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65 results for “question answering”
ChatGPT - Questions and Answers to the Political Coordinates Test in English, French, Italian and German
<p>This dataset contains the questions and answers resulting from the administration of the policy coordination test in English, French, Italian and German to ChatGPT. The version used was GPT-4.</p>
Semantic Parameter Matching in Web APIs with Transformer-based Question Answering
<p>This repository contains the evaluation results of our study, as well as datasets and model checkpoints. <br> For a detailed overview regarding the provided materials, please refer to README.md.</p>
On the Helpfulness of Answering Developer Questions on Discord with Similar Conversations and Posts from the Past
<p>Replication Package for "On the Helpfulness of Answering Developer Questions on Discord with Similar Conversations and Posts from the Past".</p>
openEHR survey questions and answers
<p>openEHR survey administered to openEHR CDR vendors/developers in 2023</p>
The PROSECCA Study, Answering New Questions in Prostate Cancer
ClinicalTrials.gov study NCT06714630. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Multifaceted Online Interventions to Increase Clinicians Searching of Current Best Evidence to Answer Clinical Questions
ClinicalTrials.gov study NCT02038439. IPD Sharing: Not stated. Countries: 1. Publications: 2.
Data from: Too much of a good thing? Finding the most informative genetic dataset to answer conservation questions
Open the record for dataset details and reuse information.
Questions and Answers of a Survey Related to the Use of Dublin Core to Register Metadata Generated in Software Engineering Experiments.
<p>Questions were presented and answers were collected during the application of the survey.</p>
Developers' Information Seeking in Question & Answer Websites through a Gender Lens
Open the record for dataset details and reuse information.
SE-PQA: a Resource for Personalized Community Question Answering
<p>Personalization in Information Retrieval is a topic studied for a long time. Nevertheless, there is still a lack of high-quality, real-world datasets to conduct large-scale experiments and evaluate models for personalized search. This paper contributes to fill this gap by introducing SE-PQA (StackExchange - Personalized Question Answering), a new resource to design and evaluate personalized models related to the two tasks of community Question Answering (cQA). The contributed dataset includes more than 1 million queries and 2 million answers, annotated with a rich set of features modeling the social interactions among the users of a popular cQA platform. We describe the characteristics of SE-PQA and detail the features associated with both questions and answers. We also provide reproducible baseline methods for the cQA task based on the resource, including deep learning models and personalization approaches. The results of the preliminary experiments conducted show the appropriateness of SE-PQA to train effective cQA models; they also show that personalization improves remarkably the effectiveness of all the methods tested. Furthermore, we show the benefits in terms of robustness and generalization of combining data from multiple communities for personalization purposes.