Shlomo Argamon

Shlomo Argamon is an American/Israeli computer scientist and forensic linguist. He is currently the chair of the computer science department as well as a tenured professor of computer science and director of the Master of Data Science[1] program at Illinois Institute of Technology in Chicago, IL.

Shlomo Argamon
Born1967 (age 52–53)
EducationB.S. applied mathematics, M.Phil., Ph.D. computer science
Alma materCarnegie-Mellon University, Yale University
OccupationComputational linguistics
EmployerIllinois Institute of Technology
Known forComputational stylistics
TitleDirector, Master of Data Science; Director, Linguistic Cognition Laboratory, Illinois Institute of Technology
Websitelingcog.blogspot.com

Education

Shlomo Argamon received his B.S. in applied mathematics from Carnegie-Mellon University and his M.Phil and Ph.D. in computer science from Yale University, supervised by Drew McDermott.[2] He spent two years doing postdoctoral research under a Fulbright Foundation fellowship with Sarit Kraus at Bar-Ilan University in Ramat Gan, Israel.

Research

Since the late 1990s, Argamon has worked primarily on computational linguistic analysis of non-denotational meaning, including computational analysis of language stylistics, sentiment analysis,[3][4][5] and metaphor analysis.[6] He has also published well-cited research on active learning (machine learning),[7] metalearning,[8] and robotic mapping.

Computational Stylistics

Argamon is best known for his work on computational stylistics, particularly author profiling. Together with Moshe Koppel and others, he has shown how statistical analysis of word usage can determine an author's age, sex, native language, and personality type with high accuracy in English-language texts.[9][10][11] His work has also shown how textual features indicating differences between male and female authorship are consistent between languages and across time.[12][13][14]

He has also developed computational stylistic methods that provide insights into the meaning of stylistic differences. One of Argamon's key innovations for this purpose is the development of computational stylistic analysis using systemic functional linguistics.[15][16] For example, together with Jeff Dodick and Paul Chase, he examined whether there are clear and consistent differences between scientific method in experimental sciences and historical sciences. Their work showed how using systemic functional features in computational stylistic analysis provides evidence for multiple scientific methodologies of the sorts posited previously by philosophers of science.[17]

Forensic Linguistics is viewed through its two major components, first one being Written Language and the Second one being Spoken Language.[18] Written language is mainly used on transcripts for police interviews, for both the witnesses and the suspects. The transcripts are considered examined text material from criminal messages, terrorist threats or blackmailing messages and translate them from one language to another and then reviewed to help in answering questions about the author if the message. Many different kinds of text materials can be examined, some being notes, phone messages, letters both typed and handwritten as well as text from social medias. Much more can be determined by combining computational stylistics and scientific methods in order to enhance Cybersecurity.

Linguistics for Cybersecurity

Recently, Argamon has pushed for the increased use of linguistic analysis for attribution of cybersecurity attacks. He has pointed out how linguistic attribution techniques can often be used to good effect on natural language texts that arise in different attack scenarios, and has provided analyses for high-profile cases such as the Sony Pictures hack,[19][20] the Democratic National Committee cyber attacks,[21] and the Shadow Brokers NSA leak.[22][23]

Data Science

In 2013, Argamon founded the Illinois Institute of Technology Master of Data Science program,[1] which he currently directs. The program seeks to teach students "to think about the real problems that need to be solved, not to simply find technical solutions." Argamon views data scientists as "sensemakers", whose job is not merely to produce analytic results, but to help their clients make sense of a complex, uncertain, and fast-changing world through rigorous analysis and explanation of the data.[24][25]

Honors
gollark: No, that would be really deathing.
gollark: What if you fake faking your death?
gollark: What if you fake your own death so you can claim you're suffering trauma and don't have to study?
gollark: > someone should dieNo. That is a bad thing.
gollark: Euler was a real person, alright.

