scholarly journals STABILITY OF TOPIC MODELING VIA MODALITY REGULARIZATION

2020 ◽  
Author(s):  
R. Derbanosov ◽  
◽  
M. Bakhanova ◽  
◽  
Keyword(s):  
Topic Modeling ◽  
Latent Dirichlet Allocation ◽  
Semantic Analysis ◽  
Topic Model ◽  
Side Information ◽  
Auxiliary Information ◽  
Discrete Distributions ◽  
Probabilistic Latent Semantic Analysis ◽  
Probabilistic Topic Modeling ◽  
Random Initialization

Probabilistic topic modeling is a tool for statistical text analysis that can give us information about the inner structure of a large corpus of documents. The most popular models—Probabilistic Latent Semantic Analysis and Latent Dirichlet Allocation—produce topics in a form of discrete distributions over the set of all words of the corpus. They build topics using an iterative algorithm that starts from some random initialization and optimizes a loss function. One of the main problems of topic modeling is sensitivity to random initialization that means producing significantly different solutions from different initial points. Several studies showed that side information about documents may improve the overall quality of a topic model. In this paper, we consider the use of additional information in the context of the stability problem. We represent auxiliary information as an additional modality and use BigARTM library in order to perform experiments on several text collections. We show that using side information as an additional modality improves topics stability without significant quality loss of the model.

scholarly journals Multi-Task Topic Analysis Framework for Hallmarks of Cancer with Weak Supervision

2020 ◽  
Vol 10 (3) ◽  
pp. 834
Author(s):  
Erdenebileg Batbaatar ◽  
Van-Huy Pham ◽  
Keun Ho Ryu
Keyword(s):  
Topic Modeling ◽  
Latent Dirichlet Allocation ◽  
Semantic Analysis ◽  
Topic Model ◽  
Label Propagation ◽  
Probabilistic Latent Semantic Analysis ◽  
Hallmarks Of Cancer ◽  
Weak Supervision ◽  
Topic Analysis ◽  
Cancer Data

The hallmarks of cancer represent an essential concept for discovering novel knowledge about cancer and for extracting the complexity of cancer. Due to the lack of topic analysis frameworks optimized specifically for cancer data, the studies on topic modeling in cancer research still have a strong challenge. Recently, deep learning (DL) based approaches were successfully employed to learn semantic and contextual information from scientific documents using word embeddings according to the hallmarks of cancer (HoC). However, those are only applicable to labeled data. There is a comparatively small number of documents that are labeled by experts. In the real world, there is a massive number of unlabeled documents that are available online. In this paper, we present a multi-task topic analysis (MTTA) framework to analyze cancer hallmark-specific topics from documents. The MTTA framework consists of three main subtasks: (1) cancer hallmark learning (CHL)—used to learn cancer hallmarks on existing labeled documents; (2) weak label propagation (WLP)—used to classify a large number of unlabeled documents with the pre-trained model in the CHL task; and (3) topic modeling (ToM)—used to discover topics for each hallmark category. In the CHL task, we employed a convolutional neural network (CNN) with pre-trained word embedding that represents semantic meanings obtained from an unlabeled large corpus. In the ToM task, we employed a latent topic model such as latent Dirichlet allocation (LDA) and probabilistic latent semantic analysis (PLSA) model to catch the semantic information learned by the CNN model for topic analysis. To evaluate the MTTA framework, we collected a large number of documents related to lung cancer in a case study. We also conducted a comprehensive performance evaluation for the MTTA framework, comparing it with several approaches.

scholarly journals A word embedding topic model for topic detection and summary in social networks

2019 ◽  
Vol 52 (9-10) ◽  
pp. 1289-1298 ◽  
Author(s):  
Lei Shi ◽  
Gang Cheng ◽  
Shang-ru Xie ◽  
Gang Xie
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Latent Dirichlet Allocation ◽  
Semantic Analysis ◽  
Topic Model ◽  
Word Embedding ◽  
Probabilistic Latent Semantic Analysis ◽  
Topic Detection ◽  
Short Text ◽  
Internal Relationship

The aim of topic detection is to automatically identify the events and hot topics in social networks and continuously track known topics. Applying the traditional methods such as Latent Dirichlet Allocation and Probabilistic Latent Semantic Analysis is difficult given the high dimensionality of massive event texts and the short-text sparsity problems of social networks. The problem also exists of unclear topics caused by the sparse distribution of topics. To solve the above challenge, we propose a novel word embedding topic model by combining the topic model and the continuous bag-of-words mode (Cbow) method in word embedding method, named Cbow Topic Model (CTM), for topic detection and summary in social networks. We conduct similar word clustering of the target social network text dataset by introducing the classic Cbow word vectorization method, which can effectively learn the internal relationship between words and reduce the dimensionality of the input texts. We employ the topic model-to-model short text for effectively weakening the sparsity problem of social network texts. To detect and summarize the topic, we propose a topic detection method by leveraging similarity computing for social networks. We collected a Sina microblog dataset to conduct various experiments. The experimental results demonstrate that the CTM method is superior to the existing topic model method.

