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Добірка наукової літератури з теми "Text"

Автор: Grafiati

Опубліковано: 4 червня 2021

Оновлено: 1 серпня 2024

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Статті в журналах з теми "Text"

1

LOW, G. D. "FROM TEXT TO TEST." World Englishes 4, no. 1 (March 1985): 180–83. http://dx.doi.org/10.1111/j.1467-971x.1985.tb00400.x.

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2

Reddy, Mr B. Ravinder, J. Nandini, and P. Sowmya Y. Sathwik. "Handwritten Text Recognition and Digital Text Conversion." International Journal of Trend in Scientific Research and Development Volume-3, Issue-3 (April 30, 2019): 1826–27. http://dx.doi.org/10.31142/ijtsrd23508.

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3

Theunissen, T. J. J. M. "Text banking and test design." Language Testing 4, no. 1 (June 1987): 1–8. http://dx.doi.org/10.1177/026553228700400101.

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4

Cope, Bill, and Mary Kalantzis. "Text-Made Text." E-Learning and Digital Media 1, no. 2 (June 2004): 198–282. http://dx.doi.org/10.2304/elea.2004.1.2.4.

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5

Vössing, J., C. Stamov-Roßnagel, and K. Heinitz. "Text difficulty affects metacomprehension accuracy and knowledge test performance in text learning." Journal of Computer Assisted Learning 33, no. 3 (March 19, 2017): 282–91. http://dx.doi.org/10.1111/jcal.12179.

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6

Mohammed Ali, Kawthar, and Diaa Abdul Razzaq Alani. "THE CHARACTER BETWEEN READING TEXT AND VISUAL TEXT(2000-2017)." Journal of The University of Duhok 22, no. 1 (November 25, 2019): 358–68. http://dx.doi.org/10.26682/hjuod.2019.22.1.19.

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7

Sundberg, Norman D. "A Text for Future Test Users." Contemporary Psychology: A Journal of Reviews 30, no. 3 (March 1985): 203–4. http://dx.doi.org/10.1037/023632.

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8

Sarangi, Srikant. "Editorial: Text & Talk." Text - Interdisciplinary Journal for the Study of Discourse 25, no. 1 (January 1, 2005): 1–5. http://dx.doi.org/10.1515/text.2005.25.1.1.

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9

Kim, Tatyana S. "Opposition Text Structure." International Journal of Psychosocial Rehabilitation 24, no. 4 (February 28, 2020): 3689–99. http://dx.doi.org/10.37200/ijpr/v24i4/pr201482.

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10

James Gifford. "Dramatic Text, Music Text:." Interdisciplinary Literary Studies 14, no. 1 (2012): 21. http://dx.doi.org/10.5325/intelitestud.14.1.0021.

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Дисертації з теми "Text"

1

Berio, Luciano. "Text of Texts." Bärenreiter Verlag, 1998. https://slub.qucosa.de/id/qucosa%3A36791.

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2

El, Morabit Karim [Verfasser], and U. [Akademischer Betreuer] Husemann. "Measurement of $\textt}\bar\text{t}}\text{H}(\text{H}\rightarrow \text{b}\bar{\text{b}})$ production in the semi-leptonic $\text{t}\bar{\text{t}}$ decay channel at the CMS Experiment / Karim El Morabit ; Betreuer: U. Husemann." Karlsruhe : KIT-Bibliothek, 2021. http://d-nb.info/1238147801/34.

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3

Beaulieu, Derek. "Text without text : concrete poetry and conceptual writing." Thesis, University of Roehampton, 2015. https://pure.roehampton.ac.uk/portal/en/studentthesis/text-without-text(9881aca7-f74a-4f6e-b8d2-58d83c01d7ae).html.

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Анотація:

Concrete poetry has been posited as the only truly international poetic movement of the 20th Century, with Conceptual writing receiving the same cultural location for the 21st-Century. Both forms are dedicated to a materiality of textual production, a poetic investigation into how language occupies space. My dissertation, Text Without Text: Concrete Poetry and Conceptual Writing consists of three chapters: “Dirty”, “Clean” and Conceptual.” Chapter One outlines how degenerated text features in Canadian avant-garde poetics and how my own work builds upon traditions formulated by Canadian poets bpNichol, bill bissett and Steve McCaffery, and can be formulated as an “inarticulate mark,” embodying what American theorist Sianna Ngai refers to as a “poetics of disgust.” Chapter Two, “Clean,” situates my later work around the theories of Eugen Gomringer, the Noigandres Group and Mary Ellen Solt; the clean affectless use of the particles of language in a means which echoes modern advertising and graphic design to create universally understood poetry embracing logos, trademarks and way-finding signage. Chapter Three, “Conceptual,” bridges my concrete poetry with my work in Conceptual writing—especially my novels Local Colour and Flatland. Conceptual writing, as theorized by Kenneth Goldsmith, Vanessa Place and others, works to interrogate a poetics of “uncreativity,” plagiarism, digitally aleatory writing and procedurality. Text Without Text: Concrete Poetry and Conceptual Writing also includes three appendices that outline my poetic oeuvre to date.

