Sortable Citations

https://www.zotero.org/groups/2222059/hl-imls_topic_modeling/items/collectionKey/3RRZNWLB

Bibliography

Classification

Bao, Yang, Nigel Collier, and Anindya Datta. “A Partially Supervised Cross-Collection Topic Model for Cross-Domain Text Classification.” In Proceedings of the 22Nd ACM International Conference on Information & Knowledge Management, 239–248. CIKM ’13. New York, NY, USA: ACM, 2013. https://doi.org/10.1145/2505515.2505556.

Beghtol, C. “Knowledge Domains: Multidisciplinarity and Bibliographic Classification Systems.” Knowledge Organization 25, no. 1 (19980101).

Bittermann, André, and Andreas Fischer. “How to Identify Hot Topics in Psychology Using Topic Modeling.” Zeitschrift Für Psychologie 226, no. 1 (2018): 3–13. https://doi.org/10.1027/2151-2604/a000318.

Brygfjeld, Svein Arne, Freddy Wetjen, and André WalsØe. “Machine Learning for Production of Dewey Decimal,” n.d. http://library.ifla.org/2216/1/115-brygfjeld-en.pdf.

Danilevsky, M., C. Wang, N. Desai, X. Ren, J. Guo, and J. Han. “Automatic Construction and Ranking of Topical Keyphrases on Collections of Short Documents.” In Proceedings of the 2014 SIAM International Conference on Data Mining, 398–406. Proceedings. Society for Industrial and Applied Mathematics, 2014. https://doi.org/10.1137/1.9781611973440.46.

Dědek, Jan, Peter Vojtáš, and Marta Vomlelová. “Fuzzy ILP Classification of Web Reports after Linguistic Text Mining.” Information Processing and Management 48, no. 3 (2012): 438–450. https://doi.org/10.1016/j.ipm.2011.02.008.

Denda, Kayo. “Beyond Subject Headings.” Library Resources & Technical Services 49, no. 4 (2005): 266–275. https://doi.org/10.5860/lrts.49n4.266.

Friedman, Carol, Tara Borlawsky, Lyudmila Shagina, H Xing, and Yves Lussier. “Bio-Ontology and Text: Bridging the Modeling Gap.” Bioinformatics 22, no. 19 (2006): 2421–29. https://doi.org/10.1093/bioinformatics/btl405.

Gu, H, Y Perl, J Geller, M Halper, and M Singh. “A Methodology for Partitioning a Vocabulary Hierarchy into Trees.” Artificial Intelligence in Medicine 15, no. 1 (1999): 77. https://doi.org/10.1016/S0933-3657(98)00046-3.

Hagedorn, Kat, Michael Kargela, Youn Noh, and David Newman. “A New Way to Find: Testing the Use of Clustering Topics in Digital Libraries.” D-Lib Magazine 17, no. 9/10 (September 2011). https://doi.org/10.1045/september2011-hagedorn.

Jian Qin, and Stephen Paling. “Converting a Controlled Vocabulary into an Ontology: The Case of GEM.” Information Research: An International Electronic Journal 6, no. 2 (2001): .

Junger, Ulrike. “Automation First – the Subject Cataloguing Policy of the Deutsche Nationalbibliothek,” n.d. http://library.ifla.org/2213/1/115-junger-en.pdf.

“Knowledge Management Section Joint with Academic and Research Libraries Section and Rare Books and Special Collections Section.” Google Docs. Accessed September 19, 2018. Document.

Lebow, David G. “A Social Machine for Transdisciplinary Research.” Informing Science 21 (January 2018): 201–17. https://doi.org/10.28945/4025.

Mimno, David, and Andrew McCallum. “Organizing the OCA: Learning Faceted Subjects from a Library of Digital Books.” In Proceedings of the 7th ACM/IEEE-CS Joint Conference on Digital Libraries, 376–385. JCDL ’07. New York, NY, USA: ACM, 2007. https://doi.org/10.1145/1255175.1255249.

Newman, David, Kat Hagedorn, Chaitanya Chemudugunta, and Padhraic Smyth. “Subject Metadata Enrichment Using Statistical Topic Models.” In Proceedings of the 7th ACM/IEEE-CS Joint Conference on Digital Libraries, 366–375. JCDL ’07. New York, NY, USA: ACM, 2007. https://doi.org/10.1145/1255175.1255248.

