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Artykuły w czasopismach na temat "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, nr 4 (5.04.2024): 1541–44. http://dx.doi.org/10.21275/sr24422134801.
Pełny tekst źródłaYandell, Mark D., i William H. Majoros. "Genomics and natural language processing". Nature Reviews Genetics 3, nr 8 (sierpień 2002): 601–10. http://dx.doi.org/10.1038/nrg861.
Pełny tekst źródłaWare, H., C. J. Mullett i V. Jagannathan. "Natural Language Processing Framework to Assess Clinical Conditions". Journal of the American Medical Informatics Association 16, nr 4 (1.07.2009): 585–89. http://dx.doi.org/10.1197/jamia.m3091.
Pełny tekst źródłaSager, N., M. Lyman, C. Bucknall, N. Nhan i L. J. Tick. "Natural Language Processing and the Representation of Clinical Data". Journal of the American Medical Informatics Association 1, nr 2 (1.03.1994): 142–60. http://dx.doi.org/10.1136/jamia.1994.95236145.
Pełny tekst źródłaFriedman, C., G. Hripcsak, W. DuMouchel, S. B. Johnson i P. D. Clayton. "Natural language processing in an operational clinical information system". Natural Language Engineering 1, nr 1 (marzec 1995): 83–108. http://dx.doi.org/10.1017/s1351324900000061.
Pełny tekst źródłaDenny, Joshua C., Lisa Bastarache, Elizabeth Ann Sastre i Anderson Spickard. "Tracking medical students’ clinical experiences using natural language processing". Journal of Biomedical Informatics 42, nr 5 (październik 2009): 781–89. http://dx.doi.org/10.1016/j.jbi.2009.02.004.
Pełny tekst źródłaWu, Stephen T., Vinod C. Kaggal, Dmitriy Dligach, James J. Masanz, Pei Chen, Lee Becker, Wendy W. Chapman, Guergana K. Savova, Hongfang Liu i Christopher G. Chute. "A common type system for clinical natural language processing". Journal of Biomedical Semantics 4, nr 1 (2013): 1. http://dx.doi.org/10.1186/2041-1480-4-1.
Pełny tekst źródłaLegnar, Maximilian, Philipp Daumke, Jürgen Hesser, Stefan Porubsky, Zoran Popovic, Jan Niklas Bindzus, Joern-Helge Heinrich Siemoneit i Cleo-Aron Weis. "Natural Language Processing in Diagnostic Texts from Nephropathology". Diagnostics 12, nr 7 (15.07.2022): 1726. http://dx.doi.org/10.3390/diagnostics12071726.
Pełny tekst źródłaMateussi, Nadayca, Michael P. Rogers, Emily A. Grimsley, Meagan Read, Rajavi Parikh, Ricardo Pietrobon i Paul C. Kuo. "Clinical Applications of Machine Learning". Annals of Surgery Open 5, nr 2 (18.04.2024): e423. http://dx.doi.org/10.1097/as9.0000000000000423.
Pełny tekst źródłada Rocha, Naila Camila, Abner Macola Pacheco Barbosa, Yaron Oliveira Schnr, Juliana Machado-Rugolo, Luis Gustavo Modelli de Andrade, José Eduardo Corrente i Liciana Vaz de Arruda Silveira. "Natural Language Processing to Extract Information from Portuguese-Language Medical Records". Data 8, nr 1 (29.12.2022): 11. http://dx.doi.org/10.3390/data8010011.
Pełny tekst źródłaRozprawy doktorskie na temat "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.
Pełny tekst źródłaThis 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.
Pełny tekst źródłaThere 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.
Pełny tekst źródłaThis 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.
Pełny tekst źródłaTitle 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.
Pełny tekst źródłaEglowski, Skylar. "CREATE: Clinical Record Analysis Technology Ensemble". DigitalCommons@CalPoly, 2017. https://digitalcommons.calpoly.edu/theses/1771.
Pełny tekst źródłaWang, Yefeng. "Information extraction from clinical notes". Thesis, The University of Sydney, 2010. https://hdl.handle.net/2123/28844.
Pełny tekst źródłaHenriksson, 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.
Pełny tekst źródłaDe 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.
Pełny tekst źródłaIslam, 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/.
Pełny tekst źródłaKsiążki na temat "Clinical Natural Language Processing"
Guo, Shuli, Lina Han i 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.
Pełny tekst źródłaIdnay, 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.
Znajdź pełny tekst źródłaFilgueiras, M., L. Damas, N. Moreira i A. P. Tomás, red. Natural Language Processing. Berlin, Heidelberg: Springer Berlin Heidelberg, 1991. http://dx.doi.org/10.1007/3-540-53678-7.