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4

Haggren, Hugo. "Text Similarity Analysis for Test Suite Minimization." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-290239.

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Анотація:

Software testing is the most expensive phase in the software development life cycle. It is thus understandable why test optimization is a crucial area in the software development domain. In software testing, the gradual increase of test cases demands large portions of testing resources (budget and time). Test Suite Minimization is considered a potential approach to deal with the test suite size problem. Several test suite minimization techniques have been proposed to efficiently address the test suite size problem. Proposing a good solution for test suite minimization is a challenging task, where several parameters such as code coverage, requirement coverage, and testing cost need to be considered before removing a test case from the testing cycle. This thesis proposes and evaluates two different NLP-based approaches for similarity analysis between manual integration test cases, which can be employed for test suite minimization. One approach is based on syntactic text similarity analysis and the other is a machine learning based semantic approach. The feasibility of the proposed solutions is studied through analysis of industrial use cases at Ericsson AB in Sweden. The results show that the semantic approach barely manages to outperform the syntactic approach. While both approaches show promise, subsequent studies will have to be done to further evaluate the semantic similarity based method.
Mjukvarutestning är den mest kostsamma fasen inom mjukvaruutveckling. Därför är det förståeligt varför testoptimering är ett kritiskt område inom mjukvarubranschen. Inom mjukvarutestning ställer den gradvisa ökningen av testfall stora krav på testresurser (budget och tid). Test Suite Minimization anses vara ett potentiellt tillvägagångssätt för att hantera problemet med växande testsamlingar. Flera minimiseringsmetoder har föreslagits för att effektivt hantera testsamlingars storleksproblem. Att föreslå en bra lösning för minimering av antal testfall är en utmanande uppgift, där flera parametrar som kodtäckning, kravtäckning och testkostnad måste övervägas innan man tar bort ett testfall från testcykeln. Denna uppsats föreslår och utvärderar två olika NLP-baserade metoder för likhetsanalys mellan testfall för manuell integration, som kan användas för minimering av testsamlingar. Den ena metoden baseras på syntaktisk textlikhetsanalys, medan den andra är en maskininlärningsbaserad semantisk strategi. Genomförbarheten av de föreslagna lösningarna studeras genom analys av industriella användningsfall hos Ericsson AB i Sverige. Resultaten visar att den semantiska metoden knappt lyckas överträffa den syntaktiska metoden. Medan båda tillvägagångssätten visar lovande resultat, måste efterföljande studier göras för att ytterligare utvärdera den semantiska likhetsbaserade metoden.

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5

Ly, Man Dan. "Text to features for Swedish text." Thesis, Uppsala universitet, Institutionen för informationsteknologi, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-396578.

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In text mining, texts are usually transformed into numerical vectors or feature vectors, before they are given to a machine learning algorithm for text classification. In this project, a set of features for classifying tweets in Swedish was created. The following classification tasks were selected: gender, age and political party prediction, sentiment analysis and authorship attribution, which is the task of determining if a text was written by a particular author or not. Relevant previous studies were researched and a suitable subset of features used in those studies were chosen. A tool was developed that preprocesses the tweets and calculates, for each tweet, values for the features in the feature set. Experiments were run on a data set consisting of tweets written by Swedish politicians. The output of the tool was given to a machine learning algorithm that created classification models. While the first four classification tasks were unsuccessful, some of the authorship attribution models managed to produce an F-score between 80 and 90%. For the failed classification tasks, the features need to be tested on a different data set or new features have to be created

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6

Jung, Ki-Ho. "Text." Hannover : Techn. Univ, 1988. http://www.gbv.de/dms/weimar/toc/12547072X_toc.pdf.

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7

Wilson, Christin M. L. "Variation and Text Type in Old Occitan Texts." The Ohio State University, 2012. http://rave.ohiolink.edu/etdc/view?acc_num=osu1331136026.

