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16 results for “semantic representation”
Shared neural codes for visual and semantic information about familiar faces in a common representational space
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Figure 11. Comparison of MultiNet and MST representations-Representing Mental Spaces and Dynamics of Natural Language Semantics
<p>In the sentence (7) there are two clauses describing two hypothetical situations. These two<br> situations are connected by the relation COND in MultiNet representation (Figure 11).</p>
Figure 10. Comparison of MultiNet and MST representations.-Representing Mental Spaces and Dynamics of Natural Language Semantics
<p>Helbig [18, p. 507] defines the expression (s MCONT c) as “a specification of the<br> informational or mental content c of a mental or informational process s… By default, the second<br> argument c is assumed to be a hypothetical object or situation.”<br> MCONT relation properties are roughly equivalent to those of subject-verb complexes<br> (SVSBs) of MST. MCONT relation can generally be treated as capturing the idea behind what<br> philosophers call propositional attitudes in representational theories of mind [12] or opaque<br> contexts [10] in semantics. For example, consider the sentence in (3) rewritten below as (6) and<br> semantic representation of which is given in Figure 10.</p>
Figure 8. Comparison of MultiNet and MST representations-Representing Mental Spaces and Dynamics of Natural Language Semantics
<p>Figure 8 shows the representation of the<br> first reading of the sentence (5) in MWR. The MultiNet is shown at top of the figure, and the corresponding mental space representation is shown at the bottom side. Blue circles mark<br> hypothetical or nonreal objects and situations and red circles mark real objects and situations.<br> Facticity value for John is real, for unicorn it is nonreal, and for the process of riding, marked with<br> blue broken circle, it is non-real as well.</p>
Figure 9. Comparison of MultiNet and MST representations-Representing Mental Spaces and Dynamics of Natural Language Semantics
<p>Second interpretation of the sentence (5) can be represented with changing John’s Facticity<br> attribute-value from [FACT = real] to [FACT = nonreal] making the whole situation and its<br> elements non-real (Figure 9).</p>
Figure 1. Mental space representation of "In the play, Mary is excited"-Representing Mental Spaces and Dynamics of Natural Language Semantics
<p>Thus an entity can have a variable reality status depending on the mental<br> space to which it belongs. The mental space constructed by the sentence Poirot is a Belgian<br> detective is a non-real imaginary story space of which Poirot is an element. But when we say in<br> reality, Poirot is not Belgian the constructed space is reality space in which Poirot (the actor, not<br> the character) does not have the fictional nationality.</p>
Figure 2. Semantic representation of the sentence Peter finished the discussion in MultiNet after Helbig [8, p. 447].-Representing Mental Spaces and Dynamics of Natural Language Semantics
<p>In Figure 3, semantic frame of the concept Finish realized in the form of the verb finish<br> requires two C-roles: An agent represented by the relation AGT, and an affected entity represented<br> by the relation AFF. Here agent is Peter and the affected entity is an abstract object ([SORT = ad]<br> means the concept is a dynamic abstraction).</p>
Learning Unsupervised Knowledge-Enhanced Representations to Reduce the Semantic Gap in Information Retrieval (Evaluation datasets)
<p>This dataset contains all the runs, pools, plots and analyses to reproduce the results presented in the paper: "Learning Unsupervised Knowledge-Enhanced Representations to Reduce the Semantic Gap in Information Retrieval ", 2020.</p>
ANR REaDY-SPOK-Semantic Representations
<p>The study investigated changes in preexisting representations stored in semantic memory after a dialogue. Researchers can find the pictures used in the set of three experiments, the data and R-scripts of the experiments, an example of confederate's script, and a new picture database. This research was supported by the French National Research Agency (ANR-19-CE28–0006).</p>
DeConf: De-conflated Semantic Representations
<p>Sense representations generated using the algorithm introduced in:</p> <ul> <li>M. T. Pilehvar and N. Collier, <a href="http://www.pilevar.com/taher/pubs/Pilehvar_Collier_EMNLP2016.pdf">De-Conflated Semantic Representations</a>. EMNLP 2016, Austin, TX.</li> </ul>
Data from: Intermediate acoustic-to-semantic representations link behavioural and neural responses to natural sounds
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Semantic Representation of the Joconde Database
<p>Semantic Representation of the Joconde Database, based on CIDOC-CRM.</p> <p>Near from 600000 creative works described. More than 11M triples.</p> <p>Source for Joconde Database:</p> <p><a href="https://www.data.gouv.fr/fr/datasets/collections-des-musees-de-france-extrait-de-la-base-joconde/">https://www.data.gouv.fr/fr/datasets/collections-des-musees-de-france-extrait-de-la-base-joconde/</a></p> <p>(json format)</p> <p>More information: <a href="https://github.com/datamusee/semjoconde">https://github.com/datamusee/semjoconde</a></p> <p> </p>
Dataset for paper "Automated Static Warning Identification via Path-based Semantic Representation" submitted to JOS
<p>The project includes the datasets and running example used in the submitted JOS paper titled "Automated Static Warning Identification via Path-based Semantic Representation"</p>
Anonymized data for paper "Cross-Project Defect Identification via Path-Based Semantic Feature Representation" submitted to ICSE 2022
<p>The project includes the dataset and code used in the submitted ICSE 2022 paper titled "# 971 Cross-Project Defect Identification via Path-Based Semantic Feature Representation"</p>
Language Acquisition in the Brain and Algorithms: Towards Systematic Monitoring of the Evolution of Semantic Representations in Biological and Artificial Neural Networks
ClinicalTrials.gov study NCT05217043. IPD Sharing: NO. Countries: 1. Publications: 0.
Dataset used in COALA: Co-Aligned Autoencoders for Learning Semantically Enriched Audio Representations
<p>This dataset consists of two hdf5 files that contain pre-computed log-mel spectrograms that have been used to to train audio embedding models. The dataset is split into a training set and a validation set containing respectively 170793 and 19103 spectrogram patches with their accompanying multi-hot encoded tags from a vocabulary of 1000 tags provided by <a href="https://freesound.org/">Freesound</a> users.</p> <p>More details can be found in "COALA: Co-Aligned Autoencoders for Learning Semantically Enriched Audio Representations" by X. Favory, <a href="https://kdrossos.net">K. Drossos</a>, <a href="https://tutcris.tut.fi/portal/en/persons/tuomas-virtanen(210e58bb-c224-40a9-bf6c-5b786297e841).html">T. Virtanen</a>, and X. Serra. The code is available at this <a href="https://github.com/xavierfav/coala">GitHub repository</a>.</p> <p> </p> <p>License:</p> <p>This dataset is derived from content from the Freesound collection. All sounds are released under Creative Commons (CC) licenses from either <a href="https://creativecommons.org/publicdomain/zero/1.0/">CC0</a>, <a href="https://creativecommons.org/licenses/by/3.0/">CC-BY,</a> <a href="https://creativecommons.org/licenses/sampling+/1.0/">CC-S+</a>, or <a href="https://creativecommons.org/licenses/by-nc/3.0/">CC-BY-NC</a>. We attribute authors of all the sounds used in the dataset and provide their corresponding licenses in the attributions.txt file.</p> <p> </p>
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Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.