Jussi Karlgren


Works for
Jussi Karlgren https://www.kth.se/social/files/50f6b437f27654557248b780/jussi.2011.11.22.NO_200.jpg Kungliga Tekniska högskolan

About me

Jussi Karlgren is an adjunct professor of language technology at KTH and a founding partner of the text analysis company Gavagai where he spends most of his time. He holds a PhD in computational linguistics, a Ph Lic in Computer and Systems Sciences, both from Stockholm University. He also holds the title of docent (adjoint professor) of language technology at Helsinki University. He has worked with research and development in information access-related language technology since 1987 at IBM Nordic laboratories, at the Swedish Institute of Computer Science (SICS), at Xerox PARC, at New York University, and at Yahoo! Research in Barcelona.

His research interests are modelling genre and stylistics, studying the negotiation of meaning in human communication, and on evaluating and validating information systems. He has worked on projects on natural human-computer dialogue, on automatic translation, and on modelling energy usage in homes and workplaces.

In the near future he will be pursuing two distinct but related research goals: 

  1. How to apply distributional models to study the referentiality of verb phrases in language use which can be localised in time and space. This will mean collecting language which is being used in various situations and relating the expressions to e.g. sensor data. The confluence of ubiquitous computing, streaming text analysis, and mobile and positional authoring of text provide an opportunity to define first steps towards a theory of digital pragmatics. 
  2. How to apply text analysis, especially stylistic analysis methods and distributional models of meaning to the needs of learning and exploring text collections. This will mean understanding what linguistic items encode authority, argumentation, uncertainty, topical development, as well as other macro-level functional features of language use. The use case of digital learning has matured together but separately from the text analysis technologies to the point where great strides forward can be expected in the near future. 

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