Sabharwal, Arjun. “Knowledge Domain Navigation in Interdisciplinary Digital Landscapes.” Journal of Library Metadata 11, no. 2 (April 2011): 63–82. https://doi.org/10.1080/19386389.2011.570655.

Shu, Fei, Jesse David Dinneen, Banafsheh Asadi, and Charles-Antoine Julien. “Mapping Science Using Library of Congress Subject Headings.” Journal of Informetrics 11, no. 4 (November 1, 2017): 1080–94. https://doi.org/10.1016/j.joi.2017.08.008.

Stein, Roger Alan, Patricia A. Jaques, and JoãO Francisco Valiati. “An Analysis of Hierarchical Text Classification Using Word Embeddings.” Information Sciences 471 (2019): 216–32. https://doi.org/10.1016/j.ins.2018.09.001.

Vishwanath Bijalwan, Vinay Kumar, Pinki Kumari, and Jordan Pascual. “KNN Based Machine Learning Approach for Text and Document Mining.” International Journal of Database Theory and Application 7, no. 1 (2014): 61–70. https://doi.org/10.14257/ijdta.2014.7.1.06.

Cross-disciplinary

Bao, Yang, Nigel Collier, and Anindya Datta. “A Partially Supervised Cross-Collection Topic Model for Cross-Domain Text Classification.” In Proceedings of the 22Nd ACM International Conference on Information & Knowledge Management, 239–248. CIKM ’13. New York, NY, USA: ACM, 2013. https://doi.org/10.1145/2505515.2505556.

Beghtol, C. “Knowledge Domains: Multidisciplinarity and Bibliographic Classification Systems.” Knowledge Organization 25, no. 1 (19980101).

Denda, Kayo. “Beyond Subject Headings.” Library Resources & Technical Services 49, no. 4 (2005): 266–275. https://doi.org/10.5860/lrts.49n4.266.

Jay, Caroline, Simon Harper, Ian Dunlop, Sam Smith, Shoaib Sufi, Carole Goble, and Iain Buchan. “Natural Language Search Interfaces: Health Data Needs Single-Field Variable Search.” Journal of Medical Internet Research 18, no. 1 (January 2016): 1–21. https://doi.org/10.2196/jmir.4912.

“Knowledge Management Section Joint with Academic and Research Libraries Section and Rare Books and Special Collections Section.” Google Docs. Accessed September 19, 2018. Document.

Lebow, David G. “A Social Machine for Transdisciplinary Research.” Informing Science 21 (January 2018): 201–17. https://doi.org/10.28945/4025.

Li, Lianghao, Xiaoming Jin, and Mingsheng Long. “Topic Correlation Analysis for Cross-Domain Text Classification,” n.d., 7.

MLS, Linda G. Ackerson. “Challenges for Engineering Libraries.” Science & Technology Libraries 21, no. 1–2 (September 1, 2001): 43–52. https://doi.org/10.1300/J122v21n01_05.

Sabharwal, Arjun. “Knowledge Domain Navigation in Interdisciplinary Digital Landscapes.” Journal of Library Metadata 11, no. 2 (April 2011): 63–82. https://doi.org/10.1080/19386389.2011.570655.


Discovery

Campbell, D. Grant, and Karl V. Fast. “Academic Libraries and the Semantic Web: What the Future May Hold for Research-Supporting Library Catalogues.” The Journal of Academic Librarianship 30, no. 5 (2004): 382–390. https://doi.org/10.1016/j.acalib.2004.06.007.

Gross, Tina, Arlene G. Taylor, and Daniel N. Joudrey. “Still a Lot to Lose: The Role of Controlled Vocabulary in Keyword Searching.” Cataloging & Classification Quarterly 53, no. 1 (January 2, 2015): 1–39. https://doi.org/10.1080/01639374.2014.917447.

Jay, Caroline, Simon Harper, Ian Dunlop, Sam Smith, Shoaib Sufi, Carole Goble, and Iain Buchan. “Natural Language Search Interfaces: Health Data Needs Single-Field Variable Search.” Journal of Medical Internet Research 18, no. 1 (January 2016): 1–21. https://doi.org/10.2196/jmir.4912.

