2014
DOI: 10.4204/eptcs.172.17
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A Study of Entanglement in a Categorical Framework of Natural Language

Abstract: In both quantum mechanics and corpus linguistics based on vector spaces, the notion of entanglement provides a means for the various subsystems to communicate with each other. In this paper we examine a number of implementations of the categorical framework of Coecke, Sadrzadeh and Clark (2010) for natural language, from an entanglement perspective. Specifically, our goal is to better understand in what way the level of entanglement of the relational tensors (or the lack of it) affects the compositional struct… Show more

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Cited by 45 publications
(49 citation statements)
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“…Similarity scores are integers ranging from 1 to 7. Another dataset 8 is created by extending VO to SubjectVerb-Object (SVO), and then assessing similarities by crowd sourcing (Kartsaklis and Sadrzadeh, 2014). The dataset GS11 created by Grefenstette and Sadrzadeh (2011) (100 pairs, 25 annotators) is also of the form SVO, but in each pair only the verbs are different (e.g.…”
Section: Phrase Similaritymentioning
confidence: 99%
See 1 more Smart Citation
“…Similarity scores are integers ranging from 1 to 7. Another dataset 8 is created by extending VO to SubjectVerb-Object (SVO), and then assessing similarities by crowd sourcing (Kartsaklis and Sadrzadeh, 2014). The dataset GS11 created by Grefenstette and Sadrzadeh (2011) (100 pairs, 25 annotators) is also of the form SVO, but in each pair only the verbs are different (e.g.…”
Section: Phrase Similaritymentioning
confidence: 99%
“…The weakness of "no inverse" suggests that relaxing the constraint of inverse matrices may hurt compositionaly, though our preliminary examination on word similarities did not find any difference. The GS11 dataset appears to favor models that can learn from interactions between the subject and object arguments, such as the non-linear model Wadd nl in Hashimoto et al (2014) and the entanglement model in Kartsaklis and Sadrzadeh (2014). However, these models do not show particular advantages on other datasets.…”
Section: Phrase Similaritymentioning
confidence: 99%
“…A question arises that whether our linguistic tensors are separable or not. We answered this question by measuring the degree of entanglement of our tensors and showing that the ones with a higher degree led to better results, as presented in Kartsaklis and Sadrzadeh [29].…”
Section: A Chronological Overview Of Discocatmentioning
confidence: 83%
“…Except for the hard similarity task, we also evaluate our approach on the transitive sentence similarity dataset (Kartsaklis and Sadrzadeh, 2014), which contains 108 pairs of transitive sentences: short phrases containing a single subject, object and verb (e.g., agent sell property). It also has another dataset which consists of 200 sentence pairs.…”
Section: Transitive Sentence Similaritymentioning
confidence: 99%