Early text classification: a Naïve solution

Abstract

Text classification is a widely studied problem, and it can be considered solved for some domains and under certain circumstances. There are scenarios, however, that have received little or no attention at all, despite its relevance and applicability. One of such scenarios is early text classification, where one needs to know the category of a document by using partial information only. A document is processed as a sequence of terms, and the goal is to devise a method that can make predictions as fast as possible. The importance of this variant of the text classification problem is evident in domains like sexual predator detection, where one wants to identify an offender as early as possible. This paper analyzes the suitability of the standard naïve Bayes classifier for approaching this problem. Specifically, we assess its performance when classifying documents after seeing an increasingly number of terms. A simple modification to the standard naïve Bayes implementation allows us to make predictions with partial information. To the best of our knowledge Naïve Bayes has not been used for this purpose before. Throughout an extensive experimental evaluation we show the effectiveness of the classifier for early text classification. What is more, we show that this simple solution is very competitive when compared with state of the art methodologies that are more elaborated. We foresee our work will pave the way for the development of more effective early text classification techniques based in the naïve Bayes formulation.

Learning When to Classify for Early Text Classification

“…The window size chosen was w = 3, that is, three terms were read between each run of the early text classification framework. Based on Escalante's work [3] we chose a naïve Bayes classifier for the CPI model. The performance for the partial documents can be seen in Fig 2. Clearly, we can classify documents without reading all terms.…”

“…In [3] the authors propose an adaptation of Naïve Bayes to tackle the problem of classification with partial information. Although they achieve similar performance to state of the art models that read the entire document, they do not approach the DMC problem.…”

“…To date, only a few papers have approached this kind of scenarios [2,3,5]. Despite its low popularity, this topic has a major potential in practical applications.…”

A text classification framework for simple and effective early depression detection over social media streams

“…For instance, some works have addressed early text classification by using diverse techniques like modifications of Naive Bayes (Escalante et al, 2016), profile-based representations (Escalante et al, 2017), and Multi-Resolution Concept Representations (López-Monroy et al, 2018). Those approaches have focused on quantifying prediction performance of the classifiers when using partial information in documents, that is, by considering how well they behave when incremental percentages of documents are provided to the classifier.…”

Early Text Classification Using Multi-Resolution Concept Representations

“…The key aspect of the work is a Markov Decision Process (MDP), where each sentence is modeled in a TFIDF vector. More recently, (Escalante et al, 2016) proposed a straightforward solution for early detection scenarios by using the naïve Bayes classifier. The idea consists in training with full documents, but when partial information has to be classified, the maximum a posteriori probability was estimated over the available text.…”