scholarly journals Topic Modeling as a Tool for Analyzing Library Chat Transcripts

2021 ◽  
Vol 40 (3) ◽  
Author(s):  
HyunSeung Koh ◽  
Mark Fienup
Keyword(s):  
Topic Modeling ◽  
Latent Dirichlet Allocation ◽  
Semantic Analysis ◽  
Academic Library ◽  
Qualitative Evaluation ◽  
Probabilistic Latent Semantic Analysis ◽  
Analysis Tool ◽  
Chat Reference ◽  
Better Than

Library chat services are an increasingly important communication channel to connect patrons to library resources and services. Analysis of chat transcripts could provide librarians with insights into improving services. Unfortunately, chat transcripts consist of unstructured text data, making it impractical for librarians to go beyond simple quantitative analysis (e.g., chat duration, message count, word frequencies) with existing tools. As a stepping-stone toward a more sophisticated chat transcript analysis tool, this study investigated the application of different types of topic modeling techniques to analyze one academic library’s chat reference data collected from April 10, 2015, to May 31, 2019, with the goal of extracting the most accurate and easily interpretable topics. In this study, topic accuracy and interpretability—the quality of topic outcomes—were quantitatively measured with topic coherence metrics. Additionally, qualitative accuracy and interpretability were measured by the librarian author of this paper depending on the subjective judgment on whether topics are aligned with frequently asked questions or easily inferable themes in academic library contexts. This study found that from a human’s qualitative evaluation, Probabilistic Latent Semantic Analysis (pLSA) produced more accurate and interpretable topics, which is not necessarily aligned with the findings of the quantitative evaluation with all three types of topic coherence metrics. Interestingly, the commonly used technique Latent Dirichlet Allocation (LDA) did not necessarily perform better than pLSA. Also, semi-supervised techniques with human-curated anchor words of Correlation Explanation (CorEx) or guided LDA (GuidedLDA) did not necessarily perform better than an unsupervised technique of Dirichlet Multinomial Mixture (DMM). Last, the study found that using the entire transcript, including both sides of the interaction between the library patron and the librarian, performed better than using only the initial question asked by the library patron across different techniques in increasing the quality of topic outcomes.

scholarly journals Estimating Topic Modeling Performance with Sharma–Mittal Entropy

Entropy ◽  
2019 ◽  
Vol 21 (7) ◽  
pp. 660 ◽  
Author(s):  
Sergei Koltcov ◽  
Vera Ignatenko ◽  
Olessia Koltsova
Keyword(s):  
Latent Semantic Analysis ◽  
Topic Modeling ◽  
Statistical Physics ◽  
Latent Dirichlet Allocation ◽  
Semantic Analysis ◽  
Probabilistic Latent Semantic Analysis ◽  
Model Parameters ◽  
Theoretical Ground ◽  
Text Documents ◽  
Optimizing Model

Topic modeling is a popular approach for clustering text documents. However, current tools have a number of unsolved problems such as instability and a lack of criteria for selecting the values of model parameters. In this work, we propose a method to solve partially the problems of optimizing model parameters, simultaneously accounting for semantic stability. Our method is inspired by the concepts from statistical physics and is based on Sharma–Mittal entropy. We test our approach on two models: probabilistic Latent Semantic Analysis (pLSA) and Latent Dirichlet Allocation (LDA) with Gibbs sampling, and on two datasets in different languages. We compare our approach against a number of standard metrics, each of which is able to account for just one of the parameters of our interest. We demonstrate that Sharma–Mittal entropy is a convenient tool for selecting both the number of topics and the values of hyper-parameters, simultaneously controlling for semantic stability, which none of the existing metrics can do. Furthermore, we show that concepts from statistical physics can be used to contribute to theory construction for machine learning, a rapidly-developing sphere that currently lacks a consistent theoretical ground.

scholarly journals Analyzing the Influence of Hyper-parameters and Regularizers of Topic Modeling in Terms of Renyi Entropy