Li, Ying, Dick R Miller, and Mary Buttner. “Bibliographic Data Mining: Automatically Building Component Part Records for e-Journal Articles on the Internet.” Journal of Internet Cataloging 5, no. 1 (2002): 29–41.

Shu, Fei, Jesse David Dinneen, Banafsheh Asadi, and Charles-Antoine Julien. “Mapping Science Using Library of Congress Subject Headings.” Journal of Informetrics 11, no. 4 (November 1, 2017): 1080–94. https://doi.org/10.1016/j.joi.2017.08.008.

Machine Learning

Aggarwal, Charu C., and ChengXiang Zhai. Mining Text Data. Springer Science & Business Media, 2012. https://link-springer-com.proxy.library.nd.edu/book/10.1007%2F978-1-4614-3223-4.

Brygfjeld, Svein Arne, Freddy Wetjen, and André WalsØe. “Machine Learning for Production of Dewey Decimal,” n.d. http://library.ifla.org/2216/1/115-brygfjeld-en.pdf.

Dědek, Jan, Peter Vojtáš, and Marta Vomlelová. “Fuzzy ILP Classification of Web Reports after Linguistic Text Mining.” Information Processing and Management 48, no. 3 (2012): 438–450. https://doi.org/10.1016/j.ipm.2011.02.008.

“Knowledge Management Section Joint with Academic and Research Libraries Section and Rare Books and Special Collections Section.” Google Docs. Accessed September 19, 2018. Document.

Stein, Roger Alan, Patricia A. Jaques, and JoãO Francisco Valiati. “An Analysis of Hierarchical Text Classification Using Word Embeddings.” Information Sciences 471 (2019): 216–32. https://doi.org/10.1016/j.ins.2018.09.001.

Vishwanath Bijalwan, Vinay Kumar, Pinki Kumari, and Jordan Pascual. “KNN Based Machine Learning Approach for Text and Document Mining.” International Journal of Database Theory and Application 7, no. 1 (2014): 61–70. https://doi.org/10.14257/ijdta.2014.7.1.06.

Zhiyuan Chen [Computer scientist. “Lifelong Machine Learning.” Morgan & Claypool, 2017. http://proxy.library.nd.edu/login?url=http://dx.doi.org/10.2200/S00737ED1V01Y201610AIM033.

Natural Language Processing

Bittermann, André, and Andreas Fischer. “How to Identify Hot Topics in Psychology Using Topic Modeling.” Zeitschrift Für Psychologie 226, no. 1 (2018): 3–13. https://doi.org/10.1027/2151-2604/a000318.

Danilevsky, M., C. Wang, N. Desai, X. Ren, J. Guo, and J. Han. “Automatic Construction and Ranking of Topical Keyphrases on Collections of Short Documents.” In Proceedings of the 2014 SIAM International Conference on Data Mining, 398–406. Proceedings. Society for Industrial and Applied Mathematics, 2014. https://doi.org/10.1137/1.9781611973440.46.

Friedman, Carol, Tara Borlawsky, Lyudmila Shagina, H Xing, and Yves Lussier. “Bio-Ontology and Text: Bridging the Modeling Gap.” Bioinformatics 22, no. 19 (2006): 2421–29. https://doi.org/10.1093/bioinformatics/btl405.

Hagedorn, Kat, Michael Kargela, Youn Noh, and David Newman. “A New Way to Find: Testing the Use of Clustering Topics in Digital Libraries.” D-Lib Magazine 17, no. 9/10 (September 2011). https://doi.org/10.1045/september2011-hagedorn.

Joo, S, I Choi, and N Choi. “Topic Analysis of the Research Domain in Knowledge Organization: A Latent Dirichlet Allocation Approach.” Knowl. Organ. 45, no. 2 (2018): 170–83. https://doi.org/10.5771/0943-7444-2018-2-170.

Mimno, David, and Andrew McCallum. “Organizing the OCA: Learning Faceted Subjects from a Library of Digital Books.” In Proceedings of the 7th ACM/IEEE-CS Joint Conference on Digital Libraries, 376–385. JCDL ’07. New York, NY, USA: ACM, 2007. https://doi.org/10.1145/1255175.1255249.

