@inproceedings{ovchinnikova-etal-2010-data,
title = "Data-Driven and Ontological Analysis of {F}rame{N}et for Natural Language Reasoning",
author = "Ovchinnikova, Ekaterina and
Vieu, Laure and
Oltramari, Alessandro and
Borgo, Stefano and
Alexandrov, Theodore",
editor = "Calzolari, Nicoletta and
Choukri, Khalid and
Maegaard, Bente and
Mariani, Joseph and
Odijk, Jan and
Piperidis, Stelios and
Rosner, Mike and
Tapias, Daniel",
booktitle = "Proceedings of the Seventh International Conference on Language Resources and Evaluation ({LREC}'10)",
month = may,
year = "2010",
address = "Valletta, Malta",
publisher = "European Language Resources Association (ELRA)",
url = "http://www.lrec-conf.org/proceedings/lrec2010/pdf/84_Paper.pdf",
abstract = "This paper focuses on the improvement of the conceptual structure of FrameNet (FN) for the sake of applying this resource to knowledge-intensive NLP tasks requiring reasoning, such as question answering, information extraction etc. In this paper we show that in addition to coverage incompleteness, the current version of FN suffers from conceptual inconsistency and lacks axiomatization which can prevent appropriate inferences. For the sake of discovering and classifying conceptual problems in FN we investigate the FrameNet-Annotated corpus for Textual Entailment. Then we propose a methodology for improving the conceptual organization of FN. The main issue we focus on in our study is enriching, axiomatizing and cleaning up frame relations. Our methodology includes a data-driven analysis of frames resulting in discovering new frame relations and an ontological analysis of frames and frame relations resulting in axiomatizing relations and formulating constraints on them. In this paper, frames and frame relations are analyzed in terms of the DOLCE formal ontology. Additionally, we have described a case study aiming at demonstrating how the proposed methodology works in practice as well as investigating the impact of the restructured and axiomatized frame relations on recognizing textual entailment.",
}
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<abstract>This paper focuses on the improvement of the conceptual structure of FrameNet (FN) for the sake of applying this resource to knowledge-intensive NLP tasks requiring reasoning, such as question answering, information extraction etc. In this paper we show that in addition to coverage incompleteness, the current version of FN suffers from conceptual inconsistency and lacks axiomatization which can prevent appropriate inferences. For the sake of discovering and classifying conceptual problems in FN we investigate the FrameNet-Annotated corpus for Textual Entailment. Then we propose a methodology for improving the conceptual organization of FN. The main issue we focus on in our study is enriching, axiomatizing and cleaning up frame relations. Our methodology includes a data-driven analysis of frames resulting in discovering new frame relations and an ontological analysis of frames and frame relations resulting in axiomatizing relations and formulating constraints on them. In this paper, frames and frame relations are analyzed in terms of the DOLCE formal ontology. Additionally, we have described a case study aiming at demonstrating how the proposed methodology works in practice as well as investigating the impact of the restructured and axiomatized frame relations on recognizing textual entailment.</abstract>
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%0 Conference Proceedings
%T Data-Driven and Ontological Analysis of FrameNet for Natural Language Reasoning
%A Ovchinnikova, Ekaterina
%A Vieu, Laure
%A Oltramari, Alessandro
%A Borgo, Stefano
%A Alexandrov, Theodore
%Y Calzolari, Nicoletta
%Y Choukri, Khalid
%Y Maegaard, Bente
%Y Mariani, Joseph
%Y Odijk, Jan
%Y Piperidis, Stelios
%Y Rosner, Mike
%Y Tapias, Daniel
%S Proceedings of the Seventh International Conference on Language Resources and Evaluation (LREC’10)
%D 2010
%8 May
%I European Language Resources Association (ELRA)
%C Valletta, Malta
%F ovchinnikova-etal-2010-data
%X This paper focuses on the improvement of the conceptual structure of FrameNet (FN) for the sake of applying this resource to knowledge-intensive NLP tasks requiring reasoning, such as question answering, information extraction etc. In this paper we show that in addition to coverage incompleteness, the current version of FN suffers from conceptual inconsistency and lacks axiomatization which can prevent appropriate inferences. For the sake of discovering and classifying conceptual problems in FN we investigate the FrameNet-Annotated corpus for Textual Entailment. Then we propose a methodology for improving the conceptual organization of FN. The main issue we focus on in our study is enriching, axiomatizing and cleaning up frame relations. Our methodology includes a data-driven analysis of frames resulting in discovering new frame relations and an ontological analysis of frames and frame relations resulting in axiomatizing relations and formulating constraints on them. In this paper, frames and frame relations are analyzed in terms of the DOLCE formal ontology. Additionally, we have described a case study aiming at demonstrating how the proposed methodology works in practice as well as investigating the impact of the restructured and axiomatized frame relations on recognizing textual entailment.
%U http://www.lrec-conf.org/proceedings/lrec2010/pdf/84_Paper.pdf
Markdown (Informal)
[Data-Driven and Ontological Analysis of FrameNet for Natural Language Reasoning](http://www.lrec-conf.org/proceedings/lrec2010/pdf/84_Paper.pdf) (Ovchinnikova et al., LREC 2010)
ACL