Literatura científica selecionada sobre o tema "Clinical Natural Language Processing"
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Artigos de revistas sobre o assunto "Clinical Natural Language Processing"
K Gautam, Leena. "Natural Language Processing - Based Structured Data Extraction from Unstructured Clinical Notes". International Journal of Science and Research (IJSR) 13, n.º 4 (5 de abril de 2024): 1541–44. http://dx.doi.org/10.21275/sr24422134801.
Texto completo da fonteYandell, Mark D., e William H. Majoros. "Genomics and natural language processing". Nature Reviews Genetics 3, n.º 8 (agosto de 2002): 601–10. http://dx.doi.org/10.1038/nrg861.
Texto completo da fonteWare, H., C. J. Mullett e V. Jagannathan. "Natural Language Processing Framework to Assess Clinical Conditions". Journal of the American Medical Informatics Association 16, n.º 4 (1 de julho de 2009): 585–89. http://dx.doi.org/10.1197/jamia.m3091.
Texto completo da fonteSager, N., M. Lyman, C. Bucknall, N. Nhan e L. J. Tick. "Natural Language Processing and the Representation of Clinical Data". Journal of the American Medical Informatics Association 1, n.º 2 (1 de março de 1994): 142–60. http://dx.doi.org/10.1136/jamia.1994.95236145.
Texto completo da fonteFriedman, C., G. Hripcsak, W. DuMouchel, S. B. Johnson e P. D. Clayton. "Natural language processing in an operational clinical information system". Natural Language Engineering 1, n.º 1 (março de 1995): 83–108. http://dx.doi.org/10.1017/s1351324900000061.
Texto completo da fonteDenny, Joshua C., Lisa Bastarache, Elizabeth Ann Sastre e Anderson Spickard. "Tracking medical students’ clinical experiences using natural language processing". Journal of Biomedical Informatics 42, n.º 5 (outubro de 2009): 781–89. http://dx.doi.org/10.1016/j.jbi.2009.02.004.
Texto completo da fonteWu, Stephen T., Vinod C. Kaggal, Dmitriy Dligach, James J. Masanz, Pei Chen, Lee Becker, Wendy W. Chapman, Guergana K. Savova, Hongfang Liu e Christopher G. Chute. "A common type system for clinical natural language processing". Journal of Biomedical Semantics 4, n.º 1 (2013): 1. http://dx.doi.org/10.1186/2041-1480-4-1.
Texto completo da fonteLegnar, Maximilian, Philipp Daumke, Jürgen Hesser, Stefan Porubsky, Zoran Popovic, Jan Niklas Bindzus, Joern-Helge Heinrich Siemoneit e Cleo-Aron Weis. "Natural Language Processing in Diagnostic Texts from Nephropathology". Diagnostics 12, n.º 7 (15 de julho de 2022): 1726. http://dx.doi.org/10.3390/diagnostics12071726.
Texto completo da fonteMateussi, Nadayca, Michael P. Rogers, Emily A. Grimsley, Meagan Read, Rajavi Parikh, Ricardo Pietrobon e Paul C. Kuo. "Clinical Applications of Machine Learning". Annals of Surgery Open 5, n.º 2 (18 de abril de 2024): e423. http://dx.doi.org/10.1097/as9.0000000000000423.
Texto completo da fonteda Rocha, Naila Camila, Abner Macola Pacheco Barbosa, Yaron Oliveira Schnr, Juliana Machado-Rugolo, Luis Gustavo Modelli de Andrade, José Eduardo Corrente e Liciana Vaz de Arruda Silveira. "Natural Language Processing to Extract Information from Portuguese-Language Medical Records". Data 8, n.º 1 (29 de dezembro de 2022): 11. http://dx.doi.org/10.3390/data8010011.
Texto completo da fonteTeses / dissertações sobre o assunto "Clinical Natural Language Processing"
Chien, Isabel. "Natural language processing for precision clinical diagnostics and treatment". Thesis, Massachusetts Institute of Technology, 2018. http://hdl.handle.net/1721.1/119754.
Texto completo da fonteThis electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.
Cataloged from student-submitted PDF version of thesis.