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8

SOARES, FABIO DE AZEVEDO. "AUTOMATIC TEXT CATEGORIZATION BASED ON TEXT MINING." PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO, 2013. http://www.maxwell.vrac.puc-rio.br/Busca_etds.php?strSecao=resultado&nrSeq=23213@1.

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Анотація:

PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO
CONSELHO NACIONAL DE DESENVOLVIMENTO CIENTÍFICO E TECNOLÓGICO
A Categorização de Documentos, uma das tarefas desempenhadas em Mineração de Textos, pode ser descrita como a obtenção de uma função que seja capaz de atribuir a um documento uma categoria a que ele pertença. O principal objetivo de se construir uma taxonomia de documentos é tornar mais fácil a obtenção de informação relevante. Porém, a implementação e a execução de um processo de Categorização de Documentos não é uma tarefa trivial: as ferramentas de Mineração de Textos estão em processo de amadurecimento e ainda, demandam elevado conhecimento técnico para a sua utilização. Além disso, exercendo grande importância em um processo de Mineração de Textos, a linguagem em que os documentos se encontram escritas deve ser tratada com as particularidades do idioma. Contudo há grande carência de ferramentas que forneçam tratamento adequado ao Português do Brasil. Dessa forma, os objetivos principais deste trabalho são pesquisar, propor, implementar e avaliar um framework de Mineração de Textos para a Categorização Automática de Documentos, capaz de auxiliar a execução do processo de descoberta de conhecimento e que ofereça processamento linguístico para o Português do Brasil.
Text Categorization, one of the tasks performed in Text Mining, can be described as the achievement of a function that is able to assign a document to the category, previously defined, to which it belongs. The main goal of building a taxonomy of documents is to make easier obtaining relevant information. However, the implementation and execution of Text Categorization is not a trivial task: Text Mining tools are under development and still require high technical expertise to be handled, also having great significance in a Text Mining process, the language of the documents should be treated with the peculiarities of each idiom. Yet there is great need for tools that provide proper handling to Portuguese of Brazil. Thus, the main aims of this work are to research, propose, implement and evaluate a Text Mining Framework for Automatic Text Categorization, capable of assisting the execution of knowledge discovery process and provides language processing for Brazilian Portuguese.

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9

Baker, Simon. "Semantic text classification for cancer text mining." Thesis, University of Cambridge, 2018. https://www.repository.cam.ac.uk/handle/1810/275838.

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Анотація:

Cancer researchers and oncologists benefit greatly from text mining major knowledge sources in biomedicine such as PubMed. Fundamentally, text mining depends on accurate text classification. In conventional natural language processing (NLP), this requires experts to annotate scientific text, which is costly and time consuming, resulting in small labelled datasets. This leads to extensive feature engineering and handcrafting in order to fully utilise small labelled datasets, which is again time consuming, and not portable between tasks and domains. In this work, we explore emerging neural network methods to reduce the burden of feature engineering while outperforming the accuracy of conventional pipeline NLP techniques. We focus specifically on the cancer domain in terms of applications, where we introduce two NLP classification tasks and datasets: the first task is that of semantic text classification according to the Hallmarks of Cancer (HoC), which enables text mining of scientific literature assisted by a taxonomy that explains the processes by which cancer starts and spreads in the body. The second task is that of the exposure routes of chemicals into the body that may lead to exposure to carcinogens. We present several novel contributions. We introduce two new semantic classification tasks (the hallmarks, and exposure routes) at both sentence and document levels along with accompanying datasets, and implement and investigate a conventional pipeline NLP classification approach for both tasks, performing both intrinsic and extrinsic evaluation. We propose a new approach to classification using multilevel embeddings and apply this approach to several tasks; we subsequently apply deep learning methods to the task of hallmark classification and evaluate its outcome. Utilising our text classification methods, we develop and two novel text mining tools targeting real-world cancer researchers. The first tool is a cancer hallmark text mining tool that identifies association between a search query and cancer hallmarks; the second tool is a new literature-based discovery (LBD) system designed for the cancer domain. We evaluate both tools with end users (cancer researchers) and find they demonstrate good accuracy and promising potential for cancer research.

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10

Powers, Harold S. "Music as Text and Text as Music." Bärenreiter Verlag, 1998. https://slub.qucosa.de/id/qucosa%3A36794.

