language Processing for Electronic Design Automation, 71–107. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-52273-5_4.

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Ma, Xiao, Elnaz Davoodi, Leila Kosseim, and Nicandro Scarabeo. "Semantic Mapping of Security Events to Known Attack Patterns." In Natural Language Processing and Information Systems, 91–98. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-91947-8_10.

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Hürriyetoǧlu, Ali, Nelleke Oostdijk, Mustafa Erkan Başar, and Antal van den Bosch. "Supporting Experts to Handle Tweet Collections About Significant Events." In Natural Language Processing and Information Systems, 138–41. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-59569-6_14.

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Hürriyetoǧlu, Ali, Nelleke Oostdijk, and Antal van den Bosch. "Estimating Time to Event of Future Events Based on Linguistic Cues on Twitter." In Intelligent Natural Language Processing: Trends and Applications, 67–97. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-67056-0_5.

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Barik, Biswanath, Erwin Marsi, and Pinar Öztürk. "Extracting Causal Relations Among Complex Events in Natural Science Literature." In Natural Language Processing and Information Systems, 131–37. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-59569-6_13.

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Filatova, Elena, and Vasileios Hatzivassiloglou. "Marking atomic events in sets of related texts." In Recent Advances in Natural Language Processing III, 247. Amsterdam: John Benjamins Publishing Company, 2004. http://dx.doi.org/10.1075/cilt.260.27fil.

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Tsolmon, Bayar, A.-Rong Kwon, and Kyung-Soon Lee. "Extracting Social Events Based on Timeline and Sentiment Analysis in Twitter Corpus." In Natural Language Processing and Information Systems, 265–70. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-31178-9_32.

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Arnulphy, Béatrice, Vincent Claveau, Xavier Tannier, and Anne Vilnat. "Supervised Machine Learning Techniques to Detect TimeML Events in French and English." In Natural Language Processing and Information Systems, 19–32. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-19581-0_2.

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Loukachevitch, Natalia, and Boris Dobrov. "RuThes Thesaurus for Natural Language Processing." In The Palgrave Handbook of Digital Russia Studies, 319–34. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-42855-6_18.

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AbstractThis chapter describes the Russian RuThes thesaurus created as a linguistic and terminological resource for automatic document processing. Its structure utilizes two popular paradigms for computer thesauri: concept-based units, a small set of relation types, rules for including multiword expression as in information retrieval thesauri; and language-motivated units, detailed sets of synonyms, description of ambiguous words as in WordNet-like thesauri. The development of the RuThes thesaurus is supported for many years: new concepts, new senses, and multiword expressions found in contemporary texts are introduced regularly. The chapter shows some examples of representing newly appeared concepts related to important internal and international events.
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Conference papers on the topic "Events in natural language processing"

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Chen, Muhao, Hongming Zhang, Qiang Ning, Manling Li, Heng Ji, Kathleen McKeown, and Dan Roth. "Event-Centric Natural Language Processing." In Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing: Tutorial Abstracts. Stroudsburg, PA, USA: Association for Computational Linguistics, 2021. http://dx.doi.org/10.18653/v1/2021.acl-tutorials.2.

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Velichkov, Boris, Ivan Koychev, and Svetla Boytcheva. "Deep Learning Contextual Models for Prediction of Sport Events Outcome from Sportsmen Interviews." In Recent Advances in Natural Language Processing. Incoma Ltd., Shoumen, Bulgaria, 2019. http://dx.doi.org/10.26615/978-954-452-056-4_142.

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"Computing Implicit Entities and Events with Getaruns." In International Workshop on Natural Language Processing and Cognitive Science. SciTePress - Science and and Technology Publications, 2009. http://dx.doi.org/10.5220/0002171600230035.

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Zhang, Zheng, Tianjun Hou, Josselin Kherroubi, and Daria Khvostichenko. "Event Detection in Drilling Remarks Using Natural Language Processing." In IADC/SPE International Drilling Conference and Exhibition. SPE, 2022. http://dx.doi.org/10.2118/208779-ms.

