German Rigau Ph.D. and B.A. in Computer Science by the Universitat Politecnica de Catalunya (UPC). Formerly member of the Computer Science department at the UPC and member of the TALP research group of the UPC, currently, he is teaching at the Computer Science Faculty of the EHU as an Associate Professor. He has published more than hundred-refereed articles and conference papers in the area of Natural Language Processing, and in particular Acquisition of Lexical Knowledge, Word Sense Disambiguation, Semantic Processing and Inferencing.
He has been involved in several European research projects (ACQUILEX, ACQUILEX II, EuroWordNet, NAMIC, MEANING, KYOTO, PATHS, OpeNER and NewsReader). He coordinated the MEANING project (IST-2001-34460) and the local groups for NAMIC, KYOTO, OpeNER and NewsReader. He has been also involved in several Spanish National research projects (ITEM, HERMES, SENSEM, KNOW, KNOW2, SKaTer and TUNER). Currently, he is coordinating the TUNER project.
He served as PC member and reviewer of the main international conferences and workshops in NLP and AI including ACL, EACL, NAACL, COLING, AAAI, ECAI, IJCAI, EMNLP, IJCNLP, CoNLL, TSD, SENSEVAL/SEMEVAL and IWC. He also served as reviewer of International Journals including: Computers and the Humanities, Journal of Natural Language Engineering, Journal of Artificial Intelligence Research and Artificial Intelligence. He has also participated in all editions of the international competition of SENSEVAL.
Currently, he is member of the Association for Computational Linguistics (ACL) and the Spanish Society for Natural Language Processing (SEPLN).
Big Data for Natural Language Processing
Requirements in computational power have grown dramatically in recent years. This is also the case in many language processing tasks, due to the overwhelming and ever increasing amount of textual information that must be processed in a reasonable time frame. This scenario has led to a paradigm shift in the computing architectures and large-scale data processing strategies used in the Natural Language Processing field.
This talk presents new distributed architectures and technology for scaling up text analysis running complete chains of linguistic processors on parallel architectures. The talk also describes a series of experiments carried out with the goal of analyzing the scaling capabilities of the current language processing pipelines on large clusters with many processing nodes.