@conference {CoppolaMoschitti2010LREC:FNshallowSemParser,
	title = {A General Purpose FrameNet-based Shallow Semantic Parser},
	booktitle = {Proceedings of the Seventh conference on International Language Resources and Evaluation (LREC{\textquoteright}10)},
	year = {2010},
	note = {There is a demo at: http://cicerone.dit.unitn.it/FrameSemantics/Demo.php (operational as of 2010.11.20)},
	month = {may},
	publisher = {European Language Resources Association (ELRA)},
	organization = {European Language Resources Association (ELRA)},
	address = {Valletta, Malta},
	abstract = {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.},
	isbn = {2-9517408-6-7},
	url = {http://www.lrec-conf.org/proceedings/lrec2010/summaries/893.html},
	author = {Bonaventura Coppola and Alessandro Moschitti},
	editor = {Nicoletta Calzolari (Conference Chair) and Khalid Choukri and Bente Maegaard and Joseph Mariani and Jan Odjik and Stelios Piperidis and Mike Rosner and Daniel Tapias}
}
@conference {CoppolaEtAl2009:FrameDetectOverSemWeb,
	title = {Frame Detection over the Semantic Web},
	booktitle = {Proceedings of the 6th European Semantic Web Conference},
	year = {2009},
	abstract = {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.},
	author = {Bonaventura Coppola and Aldo Gangemi and Alfio Massimiliano Gliozzo and Davide Picca and Valentina Presutti}
}
@conference {CoppolaEtAl2008:AutoFNAnnoConvSpch,
	title = {Automatic {FrameNet}-based annotation of Conversational Speech},
	booktitle = {Proceedings of IEEE-SLT 2008},
	year = {2008},
	month = {December},
	pages = {73-76},
	address = {Goa, India},
	author = {Bonaventura Coppola and Alessandro Moschitti and Sara Tonelli and Riccardi, Giuseppe}
}
@conference {CoppolaEtAlNAACL2009:ShallowSemPars4SLU,
	title = {Shallow Semantic Parsing for Spoken Language Understanding},
	booktitle = {Proceedings of Human Language Technologies: The 2009 Annual Conference of the North American Chapter of the Association for Computational Linguistics, Companion Volume: Short Papers},
	year = {2009},
	month = {June},
	pages = {85{\textendash}88},
	publisher = {Association for Computational Linguistics},
	organization = {Association for Computational Linguistics},
	address = {Boulder, Colorado},
	abstract = {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.},
	url = {http://www.aclweb.org/anthology/N/N09/N09-2022},
	author = {Bonaventura Coppola and Alessandro Moschitti and Riccardi, Giuseppe}
}
