Theories of the representation and processing of concepts have been greatly enhanced by models based on information available in semantic property norms. This information relates both to the identity of the features produced in the norms and to their statistical properties. In this article, we introduce a new and large set of property norms that are designed to be a more flexible tool to meet the demands of many different disciplines interested in conceptual knowledge representation, from cognitive psychology to computational linguistics. As well as providing all features listed by 2 or more participants, we also show the considerable linguistic variation that underlies each normalized feature label and the number of participants who generated each variant. Our norms are highly comparable with the largest extant set (McRae, Cree, Seidenberg, & McNorgan, 2005) in terms of the number and distribution of features. In addition, we show how the norms give rise to a coherent category structure. We provide these norms in the hope that the greater detail available in the Centre for Speech, Language and the Brain norms should further promote the development of models of conceptual knowledge. The norms can be downloaded at www.csl.psychol.cam.ac.uk/propertynorms.Electronic supplementary materialThe online version of this article (doi:10.3758/s13428-013-0420-4) contains supplementary material, which is available to authorized users.

This case-control study examines gender differences in authorship of invited commentaries published in medical journals from 2013 to 2017, controlling for field of expertise, seniority, and publication metrics.

We consider the opportunities presented by big educational learner corpora for Second Language Acquisition (SLA). In particular, we focus on theEF Cambridge Open Language Database(EFCAMDAT), an open access database of student writings submitted toEnglishtown, the online school ofEF Education First. EFCAMDAT stands out for its size (33 million words, 85 thousand learners) and a range of 128 writing tasks covering all CEFR levels with data from learners from varying nationalities. We discuss methodological issues arising from analyzing big data resources generated in educational contexts and argue that Natural Language Processing (NLP) is essential for the automated processing of such datasets. As a study case, we follow the developmental trajectory of relative clauses, a construction that necessitates deeper syntactic analysis. We consider specific issues that can affect the developmental trajectory, including task effects, formulaic language and national language effects.

Understanding spoken words involves a rapid mapping from speech to conceptual representations. One distributed feature‐based conceptual account assumes that the statistical characteristics of concepts’ features—the number of concepts they occur in (distinctiveness/sharedness) and likelihood of co‐occurrence (correlational strength)—determine conceptual activation. To test these claims, we investigated the role of distinctiveness/sharedness and correlational strength in speech‐to‐meaning mapping, using a lexical decision task and computational simulations. Responses were faster for concepts with higher sharedness, suggesting that shared features are facilitatory in tasks like lexical decision that require access to them. Correlational strength facilitated responses for slower participants, suggesting a time‐sensitive co‐occurrence‐driven settling mechanism. The computational simulation showed similar effects, with early effects of shared features and later effects of correlational strength. These results support a general‐to‐specific account of conceptual processing, whereby early activation of shared features is followed by the gradual emergence of a specific target representation.

We present a first analysis of interannotator agreement for the DIT ++ tagset of dialogue acts, a comprehensive, layered, multidimensional set of 86 tags. Within a dimension or a layer, subsets of tags are often hierarchically organised. We argue that especially for such highly structured annotation schemes the well-known kappa statistic is not an adequate measure of inter-annotator agreement. Instead, we propose a statistic that takes the structural properties of the tagset into account, and we discuss the application of this statistic in an annotation experiment. The experiment shows promising agreement scores for most dimensions in the tagset and provides useful insights into the usability of the annotation scheme, but also indicates that several additional factors influence annotator agreement. We finally suggest that the proposed approach for measuring agreement per dimension can be a good basis for measuring annotator agreement over the dimensions of a multidimensional annotation scheme.