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Abstract Remarks in daily drilling reports (DDR) and daily mud reports (DMR) are an invaluable source of information for the analysis of ongoing operations and planning of future wells. These remarks are entered as free text and thus represent unstructured information, which requires expert knowledge for interpretation. Here, we aim to develop a machine learning algorithm to automatically detect events of interest and convert this information into a structured format. Several unscheduled events, such as losses, influx and stuck pipe, were selected to develop a prototype of our natural language processing (NLP) approach for daily DMR remarks. Data selection, data annotation and analysis workflow, and results are discussed in the manuscript.
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Loukachevitch, Natalia, Ekaterina Artemova, Tatiana Batura, Pavel Braslavski, Ilia Denisov, Vladimir Ivanov, Suresh Manandhar, Alexander Pugachev, and Elena Tutubalina. "NEREL: A Russian Dataset with Nested Named Entities, Relations and Events." In International Conference Recent Advances in Natural Language Processing. INCOMA Ltd. Shoumen, BULGARIA, 2021. http://dx.doi.org/10.26615/978-954-452-072-4_100.

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Choubey, Prafulla Kumar, and Ruihong Huang. "Event Coreference Resolution by Iteratively Unfolding Inter-dependencies among Events." In Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing. Stroudsburg, PA, USA: Association for Computational Linguistics, 2017. http://dx.doi.org/10.18653/v1/d17-1226.

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Hu, Hangping, Zhen Zhang, Weijian Qin, Yuan Wang, and Xiaojian Li. "A Survey of Cloud Service Events and Their Connections." In 8th International Conference on Natural Language Processing (NATP 2022). Academy and Industry Research Collaboration Center (AIRCC), 2022. http://dx.doi.org/10.5121/csit.2022.120108.

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Any unexpected service interruption or failure may cause customer dissatisfaction or economic losses. To distinguish the rights and interests or security disputes between cloud service providers and customers, explore the essence and rules of cloud service events and their various connections, such as: Normal contact of service scheduling, normal contact of service dependence, abnormal contact of resource competition, abnormal contact of service delay, abnormal contact of service dependence, etc., as well as their rules in time, resources, scheduling and other aspects, and the form of the rules; The purpose is to provide the above abnormal connections, as well as the rule and presentation form in terms of time, resources and load, for the study of violation determination and failure tracing in the cloud service accountability mechanism.
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Xie, Xi Hai, and Le Chen. "Analysis of Sentiment Tendency Based on Major Public Health Events." In 2022 4th International Conference on Natural Language Processing (ICNLP). IEEE, 2022. http://dx.doi.org/10.1109/icnlp55136.2022.00093.

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Yang, Erhong, Qingqing Zeng, and Danqing Zhu. "Analysis about event annotation and information structure in sudden events discourse." In 2009 International Conference on Natural Language Processing and Knowledge Engineering (NLP-KE). IEEE, 2009. http://dx.doi.org/10.1109/nlpke.2009.5313778.

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Chaney, Allison, Hanna Wallach, Matthew Connelly, and David Blei. "Detecting and Characterizing Events." In Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing. Stroudsburg, PA, USA: Association for Computational Linguistics, 2016. http://dx.doi.org/10.18653/v1/d16-1122.

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Reports on the topic "Events in natural language processing"

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DANIELSON, THOMAS. NATURAL LANGUAGE PROCESSING FOR TEXT- BASED EVENT EXTRACTION: IDENTIFYING EVENTS OF INTEREST RELATED TO WORLDWIDE STATE-SPONSORED CIVIL NUCLEAR POWER. Office of Scientific and Technical Information (OSTI), March 2023. http://dx.doi.org/10.2172/1962589.

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Steedman, Mark. Natural Language Processing. Fort Belvoir, VA: Defense Technical Information Center, June 1994. http://dx.doi.org/10.21236/ada290396.