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Книги з теми "Text"

1

Sybil, Dümchen, Nerlich Michael, and Technische Universität Berlin. Institut für Romanische Literaturwissenschaft., eds. Texte-image =: Bild-Text. Berlin: Technische Universität Berlin, Institut für Romanische Literaturwissenschaft, 1990.

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2

Bauks, Michaela, Wayne Horowitz, and Armin Lange, eds. Between Text and Text. Göttingen: Vandenhoeck & Ruprecht, 2013. http://dx.doi.org/10.13109/9783666550256.

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3

1953-, McDowall John, Taylor Chris, and Wild Pansy Press, eds. Text / image =: Image / text. Leeds: Wild Pansy Press, University of Leeds, 2004.

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4

Guy, Denhière, and Rossi Jean-Pierre, eds. Text and text processing. Amsterdam: North-Holland, 1991.

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5

W, Halsall A., Rutland R. B, Centre TADAC, and Carleton University, eds. Text and ideology =: Texte et idéologie. Ottawa, Canada: Centre for Textual Analysis, Discourse and Culture, Carleton University, 1988.

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6

Stein, Charles. Text-Sound Texts. Barrytown Limited, 2002.

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7

Bertsch, Werner J. Washington D. C. Souvenir Book: English Text - Texte Francais - Deutscher Text - Texto Espanol. L. B. Prince Company, Incorporated, 1992.

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8

Wonderful Copenhagen (Souvenir Guide Book: English Text, Deutescher Text, Texte Francais, Texto Esponol). Grolund Publishers DK, 1989.

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9

Jensen, Muriel. Texts to Text Im. Longman, 1994.

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10

Dean Ward and Elizabeth Vander Lei. REAL TEXTS-TEXT >CUSTOM<. Pearson, 2013.

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Частини книг з теми "Text"

1

Louis, Dirk, and Peter Müller. "Text, Text, Text." In Java, 267–93. München: Carl Hanser Verlag GmbH & Co. KG, 2014. http://dx.doi.org/10.3139/9783446438347.011.

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2

Louis, Dirk, and Peter Müller. "Text, Text, Text." In Java, 221–42. München: Carl Hanser Verlag GmbH & Co. KG, 2018. http://dx.doi.org/10.3139/9783446453623.011.

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3

McAllister-Viel, Tara. "Text/vocal text." In Training Actors’ Voices, 185–206. Abingdon, Oxon; New York, NY: Routledge, 2018.: Routledge, 2018. http://dx.doi.org/10.4324/9781315109718-7.

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4

Klein, Holger. "Main Text, Side Text, Frame Text." In J. B. Priestley’s Plays, 25–47. London: Palgrave Macmillan UK, 1988. http://dx.doi.org/10.1007/978-1-349-03290-7_2.

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5

Garfinkel, Simson L., and Michael K. Mahoney. "Text and Rich Text." In NeXTSTEP™ PROGRAMMING, 307–21. New York, NY: Springer New York, 1993. http://dx.doi.org/10.1007/978-1-4613-9275-0_12.

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6

Biemann, Chris, Gerhard Heyer, and Uwe Quasthoff. "Text und Text Mining." In Wissensrohstoff Text, 1–34. Wiesbaden: Springer Fachmedien Wiesbaden, 2022. http://dx.doi.org/10.1007/978-3-658-35969-0_1.

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7

Hamann, Sabine, Hauke Hell, Detlef Pankow, and Robert Wunderer. "Text." In DigiScript™, 107–15. Berlin, Heidelberg: Springer Berlin Heidelberg, 1997. http://dx.doi.org/10.1007/978-3-642-46829-2_10.

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8

Shimin, Geng, Hans-Joachim Klimkeit, and Jens Peter Laut. "Text." In Eine Buddhistische Apokalypse, 21–75. Wiesbaden: VS Verlag für Sozialwissenschaften, 1998. http://dx.doi.org/10.1007/978-3-322-84082-0_2.

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Green, Tom, and Tiago Dias. "Text." In Foundation Flash CS5 for Designers, 315–60. Berkeley, CA: Apress, 2010. http://dx.doi.org/10.1007/978-1-4302-2995-7_6.

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10

Chabás, José, and Bernard R. Goldstein. "Text." In Archimedes, 9–94. Dordrecht: Springer Netherlands, 2003. http://dx.doi.org/10.1007/978-94-017-0213-3_2.

