A Method for Constructing Supervised Topic Model Based on Term Frequency-Inverse Topic Frequency

Abstract

Supervised topic modeling has been successfully applied in the fields of document classification and tag recommendation in recent years. However, most existing models neglect the fact that topic terms have the ability to distinguish topics. In this paper, we propose a term frequency-inverse topic frequency (TF-ITF) method for constructing a supervised topic model, in which the weight of each topic term indicates the ability to distinguish topics. We conduct a series of experiments with not only the symmetric Dirichlet prior parameters but also the asymmetric Dirichlet prior parameters. Experimental results demonstrate that the result of introducing TF-ITF into a supervised topic model outperforms several state-of-the-art supervised topic models.

A Method for Constructing Supervised Time Topic Model Based on Variational Autoencoder

“…Labeled LDA matches the multiple topics to the labels in the document [9]. e number of topics is determined by the metadata of the document (such as labels), and topic terms have a better way to interpret topics [10]. Partially labeled LDA learns latent topic structure within the scope of observed, human interpretable labels [23].…”

“…In order to contain the supervision, L-LDA applies a 1 : 1 correspondence between topics and labels. In addition to labels, keywords of scientific papers and categories of news are also considered as topics [10]. L-LDA is a probabilistic graphical model that describes a process for generating a labeled document collection.…”

“…e number of topics is determined by the metadata of the document (such as labels), and topic terms have a better way to interpret topics [10]. In addition, the supervised topic model supports a variety of applications, such as social event analysis [11], abnormal event detection [12], document classification [13], and tag recommendation [14].…”

A Time-Critical Topic Model for Predicting the Survival Time of Sepsis Patients

“…To simplify the calculation, we use formula (21) to replace the log of the likelihood function of formula (19):…”

“…It is difficult to predict the development of sepsis based on a small number of measurements. Conventional topic models such as latent Dirichlet allocation (LDA) are unsupervised machine learning methods that can recognize latent topic information in massive document collections [20,21]. Lehman et al [22] proposed a novel approach for ICU patient risk stratification using a topic model.…”