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Dataset results
26 results for “dialogism”
Broad-Coverage German Sentiment Classification Model and Dataset for Dialog Systems
<p><a href="http://www.lrec-conf.org/proceedings/lrec2020/pdf/2020.lrec-1.202.pdf"><strong>Training a Broad-Coverage German Sentiment Classification Model for Dialog Systems</strong></a></p> <p>This paper describes the training of a general-purpose German sentiment classification model. Sentiment classification is an important aspect of general text analytics. Furthermore, it plays a vital role in dialogue systems and voice interfaces that depend on the ability of the system to pick up and understand emotional signals from user utterances. The presented study outlines how we have collected a new German sentiment corpus and then combined this corpus with existing resources to train a broad-coverage German sentiment model. The resulting data set contains 5.4 million labelled samples. We have used the data to train both, a simple convolutional and a transformer-based classification model and compared the results achieved on various training configurations. The model and the data set will be published along with this paper.</p> <p>You can find the code for training testing the models, that was published along with the paper in this <a href="https://github.com/oliverguhr/german-sentiment">repository</a>.</p> <p>The <a href="https://github.com/oliverguhr/german-sentiment-lib"><em>germansentiment</em></a> Python package contains a easy to use interface for the model that was published with this paper.</p> <p> </p> <p> </p>
Deceptive and Abusive Online Dialogs
<p>This corpus consists of annotated deceptive and abusive online dialogues that have been produced by the Chattack system. The Chattack system is a gamified crowd-sourcing platform for tagging deceptive and abusive online behaviour.</p>
Dialogic Density in the Books of William James and John Dewey
<p>This study employs a machine learning algorithm (the Stanford Named Entity Recognizer, or NER) to shed light on the relative rates at which William James and John Dewey mention other persons in their respective books. The NER attempts to tag words and phrases in a corpus with either PERSON, ORGANIZATION, or LOCATION. I created databases for every monograph by James and Dewey. Each database contains all entities tagged with PERSON in the relevant book. </p>
Dialogic Density in the Books of William James and John Dewey
<p>This study employs a machine learning algorithm (the Stanford Named Entity Recognizer, or NER) to shed light on the relative rates at which William James and John Dewey mention other persons in their respective books. The NER attempts to tag words and phrases in a corpus with either PERSON, ORGANIZATION, or LOCATION. I created a corpus of major books published by James and Dewey, respectively, and used the NER to analyze each corpus. I then created databases and collected all entities tagged with PERSON in each book in each corpus. </p>
BRAIN Journal-Redesigning a Flexible Material Master Data Application with Language Dependency-Figure 7. Dialog structure of the View Cluster
<p>Field dependency has been generated automatically and as a result the following desired structure has been achieved (figure 7). After populating the database tables with data in different languages with the help of the view cluster, the popup has been adapted in order to support the translation of the tabs and areas. Supplementary internal tables have been defined in the function module POPUP_FLEX, in order to copy data from the translation tables. The corresponding SELECT statements have been embedded in TRY-CATCH blocks, in order to prevent short dumps due to faulty selection processes.</p>
Anaphoric Phenomena in Situated dialog: A First Round of Annotations
<p>A first release of 500 documents from the multimodal corpus Tell-me-more (Ilinykh et al., 2019) annotated with coreference information according to the ARRAU guidelines (Poesio et al., 2021).</p>
Patient Computer Dialog in Primary Care
ClinicalTrials.gov study NCT00386776. IPD Sharing: Not stated. Countries: 1. Publications: 80.
Task dialog by native-Danish talkers in Danish and English in both quiet and noise
<p>The zip files contain recorded conversations between 19 pairs of normal-hearing native-Danish talkers taking part in an experiment in the lab of the Hearing Systems Group at The Technical University of Denmark during October-November 2016.</p> <p>Each participant pair conversed in each of four conditions:<br> - In their first language (Danish) without background noise (L1-quiet)<br> - In their first language (Danish) with background noise (L1-noise)<br> - In their second language (English) without background noise (L2-quiet)<br> - In their second language (English) with background noise (L2-noise)</p> <p>The conversations were recorded in three blocks with a break between each block. Each block consisted of a conversation from each of the four conditions (in a random order). In each conversation, the pairs found 10 differences between two almost identical pictures (DiapixUK; Baker and Hazan, 2011). The pictures were randomized across conditions. The background noise was a mixed-gender 6-talker babble-like vocoded noise (ICRA 7, Dreschler et al. (2001)).</p> <p>The pairs were seated in separate sound booths and could not see each other. </p> <p>Each talker wore a Shure WH20 microphone (close mic) along with a pair of Sennheiser HD650 headphones. An operator sat outside the door monitoring the conversations and had the opportunity to talk to the participants through an operator microphone. In the headphones, both talkers heard a mix of themselves, their conversational partner, and the background noise (in the noise conditions). The gains were set and held constant such that the speech level presented by the headphones was the same as the level in the soundbooth 1 m away from the talker.