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21
A Crowdsourced Corpus of Multiple Judgments and Disagreement on Anaphoric Interpretation
Poesio, Massimo; Chamberlain, Jon; Paun, Silviu. - : Association for Computational Linguistics, 2019
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22
Comparing Bayesian Models of Annotation
Paun, Silviu; Carpenter, Bob; Hovy, Dirk. - : Association for Computational Linguistics, 2018
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23
A Probabilistic Annotation Model for Crowdsourcing Coreference
Kruschwitz, Udo; Chamberlain, Jon; Yu, Juntao. - : Association for Computational Linguistics, 2018
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24
Comparing Bayesian Models of Annotation
Paun, S.; Carpenter, B.; Chamberlain, J. D.. - : Association for Computational Linguistics, 2018
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25
Phrase Detectives Corpus
Chamberlain, Jon; Poesio, Massimo; Kruschwitz, Udo. - : Linguistic Data Consortium, 2017. : https://www.ldc.upenn.edu, 2017
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26
Phrase Detectives Corpus ...
Chamberlain, Jon; Poesio, Massimo; Kruschwitz, Udo. - : Linguistic Data Consortium, 2017
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27
Anaphora resolution : algorithms, resources, and applications
Poesio, Massimo [Herausgeber]; Stuckardt, Roland [Herausgeber]; Versley, Yannick [Herausgeber]. - 2016
DNB Subject Category Language
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28
Anaphora Resolution : Algorithms, Resources, and Applications
Poesio, Massimo [Herausgeber]; Stuckardt, Roland [Herausgeber]; Versley, Yannick [Herausgeber]. - Berlin, Heidelberg : Springer Berlin Heidelberg, 2016
DNB Subject Category Language
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29
Anaphora resolution : algorithms, resources, and applications
Poesio, Massimo (Herausgeber); Stuckardt, Roland (Herausgeber); Versley, Yannick (Herausgeber). - Heidelberg : Springer, 2016
UB Frankfurt Linguistik
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30
Grammar Is a System That Characterizes Talk in Interaction
Poesio, Massimo; Ginzburg, Jonathan. - : Frontiers Media SA, 2016
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31
The SENSEI Project: Making Sense of Human Conversations
Riccardi, Giuseppe; Bechet, Frederic; Danieli, Morena. - : Springer International Publishing, 2016
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32
Pandora’s Box Opened
Rieser, Hannes; Poesio, Massimo. - : Walter de Gruyter GmbH, 2016
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33
MultiLing 2015: Multilingual Summarization of Single and Multi-Documents, On-line Fora, and Call-center Conversations
In: Sigdial ; https://hal-amu.archives-ouvertes.fr/hal-01194230 ; Sigdial, 2015, Unknown, Unknown Region (2015)
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34
Reading visually embodied meaning from the brain: Visually grounded computational models decode visual-object mental imagery induced by written text
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35
Combining Minimally-supervised Methods for Arabic Named Entity Recognition
Althobaiti, M.; Kruschwitz, Udo; Poesio, Massimo. - : Association for Computational Linguistics, 2015
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36
The effect of linguistic and visual salience in visual world studies
Cavicchio, Federica; Melcher, David; Poesio, Massimo. - : Frontiers Media SA, 2014
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37
Identifying fake Amazon reviews as learning from crowds
Fornaciari, Tommaso; Poesio, Massimo. - : Association for Computational Linguistics, 2014
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38
Using Brain Data for Sentiment Analysis.
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39
Discriminating Taxonomic Categories and Domains in Mental Simulations of Concepts of Varying Concreteness
Anderson, Andrew J; Murphy, Brian; Poesio, Massimo. - : MIT Press - Journals, 2014
Abstract: Abstract Most studies of conceptual knowledge in the brain focus on a narrow range of concrete conceptual categories, rely on the researchers' intuitions about which object belongs to these categories, and assume a broadly taxonomic organization of knowledge. In this fMRI study, we focus on concepts with a variety of concreteness levels; we use a state of the art lexical resource (WordNet 3.1) as the source for a relatively large number of category distinctions and compare a taxonomic style of organization with a domain-based model (an example domain is Law). Participants mentally simulated situations associated with concepts when cued by text stimuli. Using multivariate pattern analysis, we find evidence that all Taxonomic categories and Domains can be distinguished from fMRI data and also observe a clear concreteness effect: Tools and Locations can be reliably predicted for unseen participants, but less concrete categories (e.g., Attributes, Communications, Events, Social Roles) can only be reliably discriminated within participants. A second concreteness effect relates to the interaction of Domain and Taxonomic category membership: Domain (e.g., relation to Law vs. Music) can be better predicted for less concrete categories. We repeated the analysis within anatomical regions, observing discrimination between all/most categories in the left mid occipital and left mid temporal gyri, and more specialized discrimination for concrete categories Tool and Location in the left precentral and fusiform gyri, respectively. Highly concrete/abstract Taxonomic categories and Domain were segregated in frontal regions. We conclude that both Taxonomic and Domain class distinctions are relevant for interpreting neural structuring of concrete and abstract concepts.
Keyword: P Philology. Linguistics; QA75 Electronic computers. Computer science
URL: http://repository.essex.ac.uk/11996/
http://repository.essex.ac.uk/11996/1/jocn_a_00508.pdf
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40
The effect of linguistic and visual salience in visual world studies
Cavicchio, Federica; Melcher, David; Poesio, Massimo. - : Frontiers Media S.A., 2014
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