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Leavy, Michelle B., Danielle Cooke, Sarah Hajjar, Erik Bikelman, Bailey Egan, Diana Clarke, Debbie Gibson, Barbara Casanova, and Richard Gliklich. Outcome Measure Harmonization and Data Infrastructure for Patient-Centered Outcomes Research in Depression: Report on Registry Configuration. Agency for Healthcare Research and Quality (AHRQ), November 2020. http://dx.doi.org/10.23970/ahrqepcregistryoutcome.

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Background: Major depressive disorder is a common mental disorder. Many pressing questions regarding depression treatment and outcomes exist, and new, efficient research approaches are necessary to address them. The primary objective of this project is to demonstrate the feasibility and value of capturing the harmonized depression outcome measures in the clinical workflow and submitting these data to different registries. Secondary objectives include demonstrating the feasibility of using these data for patient-centered outcomes research and developing a toolkit to support registries interested in sharing data with external researchers. Methods: The harmonized outcome measures for depression were developed through a multi-stakeholder, consensus-based process supported by AHRQ. For this implementation effort, the PRIME Registry, sponsored by the American Board of Family Medicine, and PsychPRO, sponsored by the American Psychiatric Association, each recruited 10 pilot sites from existing registry sites, added the harmonized measures to the registry platform, and submitted the project for institutional review board review Results: The process of preparing each registry to calculate the harmonized measures produced three major findings. First, some clarifications were necessary to make the harmonized definitions operational. Second, some data necessary for the measures are not routinely captured in structured form (e.g., PHQ-9 item 9, adverse events, suicide ideation and behavior, and mortality data). Finally, capture of the PHQ-9 requires operational and technical modifications. The next phase of this project will focus collection of the baseline and follow-up PHQ-9s, as well as other supporting clinical documentation. In parallel to the data collection process, the project team will examine the feasibility of using natural language processing to extract information on PHQ-9 scores, adverse events, and suicidal behaviors from unstructured data. Conclusion: This pilot project represents the first practical implementation of the harmonized outcome measures for depression. Initial results indicate that it is feasible to calculate the measures within the two patient registries, although some challenges were encountered related to the harmonized definition specifications, the availability of the necessary data, and the clinical workflow for collecting the PHQ-9. The ongoing data collection period, combined with an evaluation of the utility of natural language processing for these measures, will produce more information about the practical challenges, value, and burden of using the harmonized measures in the primary care and mental health setting. These findings will be useful to inform future implementations of the harmonized depression outcome measures.
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Tratz, Stephen C. Arabic Natural Language Processing System Code Library. Fort Belvoir, VA: Defense Technical Information Center, June 2014. http://dx.doi.org/10.21236/ada603814.

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Wilks, Yorick, Michael Coombs, Roger T. Hartley, and Dihong Qiu. Active Knowledge Structures for Natural Language Processing. Fort Belvoir, VA: Defense Technical Information Center, January 1991. http://dx.doi.org/10.21236/ada245893.

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Firpo, M. Natural Language Processing as a Discipline at LLNL. Office of Scientific and Technical Information (OSTI), February 2005. http://dx.doi.org/10.2172/15015192.

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Anderson, Thomas. State of the Art of Natural Language Processing. Fort Belvoir, VA: Defense Technical Information Center, November 1987. http://dx.doi.org/10.21236/ada188112.

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Hobbs, Jerry R., Douglas E. Appelt, John Bear, Mabry Tyson, and David Magerman. Robust Processing of Real-World Natural-Language Texts. Fort Belvoir, VA: Defense Technical Information Center, January 1991. http://dx.doi.org/10.21236/ada258837.

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Neal, Jeannette G., Elissa L. Feit, Douglas J. Funke, and Christine A. Montgomery. An Evaluation Methodology for Natural Language Processing Systems. Fort Belvoir, VA: Defense Technical Information Center, December 1992. http://dx.doi.org/10.21236/ada263301.

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Lehnert, Wendy G. Using Case-Based Reasoning in Natural Language Processing. Fort Belvoir, VA: Defense Technical Information Center, June 1993. http://dx.doi.org/10.21236/ada273538.

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