Assessing the impact of frame semantics on textual entailment

Burchardt, Aljoscha; Pennacchiotti, Marco; Thater, Stefan; Pinkal, Manfred

doi:10.1017/s1351324909990131

Cited by 31 publications

(21 citation statements)

References 16 publications

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“…Other tasks which have been studied based on FN include question answering (Narayanan and Harabagiu, 2004;Shen and Lapata, 2007), textual entailment (Burchardt et al, 2009) and paraphrase recognition (Padó and Lapata, 2005). This is the first work to explore the application of FN to event detection.…”

Section: Related Workmentioning

confidence: 99%

Leveraging FrameNet to Improve Automatic Event Detection

Liu¹,

Chen²,

He³

et al. 2016

Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

103

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Frames defined in FrameNet (FN) share highly similar structures with events in ACE event extraction program. An event in ACE is composed of an event trigger and a set of arguments. Analogously, a frame in FN is composed of a lexical unit and a set of frame elements, which play similar roles as triggers and arguments of ACE events respectively. Besides having similar structures, many frames in FN actually express certain types of events. The above observations motivate us to explore whether there exists a good mapping from frames to event-types and if it is possible to improve event detection by using FN. In this paper, we propose a global inference approach to detect events in FN. Further, based on the detected results, we analyze possible mappings from frames to event-types. Finally, we improve the performance of event detection and achieve a new state-of-the-art result by using the events automatically detected from FN.

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Section: Related Workmentioning

confidence: 99%

Leveraging FrameNet to Improve Automatic Event Detection

Liu¹,

Chen²,

He³

et al. 2016

Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

103

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“…Using two chan-2 Modal verbs are not or not systematically covered in WordNets or VerbNet; FrameNet relates modal verbs to their predominant sense only. Also, FrameNet's frame-to-frame relations are known to lack coverage (Burchardt et al, 2009). nels did not significantly improve results.…”

Section: Prior and Related Workmentioning

confidence: 99%

Multilingual Modal Sense Classification using a Convolutional Neural Network

Marasović¹,

Frank²

2016

Proceedings of the 1st Workshop on Representation Learning for NLP

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Modal sense classification (MSC) is a special WSD task that depends on the meaning of the proposition in the modal's scope. We explore a CNN architecture for classifying modal sense in English and German. We show that CNNs are superior to manually designed feature-based classifiers and a standard NN classifier. We analyze the feature maps learned by the CNN and identify known and previously unattested linguistic features. We benchmark the CNN on a standard WSD task, where it compares favorably to models using sense-disambiguated target vectors.

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“…Scheffczyk et al (2006) propose a reasoning procedure involving all types of FN relations. A detailed overview of applications of the frame matching to recognizing textual entailment is given in Burchardt et al (2009).…”

Section: Axioms Derived From Framenetmentioning

confidence: 99%

“…FrameNet has a shorter history in NLP applications than WordNet, but lately more and more researchers demonstrate its potential to improve the quality of question answering (e.g., Shen and Lapata, 2007) and recognizing textual entailment (e.g., Burchardt et al, 2009). 16 For more information about each frame relation see Sec.…”

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confidence: 99%

Integration of World Knowledge for Natural Language Understanding

Ovchinnikova¹

2012

Atlantis Thinking Machines

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Aims and scope of the seriesThis series publishes books resulting from theoretical research on and reproductions of general Artificial Intelligence (AI). The book series focuses on the establishment of new theories and paradigms in AI. At the same time, the series aims at exploring multiple scientific angles and methodologies, including results from research in cognitive science, neuroscience, theoretical and experimental AI, biology and from innovative interdisciplinary methodologies.All books in this series are co-published with Springer.For more information on this series and our other book series, please visit our website at:www.atlantis-press.comForeword Inference-based natural language understanding (NLU) was a thriving area of research in the 1970s and 1980s. It resulted in good theoretical work and in interesting small-scale systems. But in the early 1990s it foundered on three difficulties:• Parsers were not accurate enough to produce predicate-argument relations reliably, so that inference had no place to start.• Inference processes were not efficient enough nor accurate enough.• There was no large knowledge base designed for NLU applications.The first of these difficulties has been overcome by progress in statistical parsing. The second problem is one that many people, including Ekaterina Ovchinnikova, are working on now. The research described in this volume addresses the third difficulty, and indeed shows considerable promise in overcoming it. For this reason, I believe Dr. Ovchinnikova's work has a real potential to reignite interest in inference-based NLU in the computational linguistics community.A key notion in her work is that there already exists sufficient world knowledge in a variety of resources, at a level of precision that enables their translation into formal logic. To my mind, the most important of these are WordNet and FrameNet, especially the latter, and she describes the kind of information one can get out of these resources. She exploits in particular the hierarchical information and the glosses in WordNet, generating 600,000 axioms.She also describes how one can utilize FrameNet to generate about 50,000 axioms representing relations between words and frames, and about 5000 axioms representing frameframe relations. Her analysis of FrameNet is quite thorough, and I found this part of her work inspiring.She also critically discusses foundational ontologies such as DOLCE, SUMO. and Open-Cyc, and domain-specific ontologies of the sort being constructed for the Semantic Web. v vi Integration of World Knowledge for Natural Language UnderstandingShe examines the problems raised by semi-formal ontologies, like YAGO and ConceptNet, which have been gleaned from text or Netizens and which may be more difficult to translate into formal logic. She also shows how to use distributional data for a default mode of processing when the required knowledge is not available.Her use of knowledge from a variety of resources, combined into a single system, leads to the very hard problem of ensuring consisten...

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Assessing the impact of frame semantics on textual entailment

Cited by 31 publications

References 16 publications

Leveraging FrameNet to Improve Automatic Event Detection

Leveraging FrameNet to Improve Automatic Event Detection

Multilingual Modal Sense Classification using a Convolutional Neural Network

Integration of World Knowledge for Natural Language Understanding

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