<?xml version="1.0" encoding="UTF-8"?><xml><records><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Bonaventura Coppola</style></author><author><style face="normal" font="default" size="100%">Aldo Gangemi</style></author><author><style face="normal" font="default" size="100%">Alfio Massimiliano Gliozzo</style></author><author><style face="normal" font="default" size="100%">Davide Picca</style></author><author><style face="normal" font="default" size="100%">Valentina Presutti</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Frame Detection over the Semantic Web</style></title><secondary-title><style face="normal" font="default" size="100%">Proceedings of the 6th European Semantic Web Conference</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2009</style></year></dates><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">Research in ontology learning from text has been mainly focused on entity recognition, taxonomy induction and relation extraction. In this work we approach a more challenging research issue, consisting in detecting semantic frames from texts, and using them to encode web ontologies. We exploit a new generation frame detection system, which is able to identify FrameNet frames and their argument boundaries from text parsing. In addition, we enrich frames with the results provided by a super-sense tagger, which extracts and classiﬁes concepts and named entities from texts according to WordNet super-senses. The enrichment results include argument restrictions for the elements of a frame, and domain specializations, based on domain lexical unit detection for the target of a frame. The results are encoded according to the Lexical MetaModel, which allows a complete translation of lexical resources, keeps trace of the learning metadata, and enables custom transformations of enriched frames into modular ontology components, known as content ontology design patterns.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Bonaventura Coppola</style></author><author><style face="normal" font="default" size="100%">Alessandro Moschitti</style></author><author><style face="normal" font="default" size="100%">Sara Tonelli</style></author><author><style face="normal" font="default" size="100%">Riccardi, Giuseppe</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Automatic FrameNet-based annotation of Conversational Speech</style></title><secondary-title><style face="normal" font="default" size="100%">Proceedings of IEEE-SLT 2008</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2008</style></year><pub-dates><date><style  face="normal" font="default" size="100%">December</style></date></pub-dates></dates><pub-location><style face="normal" font="default" size="100%">Goa, India</style></pub-location><pages><style face="normal" font="default" size="100%">73-76</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Bonaventura Coppola</style></author><author><style face="normal" font="default" size="100%">Alessandro Moschitti</style></author><author><style face="normal" font="default" size="100%">Riccardi, Giuseppe</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Shallow Semantic Parsing for Spoken Language Understanding</style></title><secondary-title><style face="normal" font="default" size="100%">Proceedings of Human Language Technologies: The 2009 Annual Conference of the North American Chapter of the Association for Computational Linguistics, Companion Volume: Short Papers</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2009</style></year><pub-dates><date><style  face="normal" font="default" size="100%">June</style></date></pub-dates></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://www.aclweb.org/anthology/N/N09/N09-2022</style></url></web-urls></urls><publisher><style face="normal" font="default" size="100%">Association for Computational Linguistics</style></publisher><pub-location><style face="normal" font="default" size="100%">Boulder, Colorado</style></pub-location><pages><style face="normal" font="default" size="100%">85–88</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">Most Spoken Dialog Systems are based on speech grammars and frame/slot semantics. The semantic descriptions of input utterances are usually defined ad-hoc with no ability to generalize beyond the target application domain or to learn from annotated corpora. The approach we propose in this paper exploits machine learning of frame semantics, borrowing its theoretical model from computational linguistics. While traditional automatic Semantic Role Labeling approaches on written texts may not perform as well on spoken dialogs, we show successful experiments on such porting. Hence, we design and evaluate automatic FrameNet-based parsers both for English written texts and for Italian dialog utterances. The results show that disfluencies of dialog data do not severely hurt performance. Also, a small set of FrameNet-like manual annotations is enough for realizing accurate Semantic Role Labeling on the target domains of typical Dialog Systems.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Bonaventura Coppola</style></author><author><style face="normal" font="default" size="100%">Alessandro Moschitti</style></author></authors><secondary-authors><author><style face="normal" font="default" size="100%">Nicoletta Calzolari (Conference Chair)</style></author><author><style face="normal" font="default" size="100%">Khalid Choukri</style></author><author><style face="normal" font="default" size="100%">Bente Maegaard</style></author><author><style face="normal" font="default" size="100%">Joseph Mariani</style></author><author><style face="normal" font="default" size="100%">Jan Odjik</style></author><author><style face="normal" font="default" size="100%">Stelios Piperidis</style></author><author><style face="normal" font="default" size="100%">Mike Rosner</style></author><author><style face="normal" font="default" size="100%">Daniel Tapias</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">A General Purpose FrameNet-based Shallow Semantic Parser</style></title><secondary-title><style face="normal" font="default" size="100%">Proceedings of the Seventh conference on International Language Resources and Evaluation (LREC'10)</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2010</style></year><pub-dates><date><style  face="normal" font="default" size="100%">may</style></date></pub-dates></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://www.lrec-conf.org/proceedings/lrec2010/summaries/893.html</style></url></web-urls></urls><publisher><style face="normal" font="default" size="100%">European Language Resources Association (ELRA)</style></publisher><pub-location><style face="normal" font="default" size="100%">Valletta, Malta</style></pub-location><isbn><style face="normal" font="default" size="100%">2-9517408-6-7</style></isbn><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">In this paper we present a new FrameNet-based Shallow Semantic Parser. While Shallow Semantic Parsing has been a popular Natural Language Processing task since the 2004 and 2005 CoNLL Shared Task editions, efforts in extending such task to the FrameNet setting have been constrained by practical software engineering issues. We hereby analyze these issues, identify desirable requirements for a practical parsing framework, and show the results of our software implementation. In particular, we attempt at meeting requirements arising from both a) the need of a flexible environment supporting current ongoing research, and b) the willingness of providing an effective platform supporting preliminary application prototypes in the field. After introducing the task of FrameNet-based Shallow Semantic Parsing, we sketch the system processing workflow and summarize a set of successful experimental results, directing the reader to previous published papers for extended experiment descriptions and wider discussion of the achieved results.</style></abstract><notes><style face="normal" font="default" size="100%">There is a demo at: http://cicerone.dit.unitn.it/FrameSemantics/Demo.php (operational as of 2010.11.20)</style></notes></record></records></xml>