PPT-Computational terminography: extracting knowledge from text

Špela Vintar Dept of Translation Studies University of Ljubljana Terminology Symposium Zadar 2223 August 2014 Overview Why computational terminography From unstructured data to knowledge. washingtonedu Abstract Extracting knowledge from text has long been a goal of AI Initial approaches were purely logical and brittle More recently the availability of large quantities of text on the Web has led to the develop ment of machine learning Research Scientist. OCLC Research. Extracting names and resolving identities in unstructured text. . Three problems in automated name . extraction. Recognize. Distinguish names from non-names.. Assign the name to a broadly recognized category.. : . From . Structured Summaries to Integrated Knowledge Base. ScAi Lab, CSD, UCLA. May 2014. Immense Knowledge From Web. Wikipedia. :. 280 + . languages. 20M + articles. 1B+ edits. DBpedia:. 110 + . languages. http://www.doi.gov/doilearn/trainingdownload.cfm he preferred browser for extracting these courses is FIREFOX. The files contained in each of these zipped folders can be run from a PCsuccess whenusi web site: www.cs.vt.edu/~kafura/CS6604. Today’s Class. Meet faculty and researchers. From a variety of knowledge domains. With a variety of perspectives and experiences related to computational thinking. Although most demos are implemented with word documents, this demo employs slides so that more details can be shown to the students as the drawing is constructed.. Rev: 20120913, AJP. Extracting Drawings. web site: www.cs.vt.edu/~kafura/CS6604. NRC Report on Pedagogy for CT. Second of two workshops. Focused on K12 Education. Identified different approaches to the teaching of computational thinking. What do these approaches and ideas mean for the university level?. a. s measures of text structure. and of reading comprehension. May 14, 2012. BSI . Nijmegen, Nederland. Roy . Clariana. RClariana@psu.edu. Clariana, R.B. (2010). Multi-decision approaches for eliciting knowledge structure. In D. Ifenthaler, P. Pirnay-Dummer, & N.M. Seel (Eds.), . Grades . 6–8 ELA I. Day 3.  . Welcome Back!. 2. Plusses. /Deltas . 3. We will be experiencing and building on ideas about knowledge, . comprehension, . and fluency.. Some reading of complex text and learning new ideas (feeling what students might feel), some thinking like teachers (what does it look like in the classroom? . Ernest Davis. Cognitum. 2016. July 11, 2016. TACIT . Toward Annotating Commonsense Inferences in Text. First text: Theft of the Mona Lisa. On a mundane morning in late summer in Paris, the impossible happened. The Mona Lisa vanished. On Sunday evening, August 20, 1911, Leonardo da Vinci's best-known painting was hanging in her usual place on the wall of the Salon . Grades 6 – 8. Summer . 2017.  . Welcome Back!. 2. Thank . You for Your Feedback!. +.  . 3. We will be experiencing and building ideas about knowledge, comprehension and fluency.. Some reading of complex text and learning new ideas (feeling what students might feel), some thinking like teachers (what does it look like in the classroom? how do I plan for this?). Karin Becker. Data Mining, Integration and Analysis. Knowledge Discovery. Web and Text Mining. Data Science. Recommendation Systems. Scalability and Performance. Reproducibility. Ana Lucia . Cetertich. ScAi Lab, CSD, UCLA. May 2014. Immense Knowledge From Web. Wikipedia. :. 280 + . languages. 20M + articles. 1B+ edits. DBpedia:. 110 + . languages. 2B+ facts. 4M. + subjects (en. ). …. …. Semantic Applications explored at UCLA. Yangqiu Song. Lane . Department of CSEE. West Virginia University. 1. Much of the work was done at UIUC. Collaborators. Dan Roth . Haixun. Wang . Shusen. Wang . Weizhu. Chen. 2. Text Categorization.