Entropy ◽  
2020 ◽  
Vol 22 (4) ◽  
pp. 394
Author(s):  
Sergei Koltcov ◽  
Vera Ignatenko ◽  
Zeyd Boukhers ◽  
Steffen Staab
Keyword(s):  
Gibbs Sampling ◽  
Dirichlet Process ◽  
Topic Modeling ◽  
Statistical Physics ◽  
Latent Dirichlet Allocation ◽  
Semantic Analysis ◽  
Topic Models ◽  
Renyi Entropy ◽  
Rényi Entropy ◽  
Probabilistic Latent Semantic Analysis

Topic modeling is a popular technique for clustering large collections of text documents. A variety of different types of regularization is implemented in topic modeling. In this paper, we propose a novel approach for analyzing the influence of different regularization types on results of topic modeling. Based on Renyi entropy, this approach is inspired by the concepts from statistical physics, where an inferred topical structure of a collection can be considered an information statistical system residing in a non-equilibrium state. By testing our approach on four models—Probabilistic Latent Semantic Analysis (pLSA), Additive Regularization of Topic Models (BigARTM), Latent Dirichlet Allocation (LDA) with Gibbs sampling, LDA with variational inference (VLDA)—we, first of all, show that the minimum of Renyi entropy coincides with the “true” number of topics, as determined in two labelled collections. Simultaneously, we find that Hierarchical Dirichlet Process (HDP) model as a well-known approach for topic number optimization fails to detect such optimum. Next, we demonstrate that large values of the regularization coefficient in BigARTM significantly shift the minimum of entropy from the topic number optimum, which effect is not observed for hyper-parameters in LDA with Gibbs sampling. We conclude that regularization may introduce unpredictable distortions into topic models that need further research.

scholarly journals Modeling Topics in the Alternative Uses Task

2018 ◽  
Author(s):  
Rick Hass
Keyword(s):  
Topic Modeling ◽  
Latent Dirichlet Allocation ◽  
Semantic Analysis ◽  
Topic Model ◽  
Short Paper ◽  
Log P ◽  
Test Items ◽  
Log Likelihood ◽  
Data Log ◽  
Representation Of Knowledge

What is the representation of knowledge underlying human responses to alternative uses test items? This short paper describes an application of Latent Dirichlet Allocation (LDA, also known as topic modeling) to solve this problem of knowledge representation. For thissmall application, a document was de?ned as the set of responses given by a single participant to the alternative uses test brick prompt. This was chosen instead of single responses as the document unit, as single responses to alternative uses items are rather short, and LDA assumesthat documents are probabilistic mixtures of topics. The approach explored in this paper used LDA with Gibbs sampling, with the primary goal of model selection. The log likelihood of the data (log P(w | T)) was computed as to topics varied from 5 to 100. Results showed that the log likelihood increased to a peak at 15 topics and then steadily declined up to 100 topics. In the 15-topic model the most frequently appearing topic was that which gave the highest probability to the terms build, house, step, and smash. Documents best represented by that topic assignment were, on average, more similar to the dictionary de?finition of a brick based on vector cosines computed with Latent Semantic Analysis. Additional implications for using the topic model as a knowledge base for cognitive systems, and also as a tool for quantifying flexibility, the number of categories present in alternative uses response arrays, will also be discussed.

Topic prediction and knowledge discovery based on integrated topic modeling and deep neural networks approaches

2021 ◽  
pp. 1-17
Author(s):  
Zeinab Shahbazi ◽  
Yung-Cheol Byun
Keyword(s):  
Machine Learning ◽  
Knowledge Discovery ◽  
Real World ◽  
Topic Modeling ◽  
Latent Dirichlet Allocation ◽  
Semantic Analysis ◽  
Machine Learning Techniques ◽  
Probabilistic Latent Semantic Analysis ◽  
Learning Techniques ◽  
Dirichlet Allocation

Understanding the real-world short texts become an essential task in the recent research area. The document deduction analysis and latent coherent topic named as the important aspect of this process. Latent Dirichlet Allocation (LDA) and Probabilistic Latent Semantic Analysis (PLSA) are suggested to model huge information and documents. This type of contexts’ main problem is the information limitation, words relationship, sparsity, and knowledge extraction. The knowledge discovery and machine learning techniques integrated with topic modeling were proposed to overcome this issue. The knowledge discovery was applied based on the hidden information extraction to increase the suitable dataset for further analysis. The integration of machine learning techniques, Artificial Neural Network (ANN) and Long Short-Term (LSTM) are applied to anticipate topic movements. LSTM layers are fed with latent topic distribution learned from the pre-trained Latent Dirichlet Allocation (LDA) model. We demonstrate general information from different techniques applied in short text topic modeling. We proposed three categories based on Dirichlet multinomial mixture, global word co-occurrences, and self-aggregation using representative design and analysis of all categories’ performance in different tasks. Finally, the proposed system evaluates with state-of-art methods on real-world datasets, comprises them with long document topic modeling algorithms, and creates a classification framework that considers further knowledge and represents it in the machine learning pipeline.