Includes bibliographical references (pages 61-65).
In this thesis, I focus upon application of natural language processing to clinical diagnostics and treatment within the palliative care and serious illness field. I explore a variety of natural language processing methods, including deep learning, rule-based, and classic machine learning, and applied to the identication of documentation reflecting advanced care planning measures, serious illnesses, and serious illness symptoms. I introduce two tools that can be used to analyze clinical notes from electronic health records: ClinicalRegex, a regular expression interface, and PyCCI, an a clinical text annotation tool. Additionally, I discuss a palliative care-focused research project in which I apply machine learning natural language processing methods to identifying clinical documentation in the palliative care and serious illness field. Advance care planning, which includes clarifying and documenting goals of care and preferences for future care, is essential for achieving end-of-life care that is consistent with the preferences of dying patients and their families. Physicians document their communication about these preferences as unstructured free text in clinical notes; as a result, routine assessment of this quality indicator is time consuming and costly. Integrating goals of care conversations and advance care planning into decision-making about palliative surgery have been shown to result in less invasive care near the time of death and improve clinical outcomes for both the patient and surviving family members. Natural language processing methods offer an efficient and scalable way to improve the visibility of documented serious illness conversations within electronic health record data, helping to better quality of care.
by Isabel Chien.
M. Eng.
Mehrabi, Saeed. "Advanced natural language processing and temporal mining for clinical discovery". Thesis, Indiana University - Purdue University Indianapolis, 2016. http://pqdtopen.proquest.com/#viewpdf?dispub=10032405.
Texto completo da fonteThere has been vast and growing amount of healthcare data especially with the rapid adoption of electronic health records (EHRs) as a result of the HITECH act of 2009. It is estimated that around 80% of the clinical information resides in the unstructured narrative of an EHR. Recently, natural language processing (NLP) techniques have offered opportunities to extract information from unstructured clinical texts needed for various clinical applications. A popular method for enabling secondary uses of EHRs is information or concept extraction, a subtask of NLP that seeks to locate and classify elements within text based on the context. Extraction of clinical concepts without considering the context has many complications, including inaccurate diagnosis of patients and contamination of study cohorts. Identifying the negation status and whether a clinical concept belongs to patients or his family members are two of the challenges faced in context detection. A negation algorithm called Dependency Parser Negation (DEEPEN) has been developed in this research study by taking into account the dependency relationship between negation words and concepts within a sentence using the Stanford Dependency Parser. The study results demonstrate that DEEPEN, can reduce the number of incorrect negation assignment for patients with positive findings, and therefore improve the identification of patients with the target clinical findings in EHRs. Additionally, an NLP system consisting of section segmentation and relation discovery was developed to identify patients’ family history. To assess the generalizability of the negation and family history algorithm, data from a different clinical institution was used in both algorithm evaluations. The temporal dimension of extracted information from clinical records representing the trajectory of disease progression in patients was also studied in this project. Clinical data of patients who lived in Olmsted County (Rochester, MN) during 1966 to 2010 was analyzed in this work. The patient records were modeled by diagnosis matrices with clinical events as rows and their temporal information as columns. Deep learning algorithm was used to find common temporal patterns within these diagnosis matrices.
Forsyth, Alexander William. "Improving clinical decision making with natural language processing and machine learning". Thesis, Massachusetts Institute of Technology, 2017. http://hdl.handle.net/1721.1/112847.
Texto completo da fonteThis electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.
Cataloged from student-submitted PDF version of thesis.
Includes bibliographical references (pages 49-53).
This thesis focused on two tasks of applying natural language processing (NLP) and machine learning to electronic health records (EHRs) to improve clinical decision making. The first task was to predict cardiac resynchronization therapy (CRT) outcomes with better precision than the current physician guidelines for recommending the procedure. We combined NLP features from free-text physician notes with structured data to train a supervised classifier to predict CRT outcomes. While our results gave a slight improvement over the current baseline, we were not able to predict CRT outcome with both high precision and high recall. These results limit the clinical applicability of our model, and reinforce previous work, which also could not find accurate predictors of CRT response. The second task in this thesis was to extract breast cancer patient symptoms during chemotherapy from free-text physician notes. We manually annotated about 10,000 sentences, and trained a conditional random field (CRF) model to predict whether a word indicated a symptom (positive label), specifically indicated the absence of a symptom (negative label), or was neutral. Our final model achieved 0.66, 1.00, and 0.77 F1 scores for predicting positive, neutral, and negative labels respectively. While the F1 scores for positive and negative labels are not extremely high, with the current performance, our model could be applied, for example, to gather better statistics about what symptoms breast cancer patients experience during chemotherapy and at what time points during treatment they experience these symptoms.
by Alexander William Forsyth.
