Information Planning for Text Data
This work addresses the challenge of costly training data acquisition in text analysis, but it is incremental as it applies existing planning methods to text data.
The paper tackled the problem of accelerating learning with fewer training examples for text data by applying information planning based on entropy and mutual information, showing that it outperforms random selection baselines.
Information planning enables faster learning with fewer training examples. It is particularly applicable when training examples are costly to obtain. This work examines the advantages of information planning for text data by focusing on three supervised models: Naive Bayes, supervised LDA and deep neural networks. We show that planning based on entropy and mutual information outperforms random selection baseline and therefore accelerates learning.