Active Learning with Constrained Topic Model

Yi Yang; Shimei Pan; Doug Downey; Kunpeng Zhang

Active Learning with Constrained Topic Model

Yi Yang, Shimei Pan, Doug Downey, Kunpeng Zhang

Computer Science

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract

Latent Dirichlet Allocation (LDA) is a topic modeling tool that automatically discovers topics from a large collection of documents. It is one of the most popular text analysis tools currently in use. In practice however, the topics discovered by LDA do not always make sense to end users. In this extended abstract, we propose an active learning framework that interactively and iteratively acquires user feedback to improve the quality of learned topics. We conduct experiments to demonstrate its effectiveness with simulated user input on a benchmark dataset.

Original language	English
Title of host publication	Active Learning with Constrained Topic Model
State	Published - 2014
Event	Workshop on Interactive Language Learning, Visualization, and Interfaces, ACL - Baltimore, MD Duration: Jun 1 2014 → …

Conference

Conference	Workshop on Interactive Language Learning, Visualization, and Interfaces, ACL
Period	6/1/14 → …

Cite this

@inproceedings{1df7cdc863634bfc86e00f6a4f6bccb7,

title = "Active Learning with Constrained Topic Model",

abstract = "Latent Dirichlet Allocation (LDA) is a topic modeling tool that automatically discovers topics from a large collection of documents. It is one of the most popular text analysis tools currently in use. In practice however, the topics discovered by LDA do not always make sense to end users. In this extended abstract, we propose an active learning framework that interactively and iteratively acquires user feedback to improve the quality of learned topics. We conduct experiments to demonstrate its effectiveness with simulated user input on a benchmark dataset.",

author = "Yi Yang and Shimei Pan and Doug Downey and Kunpeng Zhang",

year = "2014",

language = "English",

booktitle = "Active Learning with Constrained Topic Model",

note = "Workshop on Interactive Language Learning, Visualization, and Interfaces, ACL ; Conference date: 01-06-2014",

}

TY - GEN

T1 - Active Learning with Constrained Topic Model

AU - Yang, Yi

AU - Pan, Shimei

AU - Downey, Doug

AU - Zhang, Kunpeng

PY - 2014

Y1 - 2014

N2 - Latent Dirichlet Allocation (LDA) is a topic modeling tool that automatically discovers topics from a large collection of documents. It is one of the most popular text analysis tools currently in use. In practice however, the topics discovered by LDA do not always make sense to end users. In this extended abstract, we propose an active learning framework that interactively and iteratively acquires user feedback to improve the quality of learned topics. We conduct experiments to demonstrate its effectiveness with simulated user input on a benchmark dataset.

AB - Latent Dirichlet Allocation (LDA) is a topic modeling tool that automatically discovers topics from a large collection of documents. It is one of the most popular text analysis tools currently in use. In practice however, the topics discovered by LDA do not always make sense to end users. In this extended abstract, we propose an active learning framework that interactively and iteratively acquires user feedback to improve the quality of learned topics. We conduct experiments to demonstrate its effectiveness with simulated user input on a benchmark dataset.

M3 - Conference contribution

BT - Active Learning with Constrained Topic Model

T2 - Workshop on Interactive Language Learning, Visualization, and Interfaces, ACL

Y2 - 1 June 2014

ER -

Active Learning with Constrained Topic Model

Abstract

Conference

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Cite this