</p> <p> </p> <p>Performance on all communities separately:</p> <table> <tbody><tr> <th>Community</th> <th>Model (BM25 +)</th> <th>P@1</th> <th>NDCG@3</th> <th>NDCG@10</th> <th>R@100</th> <th>MAP@100</th> <th>$\lambda$</th> </tr> </tbody><tbody> <tr> <td>Academia</td> <td>MiniLM</td> <td>0.438</td> <td>0.382</td> <td>0.395</td> <td>0.489</td> <td>0.344</td> <td>(.1,.9)</td> </tr> <tr> <td> </td> <td>MiniLM + TAG</td> <td>0.453</td> <td>0.392</td> <td>0.403</td> <td>0.489</td> <td>0.352</td> <td>(.1,.8,.1)</td> </tr> <tr> <td>Anime</td> <td>MiniLM + TAG</td> <td>0.650</td> <td>0.682</td> <td>0.714</td> <td>0.856</td> <td>0.683</td> <td>(.1,.9,.0)</td> </tr> <tr> <td>Apple</td> <td>MiniLM</td> <td>0.327</td> <td>0.351</td> <td>0.381</td> <td>0.514</td> <td>0.349</td> <td>(.1,.9)</td> </tr> <tr> <td> </td> <td>MiniLM + TAG</td> <td>0.335</td> <td>0.361</td> <td>0.389</td> <td>0.514</td> <td>0.357</td> <td>(.1,.8,.1)</td> </tr> <tr> <td>Bicycles</td> <td>MiniLM</td> <td>0.405</td> <td>0.380</td> <td>0.421</td> <td>0.600</td> <td>0.365</td> <td>(.1,.9)</td> </tr> <tr> <td> </td> <td>MiniLM + TAG</td> <td>0.436</td> <td>0.405</td> <td>0.441</td> <td>0.600</td> <td>0.386</td> <td>(.1,.8,.1)</td> </tr> <tr> <td>Boardgames</td> <td>MiniLM</td> <td>0.681</td> <td>0.694</td> <td>0.728</td> <td>0.866</td> <td>0.692</td> <td>(.1,.9)</td> </tr> <tr> <td> </td> <td>MiniLM + TAG</td> <td>0.696</td> <td>0.702</td> <td>0.736</td> <td>0.866</td> <td>0.699</td> <td>(.1,.8,.1)</td> </tr> <tr> <td>Buddhism</td> <td>MiniLM + TAG</td> <td>0.490</td> <td>0.387</td> <td>0.397</td> <td>0.544</td> <td>0.334</td> <td>(.3,.7,.0)</td> </tr> <tr> <td>Christianity</td> <td>MiniLM</td> <td>0.534</td> <td>0.505</td> <td>0.555</td> <td>0.783</td> <td>0.497</td> <td>(.2,.8)</td> </tr> <tr> <td> </td> <td>MiniLM + TAG</td> <td>0.549</td> <td>0.521</td> <td>0.564</td> <td>0.783</td> <td>0.507</td> <td>(.1,.8,.1)</td> </tr> <tr> <td>Cooking</td> <td>MiniLM</td> <td>0.600</td> <td>0.567</td> <td>0.600</td> <td>0.719</td> <td>0.553</td> <td>(.1,.9)</td> </tr> <tr> <td> </td> <td>MiniLM + TAG</td> <td>0.619</td> <td>0.583</td> <td>0.614</td> <td>0.719</td> <td>0.568</td> <td>(.1,.8,.1)</td> </tr> <tr> <td>DIY</td> <td>MiniLM</td> <td>0.323</td> <td>0.313</td> <td>0.346</td> <td>0.501</td> <td>0.302</td> <td>(.1,.9)</td> </tr> <tr> <td> </td> <td>MiniLM + TAG</td> <td>0.335</td> <td>0.324</td> <td>0.356</td> <td>0.501</td> <td>0.312</td> <td>(.1,.8,.1)</td> </tr> <tr> <td>Expatriates</td> <td>MiniLM + TAG</td> <td>0.596</td> <td>0.653</td> <td>0.682</td> <td>0.832</td> <td>0.645</td> <td>(.1,.9,.0)</td> </tr> <tr> <td>Fitness</td> <td>MiniLM + TAG</td> <td>0.568</td> <td>0.575</td> <td>0.613</td> <td>0.760</td> <td>0.567</td> <td>(.2,.8,.0)</td> </tr> <tr> <td>Freelancing</td> <td>MiniLM + TAG</td> <td>0.513</td> <td>0.472</td> <td>0.506</td> <td>0.654</td> <td>0.457</td> <td>(.1,.9,.0)</td> </tr> <tr> <td>Gaming</td> <td>MiniLM</td> <td>0.510</td> <td>0.534</td> <td>0.562</td> <td>0.686</td> <td>0.532</td> <td>(.1,.9)</td> </tr> <tr> <td> </td> <td>MiniLM + TAG</td> <td>0.519</td> <td>0.547</td> <td>0.571</td> <td>0.686</td> <td>0.541</td> <td>(.1,.8,.1)</td> </tr> <tr> <td>Gardening</td> <td>MiniLM</td> <td>0.344</td> <td>0.362</td> <td>0.396</td> <td>0.520</td> <td>0.359</td> <td>(.1,.9)</td> </tr> <tr> <td> </td> <td>MiniLM + TAG</td> <td>0.345</td> <td>0.369</td> <td>0.399</td> <td>0.520</td> <td>0.363</td> <td>(.1,.8,.1)</td> </tr> <tr> <td>Genealogy</td> <td>MiniLM + TAG</td> <td>0.592</td> <td>0.605</td> <td>0.631</td> <td>0.779</td> <td>0.594</td> <td>(.3,.7,.0)</td> </tr> <tr> <td>Health</td> <td>MiniLM + TAG</td> <td>0.718</td> <td>0.765</td> <td>0.797</td> <td>0.934</td> <td>0.765</td> <td>(.2,.8,.0)</td> </tr> <tr> <td>Gaming</td> <td>MiniLM</td> <td>0.510</td> <td>0.534</td> <td>0.562</td> <td>0.686</td> <td>0.532</td> <td>(.1,.9)</td> </tr> <tr> <td> </td> <td>MiniLM + TAG</td> <td>0.519</td> <td>0.547</td> <td>0.571</td> <td>0.686</td> <td>0.541</td> <td>(.1,.8,.1)</td> </tr> <tr> <td>Hermeneutics</td> <td>MiniLM</td> <td>0.589</td> <td>0.538</td> <td>0.593</td> <td>0.828</td> <td>0.526</td> <td>(.2,.8)</td> </tr> <tr> <td> </td> <td>MiniLM + TAG</td> <td>0.632</td> <td>0.570</td> <td>0.617</td> <td>0.828</td> <td>0.552</td> <td>(.1,.8,.1)</td> </tr> <tr> <td>Hinduism</td> <td>MiniLM</td> <td>0.388</td> <td>0.415</td> <td>0.459</td> <td>0.686</td> <td>0.416</td> <td>(.2,.8)</td> </tr> <tr> <td> </td> <td>MiniLM + TAG</td> <td>0.382</td> <td>0.410</td> <td>0.457</td> <td>0.686</td> <td>0.412</td> <td>(.1,.8,.1)</td> </tr> <tr> <td>History</td> <td>MiniLM + TAG</td> <td>0.740</td> <td>0.735</td> <td>0.764</td> <td>0.862</td> <td>0.730</td> <td>(.2,.8,.0)</td> </tr> <tr> <td>Hsm</td> <td>MiniLM + TAG</td> <td>0.666</td> <td>0.707</td> <td>0.737</td> <td>0.870</td> <td>0.690</td> <td>(.2,.8,.0)</td> </tr> <tr> <td>Interpersonal</td> <td>MiniLM + TAG</td> <td>0.663</td> <td>0.617</td> <td>0.653</td> <td>0.739</td> <td>0.604</td> <td>(.2,.8,.0)</td> </tr> <tr> <td>Islam</td> <td>MiniLM</td> <td>0.382</td> <td>0.412</td> <td>0.453</td> <td>0.642</td> <td>0.410</td> <td>(.1,.9)</td> </tr> <tr> <td> </td> <td>MiniLM + TAG</td> <td>0.395</td> <td>0.427</td> <td>0.464</td> <td>0.642</td> <td>0.421</td> <td>(.1,.8,.1)</td> </tr> <tr> <td>Judaism</td> <td>MiniLM + TAG</td> <td>0.363</td> <td>0.387</td> <td>0.432</td> <td>0.649</td> <td>0.388</td> <td>(.2,.8,.0)</td> </tr> <tr> <td>Law</td> <td>MiniLM</td> <td>0.663</td> <td>0.647</td> <td>0.678</td> <td>0.803</td> <td>0.639</td> <td>(.2,.8)</td> </tr> <tr> <td> </td> <td>MiniLM + TAG</td> <td>0.677</td> <td>0.657</td> <td>0.687</td> <td>0.803</td> <td>0.649</td> <td>(.1,.8,.1)</td> </tr> <tr> <td>Lifehacks</td> <td>MiniLM</td> <td>0.714</td> <td>0.601</td> <td>0.617</td> <td>0.703</td> <td>0.553</td> <td>(.1,.9)</td> </tr> <tr> <td> </td> <td>MiniLM + TAG</td> <td>0.714</td> <td>0.621</td> <td>0.631</td> <td>0.703</td> <td>0.568</td> <td>(.1,.8,.1)</td> </tr> <tr> <td>Linguistics</td> <td>MiniLM + TAG</td> <td>0.584</td> <td>0.588</td> <td>0.630</td> <td>0.794</td> <td>0.587</td> <td>(.2,.8,.0)</td> </tr> <tr> <td>Literature</td> <td>MiniLM + TAG</td> <td>0.871</td> <td>0.878</td> <td>0.889</td> <td>0.934</td> <td>0.876</td> <td>(.3,.7,.0)</td> </tr> <tr> <td>Martialarts</td> <td>MiniLM</td> <td>0.630</td> <td>0.599</td> <td>0.645</td> <td>0.796</td> <td>0.596</td> <td>(.1,.9)</td> </tr> <tr> <td> </td> <td>MiniLM + TAG</td> <td>0.640</td> <td>0.628</td> <td>0.660</td> <td>0.796</td> <td>0.612</td> <td>(.1,.8,.1)</td> </tr> <tr> <td>Money</td> <td>MiniLM</td> <td>0.545</td> <td>0.535</td> <td>0.563</td> <td>0.706</td> <td>0.515</td> <td>(.2,.8)</td> </tr> <tr> <td> </td> <td>MiniLM + TAG</td> <td>0.559</td> <td>0.542</td> <td>0.571</td> <td>0.706</td> <td>0.523</td> <td>(.1,.8,.1)</td> </tr> <tr> <td>Movies</td> <td>MiniLM</td> <td>0.713</td> <td>0.722</td> <td>0.753</td> <td>0.865</td> <td>0.724</td> <td>(.1,.9)</td> </tr> <tr> <td> </td> <td>MiniLM + TAG</td> <td>0.728</td> <td>0.735</td> <td>0.762</td> <td>0.865</td> <td>0.735</td> <td>(.1,.8,.1)</td> </tr> <tr> <td>Music</td> <td>MiniLM</td> <td>0.508</td> <td>0.447</td> <td>0.476</td> <td>0.602</td> <td>0.418</td> <td>(.2,.8)</td> </tr> <tr> <td> </td> <td>MiniLM + TAG</td> <td>0.522</td> <td>0.460</td> <td>0.486</td> <td>0.602</td> <td>0.427</td> <td>(.1,.8,.1)</td> </tr> <tr> <td>Musicfans</td> <td>MiniLM + TAG</td> <td>0.531</td> <td>0.531</td> <td>0.560</td> <td>0.693</td> <td>0.539</td> <td>(.1,.9,.0)</td> </tr> <tr> <td>Opensource</td> <td>MiniLM</td> <td>0.574</td> <td>0.593</td> <td>0.621</td> <td>0.771</td> <td>0.581</td> <td>(.2,.8)</td> </tr> <tr> <td> </td> <td>MiniLM + TAG</td> <td>0.577</td> <td>0.598</td> <td>0.622</td> <td>0.771</td> <td>0.581</td> <td>(.1,.8,.1)</td> </tr> <tr> <td>Outdoors</td> <td>MiniLM + TAG</td> <td>0.681</td> <td>0.643</td> <td>0.675</td> <td>0.819</td> <td>0.629</td> <td>(.1,.9,.0)</td> </tr> <tr> <td>Parenting</td> <td>MiniLM + TAG</td> <td>0.485</td> <td>0.430</td> <td>0.452</td> <td>0.602</td> <td>0.399</td> <td>(.1,.9,.0)</td> </tr> <tr> <td>Pets</td> <td>MiniLM</td> <td>0.509</td> <td>0.531</td> <td>0.565</td> <td>0.685</td> <td>0.523</td> <td>(.1,.9)</td> </tr> <tr> <td> </td> <td>MiniLM + TAG</td> <td>0.519</td> <td>0.549</td> <td>0.581</td> <td>0.685</td> <td>0.541</td> <td>(.1,.8,.1)</td> </tr> <tr> <td>Philosophy</td> <td>MiniLM + TAG</td> <td>0.568</td> <td>0.514</td> <td>0.546</td> <td>0.707</td> <td>0.491</td> <td>(.2,.8,.0)</td> </tr> <tr> <td>Politics</td> <td>MiniLM + TAG</td> <td>0.659</td> <td>0.630</td> <td>0.659</td> <td>0.814</td> <td>0.608</td> <td>(.1,.9,.0)</td> </tr> <tr> <td>Rpg</td> <td>MiniLM</td> <td>0.657</td> <td>0.646</td> <td>0.685</td> <td>0.849</td> <td>0.640</td> <td>(.2,.8)</td> </tr> <tr> <td> </td> <td>MiniLM + TAG</td> <td>0.677</td> <td>0.660</td> <td>0.695</td> <td>0.849</td> <td>0.651</td> <td>(.1,.8,.1)</td> </tr> <tr> <td>Scifi</td> <td>MiniLM</td> <td>0.532</td> <td>0.563</td> <td>0.596</td> <td>0.745</td> <td>0.559</td> <td>(.2,.8)</td> </tr> <tr> <td> </td> <td>MiniLM + TAG</td> <td>0.549</td> <td>0.574</td> <td>0.606</td> <td>0.745</td> <td>0.569</td> <td>(.1,.8,.1)</td> </tr> <tr> <td>Skeptics</td> <td>MiniLM + TAG</td> <td>0.862</td> <td>0.869</td> <td>0.887</td> <td>0.969</td> <td>0.867</td> <td>(.2,.8,.0)</td> </tr> <tr> <td>Sound</td> <td>MiniLM</td> <td>0.377</td> <td>0.410</td> <td>0.451</td> <td>0.626</td> <td>0.405</td> <td>(.2,.8)</td> </tr> <tr> <td> </td> <td>MiniLM + TAG</td> <td>0.380</td> <td>0.423</td> <td>0.454</td> <td>0.626</td> <td>0.413</td> <td>(.1,.8,.1)</td> </tr> <tr> <td>Sports</td> <td>MiniLM</td> <td>0.673</td> <td>0.721</td> <td>0.756</td> <td>0.902</td> <td>0.724</td> <td>(.2,.8)</td> </tr> <tr> <td> </td> <td>MiniLM + TAG</td> <td>0.692</td> <td>0.743</td> <td>0.775</td> <td>0.902</td> <td>0.740</td> <td>(.1,.8,.1)</td> </tr> <tr> <td>Sustainability</td> <td>MiniLM</td> <td>0.657</td> <td>0.677</td> <td>0.735</td> <td>0.895</td> <td>0.675</td> <td>(.1,.9)</td> </tr> <tr> <td> </td> <td>MiniLM + TAG</td> <td>694</td> <td>0.716</td> <td>0.763</td> <td>0.895</td> <td>0.706</td> <td>(.1,.8,.1)</td> </tr> <tr> <td>Travel</td> <td>MiniLM + TAG</td> <td>0.546</td> <td>0.547</td> <td>0.576</td> <td>0.700</td> <td>0.530</td> <td>(.1,.9,.0)</td> </tr> <tr> <td>Vegetarianism</td> <td>MiniLM + TAG</td> <td>0.623</td> <td>0.626</td> <td>0.678</td> <td>0.869</td> <td>0.641</td> <td>(.3,.7,.0)</td> </tr> <tr> <td>Woodworking</td> <td>MiniLM + TAG</td> <td>0.656</td> <td>0.654</td> <td>0.692</td> <td>0.847</td> <td>0.645</td> <td>(.2,.8,.0)</td> </tr> <tr> <td>Workplace</td> <td>MiniLM + TAG</td> <td>0.574</td> <td>0.444</td> <td>0.429</td> <td>0.495</td> <td>0.359</td> <td>(.3,.7,.0)</td> </tr> <tr> <td>Writers</td> <td>MiniLM + TAG</td> <td>0.561</td> <td>0.490</td> <td>0.516</td> <td>0.644</td> <td>0.466</td> <td>(.2,.8,.0)</td> </tr> <tr> <td>Average</td> <td>MiniLM</td> <td>0.519</td> <td>0.506</td> <td>0.536</td> <td>0.677</td> <td>0.492</td> <td>-</td> </tr> <tr> <td> </td> <td>MiniLM + TAG</td> <td>0.530</td> <td>0.515</td> <td>0.544</td> <td>0.677</td> <td>0.500</td> <td>-</td> </tr> </tbody> </table>
