Probability Topic Model Research Articles

An event based topic learning pipeline for neuroimaging literature mining

Neuroimaging text mining extracts knowledge from neuroimaging texts and has received widespread attention. Topic learning is an important research focus of neuroimaging text mining. However, current neuroimaging topic learning researches mainly used traditional probability topic models to extract topics from literature and cannot obtain high-quality neuroimaging topics. The existing topic learning methods also cannot meet the requirements of topic learning oriented to full-text neuroimaging literature. In this paper, three types of neuroimaging research topic events are defined to describe the process and result of neuroimaging researches. An event based topic learning pipeline, called neuroimaging Event-BTM, is proposed to realize topic learning from full-text neuroimaging literature. The experimental results on the PLoS One data set show that the accuracy and completeness of the proposed method are significantly better than the existing main topic learning methods.

Modeling landscape sustainability in the oil producing Niger delta area of Nigeria

This paper models landscape sustainability in the oil-producing Niger Delta area of Nigeria. Simon decision making cycle (intelligence-design-choice) was used to examine landscape sustainability planning issues. This approach involves three levels of application namely intelligence, design, and choice. Probability topic model was used in R software to identify the key problems in the Niger Delta area as (1) oil spillage impacts on water/land use (landscape capital) and (2) militancy (vandalism) and leadership (planning). These problems significantly, affect the landscape capital in the Niger Delta area. System dynamic simulation was applied to evaluate landscape capital under different scenarios of vandalism and different levels of revenue allocation. The study found that the greatest landscape capital can be achieved in 18.25 years if the revenue allocation to the Niger Delta oil producing states is increased from the current level of 13 percent to 21 percent. These results are consistent with UNEP's report that highlights oil spillages in the Niger Delta area as affecting landscape sustainability in the area.

Scene Semantic Recognition Based on Probability Topic Model

In recent years, scene semantic recognition has become the most exciting and fastest growing research topic. Lots of scene semantic analysis methods thus have been proposed for better scene content interpretation. By using latent Dirichlet allocation (LDA) to deduce the effective topic features, the accuracy of image semantic recognition has been significantly improved. Besides, the method of extracting deep features by layer-by-layer iterative computation using convolutional neural networks (CNNs) has achieved great success in image recognition. The paper proposes a method called DF-LDA, which is a hybrid supervised–unsupervised method combined CNNs with LDA to extract image topics. This method uses CNNs to explore visual features that are more suitable for scene images, and group the features of salient semantics into visual topics through topic models. In contrast to the LDA as a tool for simply extracting image semantics, our approach achieves better performance on three datasets that contain various scene categories.

A New Fine-grain SMS Corpus and Its Corresponding Classifier Using Probabilistic Topic Model

Nowadays, SMS spam has been overflowing in many countries. In fact, the standards of filtering SMS spam are different from country to country. However, the current technologies and researches about SMS spam filtering all focus on dividing SMS message into two classes: legitimate and illegitimate. It does not conform to the actual situation and need. Furthermore, they are facing several difficulties, such as: (1) High quality and large-scale SMS spam corpus is very scarce, fine categorized SMS spam corpus is even none at all. This seriously handicaps the researchers’ studies. (2) The limited length of SMS messages lead to lack of enough features. These factors seriously degrade the performance of the traditional classifiers (such as SVM, K-NN, and Bayes). In this paper, we present a new fine categorized SMS spam corpus which is unique and the largest one as far as we know. In addition, we propose a classifier, which is based on the probability topic model. The classifier can alleviate feature sparse problem in the task of SMS spam filtering. Moreover, we compare the approach with three typical classifiers on the new SMS spam corpus. The experimental results show that the proposed approach is more effective for the task of SMS spam filtering.

