Modeling Eye Movements when Reading Microblogs
Authors: Maria Barrett, Anders Soegaard
AAAI 2015 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | I will explore a data driven method based on experimental eye tracking data with the prospect to train a machine learning classifier to distinguish between the eye movements of an interested and an uninterested reader. |
| Researcher Affiliation | Academia | Maria Barrett and Anders Søgaard Centre for Language Technology University of Copenhagen Njalsgade 140 DK-2300 Copenhagen S Denmark |
| Pseudocode | No | The paper is a project description and does not contain any pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any statement or link regarding the release of open-source code for the described methodology. |
| Open Datasets | No | The paper mentions collecting 'experimental eye tracking data' and 'annotating tweets which has been done by my research group', but does not provide concrete access information or refer to an established public dataset. |
| Dataset Splits | No | The paper is a Ph.D. project proposal outlining future work; it does not contain specific details about training, validation, or test dataset splits. |
| Hardware Specification | No | The paper mentions 'a cheap consumer eye tracker' and 'The Eye Tribe s $99 eye tracker' as a data collection device, but it does not specify hardware used for running experiments or model training (e.g., GPU/CPU models, memory). |
| Software Dependencies | No | The paper mentions 'Part-of-speech (POS) tags from the Universal POS tagset' but does not list specific software dependencies with version numbers (e.g., Python 3.x, PyTorch 1.x). |
| Experiment Setup | No | The paper is a Ph.D. project proposal outlining future work and does not describe specific experimental setup details, such as hyperparameters or training configurations. |