raw data - Old Questions, New Answers: Real-World, Long-Term Efficacy of Hymenoptera Venom Immunotherapy: Prevalence of Venom-Induced Anaphylaxis, Risk Factors, Field Sting Reactions
Open the record for dataset details and reuse information.
Evaluating Large Language Models' Proficiency in Answering Arabic GAT Exam Questions
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What Edits Are Done on The Highly Answered Questions in Stack Overflow? An Empirical Study
<p>This contains the data set and the code we take advantage of to process data and make analyzation.</p>
SUTD-TrafficQA: A Question Answering Benchmark and an Efficient Network for Video Reasoning Over Traffic Events
<p><strong>SUTD-TrafficQA</strong> is a dataset that takes the form of <strong>Video</strong> <strong>QA</strong> based on 10,080 in-the-wild videos and annotated 62,535 QA pairs, for benchmarking the cognitive capability of causal inference and event understanding models in complex traffic scenarios. Specifically, the dataset proposes 6 challenging reasoning tasks corresponding to various traffic scenarios, so as to evaluate the reasoning capability over different kinds of complex yet practical traffic events.</p>
Supplementary material 1 from: Ramírez-Chaves HE, Cardona-Giraldo A, Ossa-López PA, Arias Monsalve HF, Rivera-Páez FA, Morales-Martínez DM (2023) Confirming the presence of Lasiurus frantzii (Peters, 1870) (Chiroptera, Vespertilionidae) in South America: more questions than answers. ZooKeys 1180: 145-157. https://doi.org/10.3897/zookeys.1180.105497
Voucher number and Genbank accession codes of the sequences of Lasiurus used in this study
Data from: When to reproduce? A new answer to an old question
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Analysis of the questions for a question and answer session as part of Co-design Activity 3
<p>One table was used to analyze the questions to be available on the app for a question and answer session between a health professional and a Deaf patient.</p>
DQA: An Open-Domain Dataset of Deep Questions Answering
<p>DQA, the first open-domain question answering dataset consisting of comprehensive questions with verbose answers. The dataset comprises of 4 question types which are classified as Level 1 questions according to Bloom’s Taxonomy i.e understanding type Q/A. </p>
Answering Questions About Vitamin D Supplementation and Sun Exposure in Patients Who Have Undergone Surgery for Stage IB, Stage II, or Stage IIIA Melanoma
ClinicalTrials.gov study NCT00672321. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Increasing Medical and Nursing Students Searching of Current Best Evidence to Answer Clinical Questions
ClinicalTrials.gov study NCT02240095. IPD Sharing: Not stated. Countries: 1. Publications: 0.
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
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.