References
  1. "Master of Data Science | IIT College of Science".
  2. http://webmail.cs.yale.edu/publications/techreports/tr1032.ps.gz
  3. Kenneth Bloom, Navendu Garg, and Shlomo Argamon. Extracting appraisal expressions. In Proc. Human Language Technologies: Conference of the North American Association for Computational Linguistics (NAACL-HLT), Rochester, New York, April, 2007.
  4. Casey Whitelaw, Navendu Garg, and Shlomo Argamon. Using appraisal groups for sentiment analysis. In Proc. Conference on Information and Knowledge Management, Bremen, Germany, November 2005.
  5. Shlomo Argamon, Ken Bloom, Andrea Esuli, and Fabrizio Sebastiani. Automatically Determining Attitude Type and Force for Sentiment Analysis. 3rd Language and Technology Conference, Poznan, Poland, October 2007.
  6. Lisa Gandy, Nadji Allan, Mark Atallah, Ophir Frieder, Newton Howard, Sergey Kanareykin, Moshe Koppel, Mark Last, Yair Neuman, Shlomo Argamon. Automatic identification of conceptual metaphors with limited knowledge. In Proc. Twenty-Seventh AAAI Conference on Artificial Intelligence (AAAI-13), Bellevue, WA, July 2013.
  7. Shlomo Argamon-Engelson and Ido Dagan. Committee-based sample selection for probabilistic classifiers. Journal of Artificial Intelligence Research, 11:335-360, 1999.
  8. Julio Ortega, Moshe Koppel, and Shlomo Argamon-Engelson. Arbitrating among competing classifiers using learned referees. Knowledge and Information Systems, 3(4):470–490, 2001.
  9. Argamon, Shlomo, Moshe Koppel, Jonathan Fine, and Anat Rachel Shimoni. "Gender, genre, and writing style in formal written texts." Text 23, no. 3 (2003): 321-346.
  10. Argamon, Shlomo, Moshe Koppel, James W. Pennebaker, and Jonathan Schler. "Automatically profiling the author of an anonymous text." Communications of the ACM 52, no. 2 (2009): 119-123.
  11. Argamon, Shlomo, Moshe Koppel, James W. Pennebaker, and Jonathan Schler. "Mining the Blogosphere: Age, gender and the varieties of self-expression." First Monday 12, no. 9 (2007). http://journals.uic.edu/ojs/index.php/fm/article/view/2003
  12. Argamon, Shlomo, Jean-Baptiste Goulain, Russell Horton, and Mark Olsen. "Vive la Différence! Text mining gender difference in French literature." Digital Humanities Quarterly 3, no. 2 (2009).
  13. Argamon, Shlomo, Russell Horton, Mark Olsen, and Sterling Stuart Stein. "Gender, Race, and Nationality in BlackDrama, 1850-2000: Mining Differences in Language Use in Authors and their Characters." Proceedings of Digital Humanities (2007).
  14. Hota, Sobhan R., Shlomo Argamon, and Rebecca Chung. "Gender in Shakespeare: Automatic stylistics gender character classification using syntactic, lexical and lemma features." Proc. Chicago Colloquium on Digital Humanities and Computer Science (DHCS) (2006).
  15. Argamon, Shlomo, Casey Whitelaw, Paul Chase, Sobhan Raj Hota, Navendu Garg, and Shlomo Levitan. "Stylistic text classification using functional lexical features." Journal of the American Society for Information Science and Technology 58, no. 6 (2007): 802-822.
  16. Argamon, Shlomo, and Moshe Koppel. "The rest of the story: Finding meaning in stylistic variation." In The Structure of Style, pp. 79-112. Springer, Berlin, Heidelberg, 2010.
  17. Argamon, Shlomo, Jeff Dodick, and Paul Chase. "Language use reflects scientific methodology: A corpus-based study of peer-reviewed journal articles." Scientometrics 75, no. 2 (2008): 203-238.
  18. "Language Matters! – Exciting insights into the realm of Applied Linguistics". Retrieved 2020-04-20.
  19. "Doubts Persist on U.S. Claims of North Korean Role in Sony Hack".
  20. "New Study May Add to Skepticism Among Security Experts That North Korea Was Behind Sony Hack". 2014-12-24.
  21. Savage, Charlie; Perlroth, Nicole (2016-07-27). "Is D.N.C. Email Hacker a Person or a Russian Front? Experts Aren't Sure". The New York Times.
  22. "The NSA Data Leakers Might be Faking Their Awful English to Deceive Us". 2016-08-18.
  23. "Second Snowden could be behind sale of NSA hacking tools".
  24. "The Well-Rounded Data Scientist". 16 April 2014.
  25. "Becoming a Data Scientist Podcast Episode 03: Shlomo Argamon | Becoming a Data Scientist".
  26. "BCS Register of Members". wam.bcs.org. Retrieved 2018-10-05.
  27. "Sixth Annual Forensic Linguistics Distinguished Visitor Lecture" (PDF).
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