On the role of lexical and world knowledge in RTE3

Clark, Peter E.; Harrison, Philip; Thompson, John A.; Murray, William R.; Hobbs, Jerry R.; Fellbaum, Christiane

doi:10.3115/1654536.1654549

Cited by 32 publications

(30 citation statements)

References 5 publications

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“…The last observation corresponds to the findings reported by Clark et al (2007). Manually going through the T-H pairs used in the experiment we discovered that finding a proof failed in 12 cases because of a) incompleteness of the FATE annotation (8 pairs), b) Boxer's processing errors (5 pairs), c) lack of general non-definitional knowledge (7 pairs).…”

Section: Case Studysupporting

confidence: 85%

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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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Section: Case Studysupporting

confidence: 85%

“…6 Examples can be found in (Clark et al, 2007). Lexical-semantic knowledge obviously depends on the target language; existing lexical-semantic databases are for the most part domain-independent.…”

Section: Constructing a Knowledge Basementioning

confidence: 99%

Integration of World Knowledge for Natural Language Understanding

Ovchinnikova¹

2012

Atlantis Thinking Machines

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“…Systems based on natural logic (MacCartney and Manning, 2007) use natural language as a representation, but still perform inference using a structured algebra rather than a statistical model. Regardless of the inference procedure, improvements to external lexical resources can improve RTE systems across the board (Clark et al, 2007).…”

Section: Recognizing Textual Entailmentmentioning

confidence: 99%

Adding Semantics to Data-Driven Paraphrasing

Pavlick¹,

Bos²,

Nissim³

et al. 2015

Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Confere

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We add an interpretable semantics to the paraphrase database (PPDB). To date, the relationship between phrase pairs in the database has been weakly defined as approximately equivalent. We show that these pairs represent a variety of relations, including directed entailment (little girl/girl) and exclusion (nobody/someone). We automatically assign semantic entailment relations to entries in PPDB using features derived from past work on discovering inference rules from text and semantic taxonomy induction. We demonstrate that our model assigns these relations with high accuracy. In a downstream RTE task, our labels rival relations from WordNet and improve the coverage of a proof-based RTE system by 17%.

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“…Bar-Haim, Szpektor and Glickman 2005;Vanderwende and Dolan 2005;Litkowski 2006;Clark et al 2007) indicate that the level of granularity offered by FrameNet is relevant for modelling many phenomena which occur in the current textual entailment corpora. Bar-Haim, Szpektor and Glickman 2005;Vanderwende and Dolan 2005;Litkowski 2006;Clark et al 2007) indicate that the level of granularity offered by FrameNet is relevant for modelling many phenomena which occur in the current textual entailment corpora.…”

Section: The Role Of Frame Semantics In Rtementioning

confidence: 99%

Assessing the impact of frame semantics on textual entailment

Burchardt¹,

Pennacchiotti²,

Thater³

et al. 2009

Nat. Lang. Eng.

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In this article, we underpin the intuition that frame semantic information is a useful resource for modelling textual entailment. To this end, we provide a manual frame semantic annotation for the test set used in the second recognizing textual entailment (RTE) challengethe FrameNet-annotated textual entailment (FATE) corpus -and discuss experiments we conducted on this basis. In particular, our experiments show that the frame semantic lexicon provided by the Berkeley FrameNet project provides surprisingly good coverage for the task at hand. We identify issues of automatic semantic analysis components, as well as insufficient modelling of the information provided by frame semantic analysis as reasons for ambivalent results of current systems based on frame semantics.

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On the role of lexical and world knowledge in RTE3

Cited by 32 publications

References 5 publications

Integration of World Knowledge for Natural Language Understanding

Integration of World Knowledge for Natural Language Understanding

Adding Semantics to Data-Driven Paraphrasing

Assessing the impact of frame semantics on textual entailment

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