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Related Experiment Video

Updated: Feb 19, 2026

Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment

Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment

Published on: May 7, 2019

Aggregating and Predicting Sequence Labels from Crowd Annotations.

An T Nguyen¹, Byron C Wallace², Junyi Jessy Li³

¹University of Texas at Austin.

Proceedings of the Conference. Association for Computational Linguistics. Meeting

|November 3, 2017

Summary

This summary is machine-generated.

This study introduces new methods for handling noisy sequence labels in Natural Language Processing (NLP) from multiple annotators. The research improves consensus annotation and sequence prediction for tasks like Named-Entity Recognition.

Related Experiment Videos

Last Updated: Feb 19, 2026

Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment

Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment

Published on: May 7, 2019

Area of Science:

Natural Language Processing (NLP)
Machine Learning
Data Annotation

Background:

Handling noisy sequence labels from multiple annotators is a significant challenge in NLP.
Existing research has limited focus on aggregating crowd-sourced sequential data.

Purpose of the Study:

To develop methods for aggregating noisy sequential crowd labels into a consensus annotation.
To utilize crowd annotations for training models to predict sequences in unannotated text.

Main Methods:

Proposed a novel Hidden Markov Model variant for aggregating sequential crowd labels.
Developed a neural approach using Long Short-Term Memory (LSTM) for sequence prediction.
Evaluated methods on Named-Entity Recognition (NER) in news articles and Information Extraction (IE) from biomedical abstracts.

Main Results:

Achieved improved performance over strong baselines in both aggregation and prediction tasks.
Demonstrated the effectiveness of the proposed Hidden Markov Model and LSTM approaches.
Showcased successful application in diverse text genres and NLP tasks.

Conclusions:

The proposed methods effectively address the challenge of noisy sequential labels in NLP.
The study provides valuable contributions to crowd-sourced data aggregation and sequence modeling.
Source code and data are publicly available for further research and application.