Pełny tekst źródłaNatural language processing. Oxford, OX: Blackwell Scientific Publications, 1988.
Znajdź pełny tekst źródłaLee, Raymond S. T. Natural Language Processing. Singapore: Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-99-1999-4.
Pełny tekst źródłaKulkarni, Akshay, i Adarsha Shivananda. Natural Language Processing Recipes. Berkeley, CA: Apress, 2021. http://dx.doi.org/10.1007/978-1-4842-7351-7.
Pełny tekst źródłaKulkarni, Akshay, Adarsha Shivananda i Anoosh Kulkarni. Natural Language Processing Projects. Berkeley, CA: Apress, 2022. http://dx.doi.org/10.1007/978-1-4842-7386-9.
Pełny tekst źródłaSøgaard, Anders. Explainable Natural Language Processing. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-031-02180-0.
Pełny tekst źródłaTapsai, Chalermpol, Herwig Unger i Phayung Meesad. Thai Natural Language Processing. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-56235-9.
Pełny tekst źródłaOflazer, Kemal, i Murat Saraçlar, red. Turkish Natural Language Processing. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-90165-7.
Pełny tekst źródłaCzęści książek na temat "Clinical Natural Language Processing"
Khose, Bhasha, i Yashodhara V. Haribhakta. "Clinical NLP for Drug Safety". W Natural Language Processing in Healthcare, 159–88. Boca Raton: CRC Press, 2022. http://dx.doi.org/10.1201/9781003138013-8.
Pełny tekst źródłaHasan, Sadid A., i Oladimeji Farri. "Clinical Natural Language Processing with Deep Learning". W Data Science for Healthcare, 147–71. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-05249-2_5.
Pełny tekst źródłaBaruah, Rupjyoti, i Anil Kumar Singh. "A Clinical Practice by Machine Translation on Low Resource Languages". W Natural Language Processing in Healthcare, 1–17. Boca Raton: CRC Press, 2022. http://dx.doi.org/10.1201/9781003138013-1.
Pełny tekst źródłaHarik, Polina, Janet Mee, Christopher Runyon i Brian E. Clauser. "Assessment of Clinical Skills". W Advancing Natural Language Processing in Educational Assessment, 58–73. New York: Routledge, 2023. http://dx.doi.org/10.4324/9781003278658-5.
Pełny tekst źródłaDehghan, Azad, Tom Liptrot, Daniel Tibble, Matthew Barker-Hewitt i Goran Nenadic. "Identification of Occupation Mentions in Clinical Narratives". W 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.
Pełny tekst źródłaLonsdale, Deryle, Clint Tustison, Craig Parker i David W. Embley. "Formulating Queries for Assessing Clinical Trial Eligibility". W Natural Language Processing and Information Systems, 82–93. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11765448_8.
Pełny tekst źródłaCeli, Leo Anthony, Christina Chen, Daniel Gruhl, Chaitanya Shivade i Joy Tzung-Yu Wu. "Introduction to Clinical Natural Language Processing with Python". W 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.
Pełny tekst źródłaGangavarapu, Tushaar, Aditya Jayasimha, Gokul S. Krishnan i Sowmya Kamath S. "TAGS: Towards Automated Classification of Unstructured Clinical Nursing Notes". W 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.
Pełny tekst źródłaKaiser, Katharina, i Silvia Miksch. "Supporting the Abstraction of Clinical Practice Guidelines Using Information Extraction". W 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.
Pełny tekst źródłaSoto, Xabier, Olatz Perez-de-Viñaspre, Maite Oronoz i 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". W Natural Language Processing in Healthcare, 139–58. Boca Raton: CRC Press, 2022. http://dx.doi.org/10.1201/9781003138013-7.
Pełny tekst źródłaStreszczenia konferencji na temat "Clinical Natural Language Processing"
Arias, Juan F. "Natural Language Processing for Clinical Quality Measures". W 2023 IEEE 36th International Symposium on Computer-Based Medical Systems (CBMS). IEEE, 2023. http://dx.doi.org/10.1109/cbms58004.2023.00255.
Pełny tekst źródłaRojas, Matías, Jocelyn Dunstan i Fabián Villena. "Clinical Flair: A Pre-Trained Language Model for Spanish Clinical Natural Language Processing". W 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.
Pełny tekst źródłaTrivedi, Gaurav. "Towards Interactive Natural Language Processing in Clinical Care". W 2018 IEEE International Conference on Healthcare Informatics (ICHI). IEEE, 2018. http://dx.doi.org/10.1109/ichi.2018.00096.
