amazon-mechanical-turk

There are 13 repositories under amazon-mechanical-turk topic.

  • hltcoe/turkle

    Django-based clone of Amazon's Mechanical Turk service running in your local environment.

    Language:Python1481712946

  • jaxony/turktool

    Modern React app for bounding box annotation on mturk

    Language:JavaScript563316

  • phillipi/AMT_Real_vs_Fake

    Code for running real vs fake experiments on Amazon Mechanical Turk

    Language:HTML444210

  • Data-on-the-Mind/2017-summer-workshop

    Exercises, data, and more for our 2017 summer workshop (funded by the Estes Fund and in partnership with Project Jupyter and Berkeley's D-Lab)

    Language:HTML3412019

  • raffienficiaud/django_mturk_minimalistic

    A minimalistic Django app for Amazon Mechanical Turk External Question

    Language:Python11300

  • stefantaubert/tts-mos-test-mturk

    Command-line interface (CLI) and Python library to evaluate text-to-speech (TTS) mean opinion score (MOS) studies done on Amazon Mechanical Turk (MTurk). The calculation of the confidence intervals is done in the same manner as described in (Ribeiro et al., 2011).

    Language:Python6200

  • budang/turkey-lite

    A JavaScript tool for collecting implicit behavioral data on Amazon Mechanical Turk, etc

    Language:JavaScript2202

  • jacobis/paraphrased-opinion-spam

    Paraphrased OPinion Spam (POPS) Corpus v1.0

    2300

  • ehsan-ashik/mturk-api-helpers-console-app

    The .NET app includes a curated list of helper functions with ability to perform advanced Requester tasks programmatically on Amazon Mechanical Turk crowdsourcing platform.

    Language:C#1100

  • anuparna/VQACrowdSourcing

    A crowd-sourcing system for Visual Question Answering

    Language:HTML0100

  • data-intelligence-for-health-lab/CrowdSourcing-for-Digital-Public-Health-Surveillance

    To explore and evaluate the application of crowdsourcing, in general, and AMT, in specific, for developing digital public health surveillance systems, we collected 296,166 crowd-generated labels for 98,722 tweets, labelled by 610 AMT workers, to develop machine learning (ML) models for detecting behaviours related to physical activity, sedentary behaviour, and sleep quality (PASS) among Twitter users. To infer the ground truth labels and explore the quality of these labels, we studied four statistical consensus methods that are agnostic of task features and only focus on worker labelling behaviour. Moreover, to model the meta-information associated with each labelling task and leverage the potentials of context-sensitive data in the truth inference process, we developed seven ML models, including traditional classifiers (offline and active), a deep-learning-based classification model, and a hybrid convolutional neural network (CNN) model.

    Language:Jupyter Notebook0200

  • sigmachirality/Characterize-It

    Analyzing the appearances of comic book characters with Python and Amazon MTurk.

    Language:Jupyter Notebook0400

  • gkthiruvathukal/mturk-imgupload-boto-python

    Python + Boto scripts used to create and process Amazon Mechanical Turk HITs for a recent paper, "Examining the use of Amazon’s Mechanical Turk for edge extraction of the occlusal surface of fossilized bovid teeth"

    Language:Python10