M. Eng.
Regulapati, Sushmitha. "Natural language processing framework to assist in the evaluation of adherence to clinical guidelines". Morgantown, W. Va. : [West Virginia University Libraries], 2007. https://eidr.wvu.edu/etd/documentdata.eTD?documentid=5340.
Texto completo da fonteTitle from document title page. Document formatted into pages; contains vii, 36 p. : ill. (some col.). Includes abstract. Includes bibliographical references (p. 33-36).
Leonhard, Annette Christa. "Automated question answering for clinical comparison questions". Thesis, University of Edinburgh, 2012. http://hdl.handle.net/1842/6266.
Texto completo da fonteEglowski, Skylar. "CREATE: Clinical Record Analysis Technology Ensemble". DigitalCommons@CalPoly, 2017. https://digitalcommons.calpoly.edu/theses/1771.
Texto completo da fonteWang, Yefeng. "Information extraction from clinical notes". Thesis, The University of Sydney, 2010. https://hdl.handle.net/2123/28844.
Texto completo da fonteHenriksson, Aron. "Semantic Spaces of Clinical Text : Leveraging Distributional Semantics for Natural Language Processing of Electronic Health Records". Licentiate thesis, Stockholms universitet, Institutionen för data- och systemvetenskap, 2013. http://urn.kb.se/resolve?urn=urn:nbn:se:su:diva-94344.
Texto completo da fonteDe stora mängder kliniska data som genereras i patientjournalsystem är en underutnyttjad resurs med en enorm potential att förbättra hälso- och sjukvården. Då merparten av kliniska data är i form av ostrukturerad text, vilken är utmanande för datorer att analysera, finns det ett behov av sofistikerade metoder som kan behandla kliniskt språk. Metoder som inte kräver märkta exempel utan istället utnyttjar statistiska egenskaper i datamängden är särskilt värdefulla, med tanke på den begränsade tillgången till annoterade korpusar i den kliniska domänen. System för informationsextraktion och språkbehandling behöver innehålla viss kunskap om semantik. En metod går ut på att utnyttja de distributionella egenskaperna hos språk – mer specifikt, statistisk över hur termer samförekommer – för att modellera den relativa betydelsen av termer i ett högdimensionellt vektorrum. Metoden har använts med framgång i en rad uppgifter för behandling av allmänna språk; dess tillämpning i den kliniska domänen har dock endast utforskats i mindre utsträckning. Genom att tillämpa modeller för distributionell semantik på klinisk text kan semantiska rum konstrueras utan någon tillgång till märkta exempel. Semantiska rum av klinisk text kan sedan användas i en rad medicinskt relevanta tillämpningar. Tillämpningen av distributionell semantik i den kliniska domänen illustreras här i tre användningsområden: (1) synonymextraktion av medicinska termer, (2) tilldelning av diagnoskoder och (3) identifiering av läkemedelsbiverkningar. Det krävs dock att vissa begränsningar eller utmaningar adresseras för att möjliggöra en effektiv tillämpning av distributionell semantik på ett brett spektrum av uppgifter som behandlar språk – både allmänt och, i synnerhet, kliniskt – såsom hur man kan modellera betydelsen av flerordstermer och redogöra för funktionen av negation: ett enkelt sätt att modellera parafrasering och negation i ett distributionellt semantiskt ramverk presenteras och utvärderas. Idén om ensembler av semantisk rum introduceras också; dessa överträffer användningen av ett enda semantiskt rum för synonymextraktion. Den här metoden möjliggör en kombination av olika modeller för distributionell semantik, med olika parameterkonfigurationer samt inducerade från olika korpusar. Detta är inte minst viktigt i den kliniska domänen, då det gör det möjligt att komplettera potentiellt begränsade mängder kliniska data med data från andra, mer lättillgängliga källor. Arbetet påvisar också vikten av att konfigurera dimensionaliteten av semantiska rum, i synnerhet när vokabulären är omfattande, vilket är vanligt i den kliniska domänen.
