Proceedings of the 55th Annual Meeting of the Association For Computational Linguistics (Volume 1: Long Papers) 2017
DOI: 10.18653/v1/p17-1025
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Verb Physics: Relative Physical Knowledge of Actions and Objects

Abstract: Learning commonsense knowledge from natural language text is nontrivial due to reporting bias: people rarely state the obvious, e.g., "My house is bigger than me." However, while rarely stated explicitly, this trivial everyday knowledge does influence the way people talk about the world, which provides indirect clues to reason about the world. For example, a statement like, "Tyler entered his house" implies that his house is bigger than Tyler.In this paper, we present an approach to infer relative physical kno… Show more

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Cited by 70 publications
(83 citation statements)
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References 27 publications
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“…More generally, numeracy is one type of emergent knowledge. For instance, embeddings may capture the size of objects (Forbes and Choi, 2017), speed of vehicles, and many other "commonsense" phenomena (Yang et al, 2018). Vendrov et al (2016) introduce methods to encode the order of such phenomena into embeddings for concepts such as hypernymy; our work and Yang et al (2018) show that a relative ordering naturally emerges for certain concepts.…”
Section: Discussion and Related Workmentioning
confidence: 84%
“…More generally, numeracy is one type of emergent knowledge. For instance, embeddings may capture the size of objects (Forbes and Choi, 2017), speed of vehicles, and many other "commonsense" phenomena (Yang et al, 2018). Vendrov et al (2016) introduce methods to encode the order of such phenomena into embeddings for concepts such as hypernymy; our work and Yang et al (2018) show that a relative ordering naturally emerges for certain concepts.…”
Section: Discussion and Related Workmentioning
confidence: 84%
“…relative sizes in man-swallowpaintball/desk). Previously, Forbes and Choi (2017) proposed a three level (3-LEVEL) featurization scheme, where an object-pair can take 3 values for, e.g. relative size: {−1, 0, 1} (i.e.…”
Section: World Knowledge Featuresmentioning
confidence: 99%
“…Physical world knowledge modeling appears frequently in more closely related work. Bagherinezhad et al (2016) combine computer vision and text-based information extraction to learn the relative sizes of objects; Forbes and Choi (2017) crowdsource physical knowledge along specified dimensions and employ belief propagation to learn relative physical attributes of object pairs. Wang et al (2017) propose a multimodal LDA to learn the definitional properties (e.g.…”
Section: Introductionmentioning
confidence: 99%
“…Knowledge extraction from text corpora is a vast research area (Banko et al, 2007;Mitchell et al, 2015), yet works that specifically target commonsense knowledge are comparatively rare (Gordon, 2014). Our focus is on the specific approach to mining commonsense knowledge by casting it as a KBC task, as in Li et al (2016) and Forbes and Choi (2017).…”
Section: Related Workmentioning
confidence: 99%