Rajagopal, Dheeraj, Daniel Olsher, Erik Cambria, and Kenneth Kwok. “Commonsense-Based Topic Modeling.” In Proceedings of the Second International Workshop on Issues of Sentiment Discovery and Opinion Mining, 6:1–6:8. WISDOM ’13. New York, NY, USA: ACM, 2013. https://doi.org/10.1145/2502069.2502075.

Topic Modeling

Bao, Yang, Nigel Collier, and Anindya Datta. “A Partially Supervised Cross-Collection Topic Model for Cross-Domain Text Classification.” In Proceedings of the 22Nd ACM International Conference on Information & Knowledge Management, 239–248. CIKM ’13. New York, NY, USA: ACM, 2013. https://doi.org/10.1145/2505515.2505556.

Bittermann, André, and Andreas Fischer. “How to Identify Hot Topics in Psychology Using Topic Modeling.” Zeitschrift Für Psychologie 226, no. 1 (2018): 3–13. https://doi.org/10.1027/2151-2604/a000318.

Cain, Jonathan O. “Using Topic Modeling to Enhance Access to Library Digital Collections.” Journal of Web Librarianship 10, no. 3 (July 2, 2016): 210–25. https://doi.org/10.1080/19322909.2016.1193455.

Danilevsky, M., C. Wang, N. Desai, X. Ren, J. Guo, and J. Han. “Automatic Construction and Ranking of Topical Keyphrases on Collections of Short Documents.” In Proceedings of the 2014 SIAM International Conference on Data Mining, 398–406. Proceedings. Society for Industrial and Applied Mathematics, 2014. https://doi.org/10.1137/1.9781611973440.46.

Goldstone, Andrew. “PMLA Topic Model Browser.” Accessed November 13, 2018. http://agoldst.github.io/dfr-browser/demo/.

Hagedorn, Kat, Michael Kargela, Youn Noh, and David Newman. “A New Way to Find: Testing the Use of Clustering Topics in Digital Libraries.” D-Lib Magazine 17, no. 9/10 (September 2011). https://doi.org/10.1045/september2011-hagedorn.

Joo, S, I Choi, and N Choi. “Topic Analysis of the Research Domain in Knowledge Organization: A Latent Dirichlet Allocation Approach.” Knowl. Organ. 45, no. 2 (2018): 170–83. https://doi.org/10.5771/0943-7444-2018-2-170.

“Knowledge Management Section Joint with Academic and Research Libraries Section and Rare Books and Special Collections Section.” Google Docs. Accessed September 19, 2018. Document.

Li, Lianghao, Xiaoming Jin, and Mingsheng Long. “Topic Correlation Analysis for Cross-Domain Text Classification,” n.d., 7.

Mimno, David, and Andrew McCallum. “Organizing the OCA: Learning Faceted Subjects from a Library of Digital Books.” In Proceedings of the 7th ACM/IEEE-CS Joint Conference on Digital Libraries, 376–385. JCDL ’07. New York, NY, USA: ACM, 2007. https://doi.org/10.1145/1255175.1255249.

Newman, David, Kat Hagedorn, Chaitanya Chemudugunta, and Padhraic Smyth. “Subject Metadata Enrichment Using Statistical Topic Models.” In Proceedings of the 7th ACM/IEEE-CS Joint Conference on Digital Libraries, 366–375. JCDL ’07. New York, NY, USA: ACM, 2007. https://doi.org/10.1145/1255175.1255248.

Rajagopal, Dheeraj, Daniel Olsher, Erik Cambria, and Kenneth Kwok. “Commonsense-Based Topic Modeling.” In Proceedings of the Second International Workshop on Issues of Sentiment Discovery and Opinion Mining, 6:1–6:8. WISDOM ’13. New York, NY, USA: ACM, 2013. https://doi.org/10.1145/2502069.2502075.

Zhang, Xiang, Junbo Zhao, and Yann LeCun. “Character-Level Convolutional Networks for Text Classification.” arXiv.org, n.d. https://arxiv.org/pdf/1509.01626v3.pdf.