</p> <p>In total four channels were recorded using an RME Fireface 802 soundcard at 24 bit, 48 kHz, with Matlab 2016a: <br> Channel 1: the microphone of Talker 1<br> Channel 2: the microphone of Talker 2<br> Channel 3: the operator microphone<br> Channel 4: The mix sent to the headphones</p> <p>Prior to recording, all 20 pairs provided written consent for the recordings to be used in the conversation experiment. After the recordings had been completed, we recognized that these recordings might be useful for others. Thus we approached the participants and asked if they would be willing to make the recordings publicly available. 19 out of the 20 pairs gave written consent to make the recordings publicly available. However, pair 12 asked that one of their conversations not be made publicly available (the third replicate of the conversation in Danish in quiet). Thus, this conversation and the conversations from the pair that did not provide written consent to make the recordings public (pair 9) are not included here. </p> <p>The file "Recordings_4channel_48kHz_24bit.zip" contains the original recordings. When uncompressed, the wav files are approximately 42 GB in total.</p> <p>The file "Recordings_2channel_22050Hz_16bit.zip" contains only channels 1 and 2 (the microphones from both talkers) from the recordings, which have been downsampled to 22050 Hz and rescaled as 16 bit wav files. When uncompressed, the wav files are approximately 6.4 GB in total.</p> <p>In both zip files, there are individual wav files for each conversation and the file names are structured as follows: TalkerPair_Condition_Replicate.wav.</p> <p>For any questions, please contact <br> Anna Josefine Sørensen: ajs@elektro.dtu.dk</p> <p>or</p> <p>Ewen MacDonald: emcd@elektro.dtu.dk</p> <p>References:<br> R. Baker and V. Hazan. DiapixUK: task materials for the elicitation of multiple spontaneous speech dialogs. Behavior Research Methods, 43(3):761–770, 2011. ISSN 1554-3528. doi: 10.3758/s13428-011-0075-y.</p> <p><br> W. Dreschler, H. Verschuure, C. Ludvigsen, and S. Westermann. ICRA Noises: Artificial Noise Signals with Speech-like Spectral and Temporal Properties for Hearing Instrument Assessment: Ruidos ICRA: Señates de ruido artificial con espectro similar al habla y propiedades temporales para pruebas de instrumentos auditiv. International Journal of Audiology, 40(3):148–157, 2001. ISSN 1499-2027. doi: 10.3109/00206090109073110.</p>
Dialogic Reading-Based Nutrition Education on Preschool Children
ClinicalTrials.gov study NCT07170696. IPD Sharing: NO. Countries: 1. Publications: 1.
Data from: How the online social networks are used: dialogs-based structure of MySpace
Quantitative study of collective dynamics in online social networks is a new challenge based on the abundance of empirical data. Conclusions, however, may depend on factors such as user's psychology profiles and their reasons to use the online contacts. In this study, we have compiled and analysed two datasets from MySpace. The data contain networked dialogues occurring within a specified time depth, high temporal resolution and texts of messages, in which the emotion valence is assessed by using the SentiStrength classifier. Performing a comprehensive analysis, we obtain three groups of results: dynamic topology of the dialogues-based networks have a characteristic structure with Zipf's distribution of communities, low link reciprocity and disassortative correlations. Overlaps supporting 'weak-ties' hypothesis are found to follow the laws recently conjectured for online games. Long-range temporal correlations and persistent fluctuations occur in the time series of messages carrying positive (negative) emotion; patterns of user communications have dominant positive emotion (attractiveness) and strong impact of circadian cycles and interactivity times longer than 1 day. Taken together, these results give a new insight into the functioning of online social networks and unveil the importance of the amount of information and emotion that is communicated along the social links. All data used in this study are fully anonymized.
TECHNOLOGY OF TEACHING FOREIGN LANGUAGE DIALOGIC SPEECH
Open the record for dataset details and reuse information.
Dialogical interview with Professor Vargas Deysi.
<pre>Dialogical interview with Professor Vargas Deysi. </pre>
Feasibility of a Mobile Application to Support Reflection and Dialog About Strengths in People With Chronic Illness
ClinicalTrials.gov study NCT03437863. IPD Sharing: NO. Countries: 1. Publications: 0.
Dialogical Family Guidance for Neurodevelopmental Disorders
ClinicalTrials.gov study NCT04892992. IPD Sharing: NO. Countries: 0. Publications: 1.
Data from: How the online social networks are used: dialogs-based structure of MySpace
Open the record for dataset details and reuse information.
The IL32/BAFF axis supports prosurvival dialog in the lymphoma ecosystem and is disrupted by NIK inhibition
GEO Series GSE179636. Homo sapiens. 18 samples. Type: Expression profiling by high throughput sequencing.
Rx for Success: RCT of an App for Dialogic Reading Training
ClinicalTrials.gov study NCT03828721. IPD Sharing: NO. Countries: 1. Publications: 0.
Dialogical and Narrative Processes in Couple Therapy for Depression
ClinicalTrials.gov study NCT00883207. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Improved Quality of the Treatment and Increased Compliance in Asthmatics Through the Dialog Tool Soren - Between Patient and Caregiver
ClinicalTrials.gov study NCT00272727. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Comparison of Cognitive Processing Therapy (CPT) and Dialogical Exposure Therapy (DET) for Posttraumatic Stress Disorder
ClinicalTrials.gov study NCT01693497. IPD Sharing: Not stated. Countries: 1. Publications: 0.
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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)
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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.