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Тези доповідей конференцій з теми "Text"

1

Knolle, Joscha. "Rare top quark production at the LHC: $\text{t}\bar{\text{t}}\text{Z}$, $\text{t}\bar{\text{t}}\text{W}$, $\text{t}\bar{\text{t}}\gamma$, $\text{tZq}$, $\text{t}\gamma\text{q}$, and $\text{t}\bar{\text{t}}\text{t}\bar{\text{t}}$." In XXVII International Workshop on Deep-Inelastic Scattering and Related Subjects. Trieste, Italy: Sissa Medialab, 2019. http://dx.doi.org/10.22323/1.352.0157.

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2

Fuadi, Mukhlish, and Adhi Dharma Wibawa. "Automatic Question Generation from Indonesian Texts Using Text-to-Text Transformers." In 2022 International Conference on Electrical and Information Technology (IEIT). IEEE, 2022. http://dx.doi.org/10.1109/ieit56384.2022.9967858.

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3

Usai, Emanuele. "Determination of $\kappa_\text{t}$ from $\text{t}\bar{\text{t}}$, $\text{t}\bar{\text{t}}\text{t}\bar{\text{t}}$, and others." In The Ninth Annual Conference on Large Hadron Collider Physics. Trieste, Italy: Sissa Medialab, 2021. http://dx.doi.org/10.22323/1.397.0074.

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4

Luise, M. Daniela López De, and Mariana Soffer. "Automatic Text Processing for Spanish Texts." In 2008 Electronics, Robotics and Automotive Mechanics Conference (CERMA). IEEE, 2008. http://dx.doi.org/10.1109/cerma.2008.50.

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5

Dubničková, A. Z., S. Dubnička, and R. Baldini-Ferroli. "THE $ {\text{e}}^ + {\text{e}}^ - \, \to \,\overline {\text{n}} \,{\text{n}} $ CROSS SECTION PREDICTED REMARKABLY LARGER THAN THE $ {\text{e}}^ + {\text{e}}^ - \, \to \,\overline {\text{p}} \,{\text{p}} $ONE." In Proceedings of the International Seminar. WORLD SCIENTIFIC, 1991. http://dx.doi.org/10.1142/9789814439336_0053.

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6

Kovachov, Sergii, Andriy Lazarenko, Zhakyp Karipbayev, Yana Suchikova, Tamara Tsebriienko, and Anatoli I. Popov. "$3\mathrm{D} \text{Al}_{\mathrm{x}}\text{Ga}_{1-\mathrm{x}}\text{As}/\text{por}\text{-}\text{GaAs}/\text{GaAs}$ heterostructures for solar cells." In 2022 IEEE 3rd KhPI Week on Advanced Technology (KhPIWeek). IEEE, 2022. http://dx.doi.org/10.1109/khpiweek57572.2022.9916484.

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Salton, Gerard, Amit Singhal, Chris Buckley, and Mandar Mitra. "Automatic text decomposition using text segments and text themes." In the the seventh ACM conference. New York, New York, USA: ACM Press, 1996. http://dx.doi.org/10.1145/234828.234834.

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8

Kasmanah, Kasmanah, Dika Supyandi, Tanti Ardianti, Evi Fitriyanti, and Devi Ratnasari. "Student’s Diagnostic Ability on Review Text and Persuasive Text in Indonesian Language Test." In 1st International Conference on Folklore, Language, Education and Exhibition (ICOFLEX 2019). Paris, France: Atlantis Press, 2020. http://dx.doi.org/10.2991/assehr.k.201230.061.

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9

Kuznetsov, S. V., V. A. Konyushkin, A. N. Nakladov, A. A. Alexandrov, E. I. Madirov, D. Busko, B. S. Richards, and A. Turshatov. "Up-conversion luminescence quantum yields of $\text{MF}_{2}:\text{Yb}:\mathrm{R}(\mathrm{M}=\text{Ca},\text{Sr},\text{Ba}, \text{Pb};\mathrm{R}=\text{Er},\text{Tm}, \text{Ho})$ single crystals for photonics." In 2022 International Conference Laser Optics (ICLO). IEEE, 2022. http://dx.doi.org/10.1109/iclo54117.2022.9839954.

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Loghina, Liudmila, Maksym Chylii, Anastasia Kaderavkova, Jakub Houdek, and Miroslav Vlcek. "The Versatile Synthesis of Polyhedron $\text{Core}/\text{shell}/\text{shellCd}_{0.1}\text{Zn}_{0.9}\text{Se}/\text{Cd}_{\mathrm{X}}\text{Zn}_{1-\mathrm{X}}\mathrm{S}/\text{ZnS}$ Quantum Dots." In 2022 IEEE 12th International Conference Nanomaterials: Applications & Properties (NAP). IEEE, 2022. http://dx.doi.org/10.1109/nap55339.2022.9934563.