High-Performance Data Mining for Drug Effect Detection (DADEL)
Khizra, Shufa. "Using Natural Language Processing and Machine Learning for Analyzing Clinical Notes in Sickle Cell Disease Patients". Wright State University / OhioLINK, 2018. http://rave.ohiolink.edu/etdc/view?acc_num=wright154759374321405.
Texto completo da fonteIslam, Mohammed Ashrafull. "Enhancing the interactivity of a clinical decision support system by using knowledge engineering and natural language processing". Thesis, Aston University, 2018. http://publications.aston.ac.uk/37540/.
Texto completo da fonteLivros sobre o assunto "Clinical Natural Language Processing"
Guo, Shuli, Lina Han e Wentao Yang. Clinical Chinese Named Entity Recognition in Natural Language Processing. Singapore: Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-2665-7.
Texto completo da fonteIdnay, Betina Ross Saldua. Improving Eligibility Prescreening for Alzheimer’s Disease and Related Dementias Clinical Trials with Natural Language Processing. [New York, N.Y.?]: [publisher not identified], 2022.
Encontre o texto completo da fonteFilgueiras, M., L. Damas, N. Moreira e A. P. Tomás, eds. Natural Language Processing. Berlin, Heidelberg: Springer Berlin Heidelberg, 1991. http://dx.doi.org/10.1007/3-540-53678-7.
Texto completo da fonteNatural language processing. Oxford, OX: Blackwell Scientific Publications, 1988.
Encontre o texto completo da fonteLee, Raymond S. T. Natural Language Processing. Singapore: Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-99-1999-4.
Texto completo da fonteKulkarni, Akshay, e Adarsha Shivananda. Natural Language Processing Recipes. Berkeley, CA: Apress, 2021. http://dx.doi.org/10.1007/978-1-4842-7351-7.
Texto completo da fonteKulkarni, Akshay, Adarsha Shivananda e Anoosh Kulkarni. Natural Language Processing Projects. Berkeley, CA: Apress, 2022. http://dx.doi.org/10.1007/978-1-4842-7386-9.
Texto completo da fonteSøgaard, Anders. Explainable Natural Language Processing. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-031-02180-0.
Texto completo da fonteTapsai, Chalermpol, Herwig Unger e Phayung Meesad. Thai Natural Language Processing. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-56235-9.
Texto completo da fonteOflazer, Kemal, e Murat Saraçlar, eds. Turkish Natural Language Processing. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-90165-7.
Texto completo da fonteCapítulos de livros sobre o assunto "Clinical Natural Language Processing"
Khose, Bhasha, e Yashodhara V. Haribhakta. "Clinical NLP for Drug Safety". In Natural Language Processing in Healthcare, 159–88. Boca Raton: CRC Press, 2022. http://dx.doi.org/10.1201/9781003138013-8.
Texto completo da fonteHasan, Sadid A., e Oladimeji Farri. "Clinical Natural Language Processing with Deep Learning". In Data Science for Healthcare, 147–71. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-05249-2_5.
Texto completo da fonteBaruah, Rupjyoti, e Anil Kumar Singh. "A Clinical Practice by Machine Translation on Low Resource Languages". In Natural Language Processing in Healthcare, 1–17. Boca Raton: CRC Press, 2022. http://dx.doi.org/10.1201/9781003138013-1.
Texto completo da fonteHarik, Polina, Janet Mee, Christopher Runyon e Brian E. Clauser. "Assessment of Clinical Skills". In Advancing Natural Language Processing in Educational Assessment, 58–73. New York: Routledge, 2023. http://dx.doi.org/10.4324/9781003278658-5.