scholarly journals Automated Seeded Latent Dirichlet Allocation for Social Media Based Event Detection and Mapping

Information ◽  
2020 ◽  
Vol 11 (8) ◽  
pp. 376 ◽  
Author(s):  
Cornelia Ferner ◽  
Clemens Havas ◽  
Elisabeth Birnbacher ◽  
Stefan Wegenkittl ◽  
Bernd Resch
Keyword(s):  
Event Detection ◽  
Disaster Response ◽  
Topic Modeling ◽  
Latent Dirichlet Allocation ◽  
Topic Model ◽  
Geographic Area ◽  
Relevant Information ◽  
Suggested Approach ◽  
Napa Valley ◽  
Source Of Information

In the event of a natural disaster, geo-tagged Tweets are an immediate source of information for locating casualties and damages, and for supporting disaster management. Topic modeling can help in detecting disaster-related Tweets in the noisy Twitter stream in an unsupervised manner. However, the results of topic models are difficult to interpret and require manual identification of one or more “disaster topics”. Immediate disaster response would benefit from a fully automated process for interpreting the modeled topics and extracting disaster relevant information. Initializing the topic model with a set of seed words already allows to directly identify the corresponding disaster topic. In order to enable an automated end-to-end process, we automatically generate seed words using older Tweets from the same geographic area. The results of two past events (Napa Valley earthquake 2014 and hurricane Harvey 2017) show that the geospatial distribution of Tweets identified as disaster related conforms with the officially released disaster footprints. The suggested approach is applicable when there is a single topic of interest and comparative data available.

scholarly journals Adaptive Self-Occlusion Behavior Recognition Based on pLSA

2013 ◽  
Vol 2013 ◽  
pp. 1-9 ◽  
Author(s):  
Hong-bin Tu ◽  
Li-min Xia ◽  
Lun-zheng Tan
Keyword(s):  
Random Field ◽  
Action Recognition ◽  
Markov Random Field ◽  
Semantic Analysis ◽  
Topic Model ◽  
Human Action Recognition ◽  
Human Action ◽  
Probabilistic Latent Semantic Analysis ◽  
Behavior Recognition ◽  
Markov Random

Human action recognition is an important area of human action recognition research. Focusing on the problem of self-occlusion in the field of human action recognition, a new adaptive occlusion state behavior recognition approach was presented based on Markov random field and probabilistic Latent Semantic Analysis (pLSA). Firstly, the Markov random field was used to represent the occlusion relationship between human body parts in terms an occlusion state variable by phase space obtained. Then, we proposed a hierarchical area variety model. Finally, we use the topic model of pLSA to recognize the human behavior. Experiments were performed on the KTH, Weizmann, and Humaneva dataset to test and evaluate the proposed method. The compared experiment results showed that what the proposed method can achieve was more effective than the compared methods.

PLSA-Based Personalized Information Retrieval with Network Regularization

2019 ◽  
Vol 12 (1) ◽  
pp. 105-116
Author(s):  
Qiuyu Zhu ◽  
Dongmei Li ◽  
Cong Dai ◽  
Qichen Han ◽  
Yi Lin
Keyword(s):  
Information Retrieval ◽  
Semantic Analysis ◽  
Topic Model ◽  
Rapid Development ◽  
Probabilistic Latent Semantic Analysis ◽  
Retrieval Model ◽  
User Interest ◽  
Model Based ◽  
User Query ◽  
Academic Information

With the rapid development of the Internet, the information retrieval model based on the keywords matching algorithm has not met the requirements of users, because people with various query history always have different retrieval intentions. User query history often implies their interests. Therefore, it is of great importance to enhance the recall ratio and the precision ratio by applying query history into the judgment of retrieval intentions. For this sake, this article does research on user query history and proposes a method to construct user interest model utilizing query history. Coordinately, the authors design a model called PLSA-based Personalized Information Retrieval with Network Regularization. Finally, the model is applied into academic information retrieval and the authors compare it with Baidu Scholar and the personalized information retrieval model based on the probabilistic latent semantic analysis topic model. The experiment results prove that this model can effectively extract topics and retrieves back results more satisfied for users' requirements. Also, this model improves the effect of retrieval results apparently. In addition, the retrieval model can be utilized not only in the academic information retrieval, but also in the personalized information retrieval on microblog search, associate recommendation, etc.

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