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7 results for “animacy”
Animacy and non-animacy denoting nouns
<p>Universität Zürich<br> Institut für Computerlinguistik<br> Manfred Klenner</p> <p>Die Daten sind im Rahmen eines Projekts, das vom Schweizer Nationalfond gefördert wurde (Nr. 105215-179302), entstanden.<br> ------------------------------------------------------------------------------------------------------------------------<br> **License**: Creative Commons Attribution-ShareAlike 4.0 International Public License</p> <p><br> Repository: animacy data for animcay classification</p> <p>This is the training data for an animacy classifier (see References LREC)</p> <p>1) gold_actor: 7468 nouns denoting animate entities<br> 2) gold_nonactor 5511 nouns denoting non-animate entities</p> <p>subsets of 1:</p> <p>gold_direct 6897 nouns directly denoting animate entities<br> gold_metonym 587 metonymy trigger nouns</p> <p><br> Format: just lists</p> <p>Note: although some person names are in the data, a separate NER for person names should be used .</p> <p>References:</p> <p>@inproceedings{LREC,<br> month = {Juni},<br> author = {Manfred Klenner and Anne G{\"o}hring},<br> booktitle = {Proceedings of the Language Resources and Evaluation Conference},<br> address = {Marseille, France},<br> title = {Animacy Denoting {G}erman Nouns: Annotation and Classification},<br> publisher = {European Language Resources Association},<br> pages = {1360--1364},<br> year = {2022},<br> language = {english},<br> url = {https://doi.org/10.5167/uzh-219148},<br> abstract = {In this paper, we introduce a gold standard for animacy detection comprising almost 14,500 German nouns that might be used to denote either animate entities or non-animate entities. W<br> e present inter-annotator agreement of our crowd-sourced seed annotations (9,000 nouns) and discuss the results of machine learning models applied to this data.}<br> }<br> </p>
animacy data for animcay classification
<p>This is the training data for an animacy classifier (see References LREC)</p> <p>1) gold_actor: 7468 nouns denoting animate entities<br> 2) gold_nonactor 5511 nouns denoting non-animate entities</p> <p>subsets of 1:</p> <p>gold_direct 6897 nouns directly denoting animate entities<br> gold_metonym 587 metonymy trigger nouns</p> <p>gold_female 3738 nouns denoting female actors<br> gold_male 2830 nouns denoting male actors<br> gold_nogender 329 nouns denoting female or male actors (often plural)</p> <p><br> Format: just lists</p> <p>Note: although some person names are in the data, a separate NER for person names should be used .</p> <p>References:</p> <p>@inproceedings{LREC,<br> month = {Juni},<br> author = {Manfred Klenner and Anne G{\"o}hring},<br> booktitle = {Proceedings of the Language Resources and Evaluation Conference},<br> address = {Marseille, France},<br> title = {Animacy Denoting {G}erman Nouns: Annotation and Classification},<br> publisher = {European Language Resources Association},<br> pages = {1360--1364},<br> year = {2022},<br> language = {english},<br> url = {https://doi.org/10.5167/uzh-219148},<br> abstract = {In this paper, we introduce a gold standard for animacy detection comprising almost 14,500 German nouns that might be used to denote either animate entities or non-animate entities. We present inter-annotator agreement of our crowd-sourced seed annotations (9,000 nouns) and discuss the results of machine learning models applied to this data.}<br> }<br> </p>
Perceiving animacy from shape
<p>Dataset relative to the following publication:</p> <p>Schmidt, F., Hegele, M., & Fleming, R. W. (2017). Perceiving animacy from shape. <em>Journal of Vision, 17,</em> 10. <a href="http://dx.doi.org/10.1167/17.11.10">http://dx.doi.org/10.1167/17.11.10</a></p> <p>Each folder contains the data relative to one experiment and a text file with comments.</p>
Recoded data for "Animacy-based nominal classification in northwestern Bantu and beyond"
<p>In order to make our northwestern Bantu dataset (Di Garbo and Verkerk 2022; Verkerk and Di Garbo 2022) fully comparable with Tom's (Güldemann 2023) Central African dataset, we recoded those languages of our sample that display animacy-based agreement as well as those which have completely lost gender. The latter was done in order to check whether, even in the absence of gender, any coding of animacy still affects aspects of nominal morphosyntax such as number marking. The study was essentially run by Lyne Rahabi, student assistant at Aix-Marseille University, in July 2024. Annemarie and Francesca took responsibility for the recoding of the genderless languages as well as the analysis of the newly coded data.</p>
Data from: Functionally analogous body- and animacy-responsive areas in the dog (Canis familiaris) and human occipito-temporal lobe
<p>Comparing the neural correlates of socio-cognitive skills across species provides insights into the evolution of the social brain and has revealed face- and body-sensitive regions in the primate temporal lobe. Although from a different lineage, dogs share convergent visuo-cognitive skills with humans and a temporal lobe which evolved independently in carnivores. We investigated the neural correlates of face and body perception in dogs (<em>N </em>= 15) and humans (<em>N</em> = 40) using functional MRI. Combining univariate and multivariate analysis approaches, we found functionally analogous occipito-temporal regions involved in the perception of animate entities and bodies in both species and face-sensitive regions in humans. Though unpredicted, we also observed neural representations of faces compared to inanimate objects, and dog compared to human bodies in dog olfactory regions. These findings shed light on the evolutionary foundations of human and dog social cognition and the predominant role of the temporal lobe.</p> <p>This data set contains:</p> <ul> <li>raw functional neuroimaging data of <em>N</em> = 15 dogs</li> <li>structural scans of the same dogs incl. brain masks & skull-stripped versions</li> <li>files containing the condition names, onsets and durations for each dog and task run</li> <li>files containing the motion regressors incl. motion scrubbing for each dog and task run</li> </ul> <p>Data of the comparative human neuroimaging sample will be made available by the first author upon reasonable request.</p> <p>Please also visit our Open Science Framework project site for detailed sample descriptives and group-level imaging data of both species (<a href="https://osf.io/kzcs2/">https://osf.io/kzcs2/</a>).</p>
Spikiness and animacy as potential organising principles of human ventral visual cortex
<p>Considerable research has been devoted to understanding the fundamental organising principles of the ventral visual pathway. A recent study revealed a series of 3–4 topographical maps arranged along the macaque inferotemporal (IT) cortex. The maps articulated a two-dimensional space based on the spikiness and animacy of visual objects, with 'inanimate-spiky' and 'inanimate-stubby' regions of the maps constituting two previously unidentified cortical networks. The goal of our study was to determine whether a similar functional organisation might exist in human IT. To address this question, we presented the same object stimuli and images from 'classic' object categories (bodies, faces, houses) to humans while recording fMRI activity at 7 Tesla. Contrasts designed to reveal the spikiness-animacy object space evoked extensive significant activation across human IT. However, unlike the macaque, we did not observe a clear sequence of complete maps, and selectivity for the spikiness-animacy space was deeply and mutually entangled with category-selectivity. Instead, we observed multiple new stimulus preferences in category-selective regions, including functional sub-structure related to object spikiness in scene-selective cortex. Taken together, these findings highlight spikiness as a promising organising principle of human IT and provide new insights into the role of category-selective regions in visual object processing.</p>
Spikiness and animacy as potential organising principles of human ventral visual cortex
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