= Extracting structured information from text = [[https://is.muni.cz/auth/predmet/fi/ia161|IA161 Advanced NLP Course]], Course Guarantee: Aleš Horák Prepared by: Vojtěch Kovář == TODO til 31.5.2015 == 1. choose particular papers for [[#References|References]] below (that will serve as input for the lecture later on) 1. prepare the [[#PracticalSession|Practical Session]] == State of the Art == Information extraction (IE) is a technology based on analyzing natural language in order to extract snippets of information. The process takes texts (and sometimes speech) as input and produces fixed-format, unambiguous data as output. This data may be used directly for display to users, or may be stored in a database or spreadsheet for later analysis, or may be used for indexing purposes in information retrieval (IR) applications such as Internet search engines like Google. === References === 1. Cunningham, Hamish. [https://gate.ac.uk/sale/ell2/ie/ An Introduction to Information Extraction]. Encyclopedia of Language and Linguistics, 2nd Edition. Elsevier, 2005. 1. Chang, Chia-Hui, et al.[https://www.researchgate.net/profile/Khaled_Shaalan/publication/200110627_A_Survey_of_Web_Information_Extraction_Systems/links/0912f50abd8c6b314d000000.pdf A Survey of Web Information Extraction Systems]. Knowledge and Data Engineering, IEEE Transactions on 18.10 (2006). 1. Banko, Michele, et al. [http://www.aaai.org/Papers/IJCAI/2007/IJCAI07-429.pdf Open information extraction for the web]. IJCAI. Vol. 7. 2007. 1. Fader, Anthony, Soderland, Stephen and Etzioni, Oren. [http://dl.acm.org/citation.cfm?id=2145596 Identifying relations for open information extraction]. In Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP '11). Association for Computational Linguistics, Stroudsburg, PA, USA, 2011. == Practical Session == You are given several [attachment:wiki.txt short excerpts from Czech wikipedia] as a plain text. They were analyzed by automatic sentence detection, tokenization (unitok tool), morphological analysis and tagging (desamb tool), and syntactic analysis (SET tool, with --long-phrases option) and [attachment:wiki.phrases this is the result]. Write a short program in Python which will extract simple information about who was who, from the parsed file. The result should look like [attachment:wiki.output this file].