Marco Pennacchiotti


2011

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Automatically Building Training Examples for Entity Extraction
Marco Pennacchiotti | Patrick Pantel
Proceedings of the Fifteenth Conference on Computational Natural Language Learning

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Linguistic Redundancy in Twitter
Fabio Massimo Zanzotto | Marco Pennacchiotti | Kostas Tsioutsiouliklis
Proceedings of the 2011 Conference on Empirical Methods in Natural Language Processing

2010

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SemEval-2010 Task 8: Multi-Way Classification of Semantic Relations between Pairs of Nominals
Iris Hendrickx | Su Nam Kim | Zornitsa Kozareva | Preslav Nakov | Diarmuid Ó Séaghdha | Sebastian Padó | Marco Pennacchiotti | Lorenza Romano | Stan Szpakowicz
Proceedings of the 5th International Workshop on Semantic Evaluation

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Detecting controversies in Twitter: a first study
Marco Pennacchiotti | Ana-Maria Popescu
Proceedings of the NAACL HLT 2010 Workshop on Computational Linguistics in a World of Social Media

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Proceedings of the 2010 Workshop on GEometrical Models of Natural Language Semantics
Roberto Basili | Marco Pennacchiotti
Proceedings of the 2010 Workshop on GEometrical Models of Natural Language Semantics

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Expanding textual entailment corpora fromWikipedia using co-training
Fabio Massimo Zanzotto | Marco Pennacchiotti
Proceedings of the 2nd Workshop on The People’s Web Meets NLP: Collaboratively Constructed Semantic Resources

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Open Entity Extraction from Web Search Query Logs
Alpa Jain | Marco Pennacchiotti
Proceedings of the 23rd International Conference on Computational Linguistics (Coling 2010)

2009

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Measuring Frame Relatedness
Marco Pennacchiotti | Michael Wirth
Proceedings of the 12th Conference of the European Chapter of the ACL (EACL 2009)

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Entity Extraction via Ensemble Semantics
Marco Pennacchiotti | Patrick Pantel
Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing

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Proceedings of the Workshop on Geometrical Models of Natural Language Semantics
Roberto Basili | Marco Pennacchiotti
Proceedings of the Workshop on Geometrical Models of Natural Language Semantics

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SemEval-2010 Task 8: Multi-Way Classification of Semantic Relations Between Pairs of Nominals
Iris Hendrickx | Su Nam Kim | Zornitsa Kozareva | Preslav Nakov | Diarmuid Ó Séaghdha | Sebastian Padó | Marco Pennacchiotti | Lorenza Romano | Stan Szpakowicz
Proceedings of the Workshop on Semantic Evaluations: Recent Achievements and Future Directions (SEW-2009)

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Proceedings of the 2009 Workshop on Applied Textual Inference (TextInfer)
Chris Callison-Burch | Ido Dagan | Christopher Manning | Marco Pennacchiotti | Fabio Massimo Zanzotto
Proceedings of the 2009 Workshop on Applied Textual Inference (TextInfer)

2008

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Combining Word Sense and Usage for Modeling Frame Semantics
Diego De Cao | Danilo Croce | Marco Pennacchiotti | Roberto Basili
Semantics in Text Processing. STEP 2008 Conference Proceedings

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Semantic Role Assignment for Event Nominalisations by Leveraging Verbal Data
Sebastian Padó | Marco Pennacchiotti | Caroline Sporleder
Proceedings of the 22nd International Conference on Computational Linguistics (Coling 2008)

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Automatic induction of FrameNet lexical units
Marco Pennacchiotti | Diego De Cao | Roberto Basili | Danilo Croce | Michael Roth
Proceedings of the 2008 Conference on Empirical Methods in Natural Language Processing

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FATE: a FrameNet-Annotated Corpus for Textual Entailment
Aljoscha Burchardt | Marco Pennacchiotti
Proceedings of the Sixth International Conference on Language Resources and Evaluation (LREC'08)

Several studies indicate that the level of predicate-argument structure is relevant for modeling prevalent phenomena in current textual entailment corpora. Although large resources like FrameNet have recently become available, attempts to integrate this type of information into a system for textual entailment did not confirm the expected gain in performance. The reasons for this are not fully obvious; candidates include FrameNet’s restricted coverage, limitations of semantic parsers, or insufficient modeling of FrameNet information. To enable further insight on this issue, in this paper we present FATE (FrameNet-Annotated Textual Entailment), a manually crafted, fully reliable frame-annotated RTE corpus. The annotation has been carried out over the 800 pairs of the RTE-2 test set. This dataset offers a safe basis for RTE systems to experiment, and enables researchers to develop clearer ideas on how to effectively integrate frame knowledge in semantic inferenence tasks like recognizing textual entailment. We describe and present statistics over the adopted annotation, which introduces a new schema based on full-text annotation of so called relevant frame evoking elements.

