Asymmetry in corpus-derived and human word associations

Michelbacher, Lukas; Evert, Stefan; Schütze, Hinrich

doi:10.1515/cllt.2011.012

Cited by 42 publications

(24 citation statements)

References 9 publications

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“…However, there is so far hardly any work which explored its use as a measure of collocational strength. Two exceptions are Michelbacher et al (2007Michelbacher et al ( , 2011. Michelbacher et al (2007) compute conditional probabilities based on adjective/noun collocates in a window of 10 words around node words in the BNC and correlate them with the University of South Florida Association Norms.…”

Section: Association Measures To Quantify Collocation Strength and Thmentioning

confidence: 99%

“…Second, the computational effort that goes into the computation of the rank measures is huge: since the computation of a directional association score of even a single word pair can require the computations of tens or hundreds of thousands of, say, G 2 or t-tests, which seems less than optimal given that, in the quantitative analysis of Michelbacher et al (2011), conditional probabilities did just as well as G 2 .…”

Section: Association Measures To Quantify Collocation Strength and Thmentioning

confidence: 99%

“…Third, Michelbacher et al (2011) is a very laudable study in how they try to combine corpus-linguistic data and psycholinguistic evidence. However, one cannot help but notice that the corpus-based statistics they use do not (necessarily) correspond to anything cognitive or psycholinguistic: to the best of my knowledge, there are, for instance, no cognitive, psychological, or psycholinguistic theories that involve something like ranks of G 2 -values.…”

Section: Association Measures To Quantify Collocation Strength and Thmentioning

confidence: 99%

“…However, up until even the most recent overview and testing studies of collocation/association measures (e.g. Pecina 2009), the issue of directionality has not received the attention that one of the most important notions in our field deserves; Michelbacher et al (2007Michelbacher et al ( , 2011 seem to be the only dedicated studies. The remainder of this section is devoted to the potential and implications of a stronger emphasis on directional collocations.…”

Section: A Directional Association Measure: Advantages and Further Apmentioning

confidence: 99%

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50-something years of work on collocations

Gries¹

2013

IJCL

213

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This paper explores ways in which research into collocation should be improved. After a discussion of the parameters underlying the notion of 'collocation' , the paper has three main parts. First, I argue that corpus linguistics would benefit from taking more seriously the understudied fact that collocations are not necessarily symmetric, as most association measures imply. Also, I introduce an association measure from the associative learning literature that can identify asymmetric collocations and show that it can also distinguish collocations with high and low association strengths well. Second, I summarize some advantages of this measure and brainstorm about ways in which it can help re-examine previous studies as well as support further applications. Finally, I adopt a broader perspective and discuss a variety of ways in which all association measures -directional or not -in corpus linguistics should be improved in order for us to obtain better and more reliable results.

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Section: Association Measures To Quantify Collocation Strength and Thmentioning

confidence: 99%

Section: Association Measures To Quantify Collocation Strength and Thmentioning

confidence: 99%

Section: Association Measures To Quantify Collocation Strength and Thmentioning

confidence: 99%

Section: A Directional Association Measure: Advantages and Further Apmentioning

confidence: 99%

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50-something years of work on collocations

Gries¹

2013

IJCL

213

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“…Likewise, the connections between the emergence of similarity in the QP model and other models which directly include asymmetric similarity metrics (e.g. Jones et al (2011) andMichelbacher (2011)) must await discussion elsewhere. Both of these issues will be taken up in Yearsley et al (in preparation).…”

Section: Asymmetrymentioning

confidence: 99%

Towards a Quantum Probability Theory of Similarity Judgments

Yearsley

Pothos

Hampton

et al. 2015

Quantum Interaction

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This is the accepted version of the paper.This version of the publication may differ from the final published version. Abstract. We review recent progress in understanding similarity judgments in cognition by means of quantum probability theory (QP) models. We begin by outlining some features of similarity judgments that have proven difficult to model by traditional approaches. We then briefly present a model of similarity judgments based on QP, and show how it can solve many of the problems faced by traditional approaches. Finally we look at some areas where the quantum model is currently less satisfactory, and discuss some open questions and areas for further work. Permanent

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Analyzing Co-occurrence Data

Gries

Durrant

2020

A Practical Handbook of Corpus Linguistics

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In this chapter, we provide an overview of quantitative as well as qualitative approaches to cooccurrence data. We begin with a brief terminological overview of different types of cooccurrence that are prominent in corpus-linguistic studies and then discuss the computation of some widely-used measures of association used to quantify co-occurrence. We present two representative case studies, one exploring lexical collocation and learner proficiency, the other creative uses of verbs in/with argument structure constructions. In addition, we highlight how most widely-used measures actually all fall out from viewing corpus-linguistic association as an instance of regression modeling and discuss newer developments and potential improvements of association measure research.

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Asymmetry in corpus-derived and human word associations

Cited by 42 publications

References 9 publications

50-something years of work on collocations

50-something years of work on collocations

Towards a Quantum Probability Theory of Similarity Judgments

Analyzing Co-occurrence Data

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