Statistical models for the induction and use of selectional preferences

Light, Marc; Greiff, Warren R.

doi:10.1207/s15516709cog2603_4

Cited by 21 publications

(9 citation statements)

References 12 publications

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“…Th is suggestion is in line with the selectional preference approach (Light, Greiff 2002) to metaphor identifi cation (Wilks 1975), suggesting that metaphorical language-use involves some kind of violation of literal use. For instance, the phrase 'sweet baby' is metaphorical as 'sweet' is an embodied category basically applicable to objects we can taste.…”

Section: The Model and The Algorithmsupporting

confidence: 60%

Opposition theory and computational semiotics

Assaf¹,

Cohen

Danesi

et al. 2015

SSS

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Abstract. Opposition theory suggests that binary oppositions (e.g., high vs. low) underlie basic cognitive and linguistic processes. However, opposition theory has never been implemented in a computational cognitive-semiotics model. In this paper, we present a simple model of metaphor identifi cation that relies on opposition theory. An algorithm instantiating the model has been tested on a data set of 100 phrases comprising adjectivenoun pairs in which approximately a half represent metaphorical language-use (e.g., dark thoughts) and the rest literal language-use (e.g., dark hair). Th e algorithm achieved 89% accuracy in metaphor identifi cation and illustrates the relevance of opposition theory for modelling metaphor processing.

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Section: The Model and The Algorithmsupporting

confidence: 60%

Opposition theory and computational semiotics

Assaf¹,

Cohen

Danesi

et al. 2015

SSS

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“…The standard selectional preference models take the form of a mapping σ: ( v , r , c ) ↦ a , that maps each selectional tuple ( v , r , c ) to a real number a , representing the degree of preference of a verb v for a class c with respect to role r (Light and Greiff 2002). Keeping track of single relationships, however, is not enough to build a sufficiently rich model of selectional preferences.…”

Section: Introductionmentioning

confidence: 99%

A non-negative tensor factorization model for selectional preference induction

Cruys¹

2010

Nat. Lang. Eng.

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Link to this article: http://journals.cambridge.org/abstract_S1351324910000148How to cite this article: TIM VAN DE CRUYS (2010). A non-negative tensor factorization model for selectional preference induction. Natural Language Engineering, 16, pp 417-437 AbstractThe distributional similarity methods have proven to be a valuable tool for the induction of semantic similarity. Until now, most algorithms use two-way co-occurrence data to compute the meaning of words. Co-occurrence frequencies, however, need not be pairwise. One can easily imagine situations where it is desirable to investigate co-occurrence frequencies of three modes and beyond. This paper will investigate tensor factorization methods to build a model of three-way co-occurrences. The approach is applied to the problem of selectional preference induction, and automatically evaluated in a pseudo-disambiguation task. The results show that tensor factorization, and non-negative tensor factorization in particular, is a promising tool for Natural Language Processing (nlp).

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“…Vision researchers have used KL to track surprise in a visual scene: the places on the screen where new events most violate the viewer's previous assumptions; KL then accurately captures attention attractors in visual search tasks (Demberg & Keller, 2008;Itti & Baldi, 2009). More generally, KL has seen wide use in the cognitive sciences; Resnik (1993), for example, proposed the KL divergence as a measure of selectional preferences in language (reviewed in Light & Greiff (2002)). It has found use in many successful models of linguistic discrimination, including syntactic comprehension (Hale, 2001;Levy, 2008), speech recognition (Calamaro & Jarosz, 2015;Martin et al, 2013) and word sense disambiguation (Resnik, 1997).…”

Section: Cognitive Surprise and Kullback-leibler Divergencementioning

confidence: 99%

Exploration and exploitation of Victorian science in Darwin’s reading notebooks

2017

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Search in an environment with an uncertain distribution of resources involves a trade-off between exploitation of past discoveries and further exploration. This extends to information foraging, where a knowledge-seeker shifts between reading in depth and studying new domains. To study this decisionmaking process, we examine the reading choices made by one of the most celebrated scientists of the modern era: Charles Darwin. From the full-text of books listed in his chronologically-organized reading journals, we generate topic models to quantify his local (text-to-text) and global (text-to-past) reading decisions using Kullback-Liebler Divergence, a cognitively-validated, information-theoretic measure of relative surprise. Rather than a pattern of surprise-minimization, corresponding to a pure exploitation strategy, Darwin's behavior shifts from early exploitation to later exploration, seeking unusually high levels of cognitive surprise relative to previous eras. These shifts, detected by an unsupervised Bayesian model, correlate with major intellectual epochs of his career as identified both by qualitative scholarship and Darwin's own self-commentary. Our methods allow us to compare his * To whom correspondence should be addressed.Email addresses: jammurdo@indiana.edu (Jaimie Murdock), colallen@indiana.edu (Colin Allen), simon@santafe.edu consumption of texts with their publication order. We find Darwin's consumption more exploratory than the culture's production, suggesting that underneath gradual societal changes are the explorations of individual synthesis and discovery. Our quantitative methods advance the study of cognitive search through a framework for testing interactions between individual and collective behavior and between short-and long-term consumption choices. This novel application of topic modeling to characterize individual reading complements widespread studies of collective scientific behavior.

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Statistical models for the induction and use of selectional preferences

Cited by 21 publications

References 12 publications

Opposition theory and computational semiotics

Opposition theory and computational semiotics

A non-negative tensor factorization model for selectional preference induction

Exploration and exploitation of Victorian science in Darwin’s reading notebooks

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