Microbial flora structure based on probability topic model

The heterogeneity of microbial flora structures plays an important role in the health and disease of the host. With respect to the temporal and spatial heterogeneity of the flora structure, both unsupervised and supervised learning algorithms have been developed. Because of the similarity of the characteristics of the flora data and the text data, in this paper, we investigate the temporal heterogeneity of the flora structure by applying the latent Dirichlet allocation (LDA) probability topic model for unsupervised learning. We then use system and K -Means clustering to compare these two methods. Two kinds of data sources of Beipingding monkey vaginal flora (MVB) and minimal hepatic encephalopathy (MHE) bacteria heterogeneity operational taxonomic unit (OTUs) data sets are analyzed by the Monte Carlo LDA model with the folding Gibbs sampling. We used the LDA model to divide the 27 samples and 77 sample OTUs in the MVB and MHE data sources, respectively, into six topics and four topics, which differ from the number of clusters (5, 3, and 4, 3) divided by system and K -Means clustering. In addition, experimental results show that the classification similarity of sample diversity, pH value with the physiological data-pH in MVB samples and the similarity of α value in MHE, the classification similarity of the pH and the α is consistent with the classification characteristics of LDA model. As such, the LDA model classifies the OTUs data sets more accurately with respect to the degree of aggregation of the samples. More importantly, the LDA model can also identify representative OTUs in each topic. Compared with the system clustering and K -Means clustering methods, the LDA model can not only quantify the heterogeneity of the flora structure more effectively, but also identify the corresponding heterogeneity of the OTUs.

Detecting the association of health problems in consumer-level medical text

Consumers usually do not know the complicated links between related health problems. This fact may cause troubles when they wish to seek complete information regarding such problems. This study detects the associations among health problems by extending the meaning of health terms with methods based on the latent Dirichlet allocation (LDA) probability topic model, the Medical Subject Headings (MeSH) thesaurus structure and the Wikipedia concept mapping. The terms represented health problems are selected from and extended by the consumer-level medical text. The vocabulary is different between the consumer-level and the professional-level medical text. Thus, the findings can be easily understood by the general public and be suitable to consumer-oriented applications. The methods were evaluated in two ways: (1) correlation analysis with expert rating to show the overall performance and (2) P@ N to reflect the ability of detecting strong associations. The LDA topic-model-based method outperforms the other two types. The judgment incongruence between the best method and the expert ratings has been examined, and the evidence shows that the automatic method sometimes detects real associations beyond those identified by human experts.

Sparse Multi-Modal Topical Coding for Image Annotation

Image annotation plays a significant role in large scale image understanding, indexing and retrieval. The Probability Topic Models (PTMs) attempt to address this issue by learning latent representations of input samples, and have been shown to be effective by existing studies. Though useful, PTM has some limitations in interpreting the latent representations of images and texts, which if addressed would broaden its applicability. In this paper, we introduce sparsity to PTM to improve the interpretability of the inferred latent representations. Extending the Sparse Topical Coding that originally designed for unimodal documents learning, we propose a non-probabilistic formulation of PTM for automatic image annotation, namely Sparse Multi-Modal Topical Coding. Beyond controlling the sparsity, our model can capture more compact correlations between words and image regions. Empirical results on some benchmark datasets show that our model achieves better performance on automatic image annotation and text-based image retrieval over the baseline models.

A Message Topic Model for Multi-Grain SMS Spam Filtering

At present, content-based methods are regard as the more effective in the task of Short Message Service (SMS) spam filtering. However, they usually use traditional text classification technologies, which are more suitable to deal with normal long texts; therefore, it often faces some serious challenges, such as the sparse data problem and noise data in the SMS message. In addition, the existing SMS spam filtering methods usually consider the SMS spam task as a binary-class problem, which could not provide for different categories for multi-grain SMS spam filtering. In this paper, the authors propose a message topic model (MTM) for multi-grain SMS spam filtering. The MTM derives from the famous probability topic model, and is improved in this paper to make it more suitable for SMS spam filtering. Finally, the authors compare the MTM with the SVM and the standard LDA on the public SMS spam corpus. The experimental results show that the MTM is more effective for the task of SMS spam filtering.