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Звіти організацій з теми "Text"

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Spirling, Arthur. Text Analysis: Text as Data with R. Instats Inc., 2022. http://dx.doi.org/10.61700/a52fcasdqm1du469.

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Анотація:

This seminar introduces “text as data” statistical methods using R. The course is very applied, with the primary aim of helping social science researchers understand the types of questions we can ask with text, and how to answer them. The seminar covers how texts may be modeled and compared as quantitative entities, and then moves to supervised and unsupervised methods—including topic models and embeddings. At the seminar's conclusion, participants will know how conduct their own text as data research projects. An official Instats certificate of completion is provided at the conclusion of the seminar.

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Spirling, Arthur. Text Analysis: Text as Data with R. Instats Inc., 2022. http://dx.doi.org/10.61700/lolq2hyg9sn6d469.

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Анотація:

This seminar introduces “text as data” statistical methods using R. The course is very applied, with the primary aim of helping social science researchers understand the types of questions we can ask with text, and how to answer them. The seminar covers how texts may be modeled and compared as quantitative entities, and then moves to supervised and unsupervised methods—including topic models and embeddings. At the seminar's conclusion, participants will know how conduct their own text as data research projects. An official Instats certificate of completion is provided at the conclusion of the seminar. For European PhD students, the seminar offers 2 ECTS Equivalent point.

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Kelly, Bryan, Asaf Manela, and Alan Moreira. Text Selection. Cambridge, MA: National Bureau of Economic Research, November 2019. http://dx.doi.org/10.3386/w26517.

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Gentzkow, Matthew, Bryan Kelly, and Matt Taddy. Text as Data. Cambridge, MA: National Bureau of Economic Research, March 2017. http://dx.doi.org/10.3386/w23276.

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Gillies, S. GeoJSON Text Sequences. RFC Editor, April 2017. http://dx.doi.org/10.17487/rfc8142.

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Kostoff, Ronald N., Henry A. Buchtel, John Andrews, and Kirstin M. Pfeil. Science and Technology Text Mining: Text Mining of the Journal Cortex. Fort Belvoir, VA: Defense Technical Information Center, January 2004. http://dx.doi.org/10.21236/ada425249.

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Hobbs, Jerry R. Research in Text Processing. Fort Belvoir, VA: Defense Technical Information Center, December 1992. http://dx.doi.org/10.21236/ada259434.

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Miller, Robert C. Lightweight Structure in Text. Fort Belvoir, VA: Defense Technical Information Center, May 2002. http://dx.doi.org/10.21236/ada459021.

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Whitacre, Madeline. Oppenheimer Interpretive Text Additions. Office of Scientific and Technical Information (OSTI), May 2023. http://dx.doi.org/10.2172/1975639.

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Chew, Robert F., Kirsty J. Weitzel, Peter Baumgartner, Caroline W. Oppenheimer, Brianna D'Arcangelo, Autumn Barnes, Shirley Liu, Adam Bryant Miller, Ashley Lowe, and Anna C. Yaros. Improving Text Classification with Boolean Retrieval for Rare Categories: A Case Study Identifying Firearm Violence Conversations in the Crisis Text Line Database. RTI Press, March 2023. http://dx.doi.org/10.3768/rtipress.2023.mr.0050.2304.

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Advancements in machine learning and natural language processing have made text classification increasingly attractive for information retrieval. However, developing text classifiers is challenging when no prior labeled data are available for a rare category of interest. Finding instances of the rare class using a uniform random sample can be inefficient and costly due to the rare category’s low base rate. This work presents an approach that combines the strengths of text classification and Boolean retrieval to help learn rare concepts of interest. As a motivating example, we use the task of finding conversations that reference firearm injury or violence in the Crisis Text Line database. Identifying rare categories, like firearm injury or violence, can improve crisis lines' abilities to support people with firearm-related crises or provide appropriate resources. Our approach outperforms a set of iteratively refined Boolean queries and results in a recall of 0.91 on a test set generated from a process independent of our study. Our results suggest that text classification with Boolean retrieval initialization can be effective for finding rare categories of interest and improve on the precision of using Boolean retrieval alone.

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