Texto completo da fonteDehghan, Azad, Tom Liptrot, Daniel Tibble, Matthew Barker-Hewitt e Goran Nenadic. "Identification of Occupation Mentions in Clinical Narratives". In Natural Language Processing and Information Systems, 359–65. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-41754-7_35.
Texto completo da fonteLonsdale, Deryle, Clint Tustison, Craig Parker e David W. Embley. "Formulating Queries for Assessing Clinical Trial Eligibility". In Natural Language Processing and Information Systems, 82–93. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11765448_8.
Texto completo da fonteCeli, Leo Anthony, Christina Chen, Daniel Gruhl, Chaitanya Shivade e Joy Tzung-Yu Wu. "Introduction to Clinical Natural Language Processing with Python". In Leveraging Data Science for Global Health, 229–50. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-47994-7_14.
Texto completo da fonteGangavarapu, Tushaar, Aditya Jayasimha, Gokul S. Krishnan e Sowmya Kamath S. "TAGS: Towards Automated Classification of Unstructured Clinical Nursing Notes". In Natural Language Processing and Information Systems, 195–207. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-23281-8_16.
Texto completo da fonteKaiser, Katharina, e Silvia Miksch. "Supporting the Abstraction of Clinical Practice Guidelines Using Information Extraction". In Natural Language Processing and Information Systems, 304–11. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-13881-2_32.
Texto completo da fonteSoto, Xabier, Olatz Perez-de-Viñaspre, Maite Oronoz e Gorka Labaka. "Development of a Machine Translation System for Promoting the Use of a Low Resource Language in the Clinical Domain: The Case of Basque". In Natural Language Processing in Healthcare, 139–58. Boca Raton: CRC Press, 2022. http://dx.doi.org/10.1201/9781003138013-7.
Texto completo da fonteTrabalhos de conferências sobre o assunto "Clinical Natural Language Processing"
Arias, Juan F. "Natural Language Processing for Clinical Quality Measures". In 2023 IEEE 36th International Symposium on Computer-Based Medical Systems (CBMS). IEEE, 2023. http://dx.doi.org/10.1109/cbms58004.2023.00255.
Texto completo da fonteRojas, Matías, Jocelyn Dunstan e Fabián Villena. "Clinical Flair: A Pre-Trained Language Model for Spanish Clinical Natural Language Processing". In Proceedings of the 4th Clinical Natural Language Processing Workshop. Stroudsburg, PA, USA: Association for Computational Linguistics, 2022. http://dx.doi.org/10.18653/v1/2022.clinicalnlp-1.9.
Texto completo da fonteTrivedi, Gaurav. "Towards Interactive Natural Language Processing in Clinical Care". In 2018 IEEE International Conference on Healthcare Informatics (ICHI). IEEE, 2018. http://dx.doi.org/10.1109/ichi.2018.00096.
Texto completo da fonteTrivedi, Gaurav. "Clinical Text Analysis Using Interactive Natural Language Processing". In IUI'15: IUI'15 20th International Conference on Intelligent User Interfaces. New York, NY, USA: ACM, 2015. http://dx.doi.org/10.1145/2732158.2732162.
Texto completo da fonteBoytcheva, Svetla, Galia Angelova e Zhivko Angelov. "Risk Factors Extraction from Clinical Texts based on Linked Open Data". In Recent Advances in Natural Language Processing. Incoma Ltd., Shoumen, Bulgaria, 2019. http://dx.doi.org/10.26615/978-954-452-056-4_019.
Texto completo da fonteLiu, Ming, Richard Beare, Taya Collyer, Nadine Andrew e Velandai Srikanth. "Leveraging Natural Language Processing and Clinical Notes for Dementia Detection". In Proceedings of the 5th Clinical Natural Language Processing Workshop. Stroudsburg, PA, USA: Association for Computational Linguistics, 2023. http://dx.doi.org/10.18653/v1/2023.clinicalnlp-1.20.
Texto completo da fonteAlsentzer, Emily, John Murphy, William Boag, Wei-Hung Weng, Di Jindi, Tristan Naumann e Matthew McDermott. "Publicly Available Clinical". In Proceedings of the 2nd Clinical Natural Language Processing Workshop. Stroudsburg, PA, USA: Association for Computational Linguistics, 2019. http://dx.doi.org/10.18653/v1/w19-1909.