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A Web Browser Extension for Growing-up Ontological Knowledge from Traditional Web Content
Maria Teresa Pazienza | Marco Pennacchiotti | Armando Stellato
Proceedings of the Sixth International Conference on Language Resources and Evaluation (LREC'08)

While the Web is facing interesting new changes in the way users access, interact and even participate to its growth, the most traditional applications dedicated to its fruition: web browsers, are not responding with the same euphoric boost for innovation, mostly relying on third party or open-source community-driven extensions for addressing the new Social and Semantic Web trends and technologies. This technological and decisional gap, which is probably due to the lack of a strong standardization commitment on the one side (Web 2.0/Social Web) and in the delay of massive adherence to new officially approved standards (W3C approved Semantic Web languages), has to be filled by successful stories which could lay the path for the evolution of browsers. In this work we present a novel web browser extension which combines several features coming from the worlds of terminology and information extraction, semantic annotation and knowledge management, to support users in the process of both keeping track of interesting information they find on the web, and organizing its associated content following knowledge representation standards offered by the Semantic Web

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Towards a Vector Space Model for FrameNet-like Resources
Marco Pennacchiotti | Diego De Cao | Paolo Marocco | Roberto Basili
Proceedings of the Sixth International Conference on Language Resources and Evaluation (LREC'08)

In this paper, we present an original framework to model frame semantic resources (namely, FrameNet) using minimal supervision. This framework can be leveraged both to expand an existing FrameNet with new knowledge, and to induce a FrameNet in a new language. Our hypothesis is that a frame semantic resource can be modeled and represented by a suitable semantic space model. The intuition is that semantic spaces are an effective model of the notion of “being characteristic of a frame” for both lexical elements and full sentences. The paper gives two main contributions. First, it shows that our hypothesis is valid and can be successfully implemented. Second, it explores different types of semantic VSMs, outlining which one is more suitable for representing a frame semantic resource. In the paper, VSMs are used for modeling the linguistic core of a frame, the lexical units. Indeed, if the hypothesis is verified for these units, the proposed framework has a much wider application.

2007

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The Domain Restriction Hypothesis: Relating Term Similarity and Semantic Consistency
Alfio Massimiliano Gliozzo | Marco Pennacchiotti | Patrick Pantel
Human Language Technologies 2007: The Conference of the North American Chapter of the Association for Computational Linguistics; Proceedings of the Main Conference

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Shallow Semantic in Fast Textual Entailment Rule Learners
Fabio Massimo Zanzotto | Marco Pennacchiotti | Alessandro Moschitti
Proceedings of the ACL-PASCAL Workshop on Textual Entailment and Paraphrasing

2006

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Espresso: Leveraging Generic Patterns for Automatically Harvesting Semantic Relations
Patrick Pantel | Marco Pennacchiotti
Proceedings of the 21st International Conference on Computational Linguistics and 44th Annual Meeting of the Association for Computational Linguistics

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Ontologizing Semantic Relations
Marco Pennacchiotti | Patrick Pantel
Proceedings of the 21st International Conference on Computational Linguistics and 44th Annual Meeting of the Association for Computational Linguistics

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Discovering Asymmetric Entailment Relations between Verbs Using Selectional Preferences
Fabio Massimo Zanzotto | Marco Pennacchiotti | Maria Teresa Pazienza
Proceedings of the 21st International Conference on Computational Linguistics and 44th Annual Meeting of the Association for Computational Linguistics

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Automatically Assessing Review Helpfulness
Soo-Min Kim | Patrick Pantel | Tim Chklovski | Marco Pennacchiotti
Proceedings of the 2006 Conference on Empirical Methods in Natural Language Processing

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A Bootstrapping Algorithm for Automatically Harvesting Semantic Relations
Marco Pennacchiotti | Patrick Pantel
Proceedings of the Fifth International Workshop on Inference in Computational Semantics (ICoS-5)

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Mixing WordNet, VerbNet and PropBank for studying verb relations
Maria Teresa Pazienza | Marco Pennacchiotti | Fabio Massimo Zanzotto
Proceedings of the Fifth International Conference on Language Resources and Evaluation (LREC’06)

In this paper we present a novel resource for studying the semantics of verb relations. The resource is created by mixing sense relational knowledge enclosed in WordNet, frame knowledge enclosed in VerbNet and corpus knowledge enclosed in PropBank. As a result, a set of about 1000 frame pairs is made available. A frame pair represents a pair of verbs in a peculiar semantic relation accompanied with specific information, such as: the syntactic-semantic frames of the two verbs, the mapping among their thematic roles and a set of textual examples extracted from the PennTreeBank. We specifically focus on four relations: Troponymy, Causation, Entailment and Antonymy. The different steps required for the mapping are described in detail and statistics on resource mutual coverage are reported. We also propose a practical use of the resource for the task of Textual Entailment acquisition and for Question Answering. A first attempt for automate the mapping among verb arguments is also presented: early experiments show that simple techniques can achieve good results, up to 85% F-Measure.

2005

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Discovering Entailment Relations Using “Textual Entailment Patterns”
Fabio Massimo Zanzotto | Maria Teresa Pazienza | Marco Pennacchiotti
Proceedings of the ACL Workshop on Empirical Modeling of Semantic Equivalence and Entailment