Pełny tekst źródłaTrivedi, Gaurav. "Clinical Text Analysis Using Interactive Natural Language Processing". W 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.
Pełny tekst źródłaBoytcheva, Svetla, Galia Angelova i Zhivko Angelov. "Risk Factors Extraction from Clinical Texts based on Linked Open Data". W Recent Advances in Natural Language Processing. Incoma Ltd., Shoumen, Bulgaria, 2019. http://dx.doi.org/10.26615/978-954-452-056-4_019.
Pełny tekst źródłaLiu, Ming, Richard Beare, Taya Collyer, Nadine Andrew i Velandai Srikanth. "Leveraging Natural Language Processing and Clinical Notes for Dementia Detection". W 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.
Pełny tekst źródłaAlsentzer, Emily, John Murphy, William Boag, Wei-Hung Weng, Di Jindi, Tristan Naumann i Matthew McDermott. "Publicly Available Clinical". W 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.
Pełny tekst źródłaHuang, Kexin, Abhishek Singh, Sitong Chen, Edward Moseley, Chih-Ying Deng, Naomi George i Charolotta Lindvall. "Clinical XLNet: Modeling Sequential Clinical Notes and Predicting Prolonged Mechanical Ventilation". W 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.
Pełny tekst źródłaShor, Joel, Ruyue Agnes Bi, Subhashini Venugopalan, Steven Ibara, Roman Goldenberg i Ehud Rivlin. "Clinical BERTScore: An Improved Measure of Automatic Speech Recognition Performance in Clinical Settings". W 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.
Pełny tekst źródłaWang, Yanshan, Ahmad Tafti, Sunghwan Sohn i Rui Zhang. "Applications of Natural Language Processing in Clinical Research and Practice". W 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.
Pełny tekst źródłaRaporty organizacyjne na temat "Clinical Natural Language Processing"
Leavy, Michelle B., Danielle Cooke, Sarah Hajjar, Erik Bikelman, Bailey Egan, Diana Clarke, Debbie Gibson, Barbara Casanova i 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), listopad 2020. http://dx.doi.org/10.23970/ahrqepcregistryoutcome.
Pełny tekst źródłaSteedman, Mark. Natural Language Processing. Fort Belvoir, VA: Defense Technical Information Center, czerwiec 1994. http://dx.doi.org/10.21236/ada290396.
Pełny tekst źródłaTratz, Stephen C. Arabic Natural Language Processing System Code Library. Fort Belvoir, VA: Defense Technical Information Center, czerwiec 2014. http://dx.doi.org/10.21236/ada603814.
Pełny tekst źródłaWilks, Yorick, Michael Coombs, Roger T. Hartley i Dihong Qiu. Active Knowledge Structures for Natural Language Processing. Fort Belvoir, VA: Defense Technical Information Center, styczeń 1991. http://dx.doi.org/10.21236/ada245893.
Pełny tekst źródłaFirpo, M. Natural Language Processing as a Discipline at LLNL. Office of Scientific and Technical Information (OSTI), luty 2005. http://dx.doi.org/10.2172/15015192.
Pełny tekst źródłaAnderson, Thomas. State of the Art of Natural Language Processing. Fort Belvoir, VA: Defense Technical Information Center, listopad 1987. http://dx.doi.org/10.21236/ada188112.
Pełny tekst źródłaHobbs, Jerry R., Douglas E. Appelt, John Bear, Mabry Tyson i David Magerman. Robust Processing of Real-World Natural-Language Texts. Fort Belvoir, VA: Defense Technical Information Center, styczeń 1991. http://dx.doi.org/10.21236/ada258837.
Pełny tekst źródłaNeal, Jeannette G., Elissa L. Feit, Douglas J. Funke i Christine A. Montgomery. An Evaluation Methodology for Natural Language Processing Systems. Fort Belvoir, VA: Defense Technical Information Center, grudzień 1992. http://dx.doi.org/10.21236/ada263301.
Pełny tekst źródłaLehnert, Wendy G. Using Case-Based Reasoning in Natural Language Processing. Fort Belvoir, VA: Defense Technical Information Center, czerwiec 1993. http://dx.doi.org/10.21236/ada273538.
Pełny tekst źródłaWallace, Byron C. Sociolinguistically Informed Natural Language Processing: Automating Irony Detection. Fort Belvoir, VA: Defense Technical Information Center, kwiecień 2015. http://dx.doi.org/10.21236/ada623456.
Pełny tekst źródła