Texto completo da fonteHuang, Kexin, Abhishek Singh, Sitong Chen, Edward Moseley, Chih-Ying Deng, Naomi George e Charolotta Lindvall. "Clinical XLNet: Modeling Sequential Clinical Notes and Predicting Prolonged Mechanical Ventilation". In Proceedings of the 3rd Clinical Natural Language Processing Workshop. Stroudsburg, PA, USA: Association for Computational Linguistics, 2020. http://dx.doi.org/10.18653/v1/2020.clinicalnlp-1.11.
Texto completo da fonteShor, Joel, Ruyue Agnes Bi, Subhashini Venugopalan, Steven Ibara, Roman Goldenberg e Ehud Rivlin. "Clinical BERTScore: An Improved Measure of Automatic Speech Recognition Performance in Clinical Settings". In Proceedings of the 5th Clinical Natural Language Processing Workshop. Stroudsburg, PA, USA: Association for Computational Linguistics, 2023. http://dx.doi.org/10.18653/v1/2023.clinicalnlp-1.1.
Texto completo da fonteWang, Yanshan, Ahmad Tafti, Sunghwan Sohn e Rui Zhang. "Applications of Natural Language Processing in Clinical Research and Practice". In Proceedings of the 2019 Conference of the North. Stroudsburg, PA, USA: Association for Computational Linguistics, 2019. http://dx.doi.org/10.18653/v1/n19-5006.
Texto completo da fonteRelatórios de organizações sobre o assunto "Clinical Natural Language Processing"
Leavy, Michelle B., Danielle Cooke, Sarah Hajjar, Erik Bikelman, Bailey Egan, Diana Clarke, Debbie Gibson, Barbara Casanova e 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), novembro de 2020. http://dx.doi.org/10.23970/ahrqepcregistryoutcome.
Texto completo da fonteSteedman, Mark. Natural Language Processing. Fort Belvoir, VA: Defense Technical Information Center, junho de 1994. http://dx.doi.org/10.21236/ada290396.
Texto completo da fonteTratz, Stephen C. Arabic Natural Language Processing System Code Library. Fort Belvoir, VA: Defense Technical Information Center, junho de 2014. http://dx.doi.org/10.21236/ada603814.
Texto completo da fonteWilks, Yorick, Michael Coombs, Roger T. Hartley e Dihong Qiu. Active Knowledge Structures for Natural Language Processing. Fort Belvoir, VA: Defense Technical Information Center, janeiro de 1991. http://dx.doi.org/10.21236/ada245893.
Texto completo da fonteFirpo, M. Natural Language Processing as a Discipline at LLNL. Office of Scientific and Technical Information (OSTI), fevereiro de 2005. http://dx.doi.org/10.2172/15015192.
Texto completo da fonteAnderson, Thomas. State of the Art of Natural Language Processing. Fort Belvoir, VA: Defense Technical Information Center, novembro de 1987. http://dx.doi.org/10.21236/ada188112.
Texto completo da fonteHobbs, Jerry R., Douglas E. Appelt, John Bear, Mabry Tyson e David Magerman. Robust Processing of Real-World Natural-Language Texts. Fort Belvoir, VA: Defense Technical Information Center, janeiro de 1991. http://dx.doi.org/10.21236/ada258837.
Texto completo da fonteNeal, Jeannette G., Elissa L. Feit, Douglas J. Funke e Christine A. Montgomery. An Evaluation Methodology for Natural Language Processing Systems. Fort Belvoir, VA: Defense Technical Information Center, dezembro de 1992. http://dx.doi.org/10.21236/ada263301.
Texto completo da fonteLehnert, Wendy G. Using Case-Based Reasoning in Natural Language Processing. Fort Belvoir, VA: Defense Technical Information Center, junho de 1993. http://dx.doi.org/10.21236/ada273538.
Texto completo da fonteWallace, Byron C. Sociolinguistically Informed Natural Language Processing: Automating Irony Detection. Fort Belvoir, VA: Defense Technical Information Center, abril de 2015. http://dx.doi.org/10.21236/ada623456.
Texto completo da fonte