scholarly journals Semi-supervised Max-margin Topic Model with Manifold Posterior Regularization

2017 ◽  
Author(s):  
Wenbo Hu ◽  
Jun Zhu ◽  
Hang Su ◽  
Jingwei Zhuo ◽  
Bo Zhang
Keyword(s):  
Supervised Learning ◽  
Topic Model ◽  
Topic Models ◽  
Stochastic Gradient ◽  
Mcmc Method ◽  
Tight Coupling ◽  
Label Information ◽  
Latent Topic ◽  
Latent Topics ◽  
Qualitative Performance

Supervised topic models leverage label information to learn discriminative latent topic representations. As collecting a fully labeled dataset is often time-consuming, semi-supervised learning is of high interest. In this paper, we present an effective semi-supervised max-margin topic model by naturally introducing manifold posterior regularization to a regularized Bayesian topic model, named LapMedLDA. The model jointly learns latent topics and a related classifier with only a small fraction of labeled documents. To perform the approximate inference, we derive an efficient stochastic gradient MCMC method. Unlike the previous semi-supervised topic models, our model adopts a tight coupling between the generative topic model and the discriminative classifier. Extensive experiments demonstrate that such tight coupling brings significant benefits in quantitative and qualitative performance.

Latent Topic Model for Indexing Arabic Documents

2014 ◽  
Vol 4 (1) ◽  
pp. 29-45 ◽  
Author(s):  
Rami Ayadi ◽  
Mohsen Maraoui ◽  
Mounir Zrigui
Keyword(s):  
Topic Model ◽  
Inflectional Morphology ◽  
Arabic Text ◽  
Text Representation ◽  
Text Documents ◽  
Latent Topic ◽  
Latent Topics ◽  
F Measure

In this paper, the authors present latent topic model to index and represent the Arabic text documents reflecting more semantics. Text representation in a language with high inflectional morphology such as Arabic is not a trivial task and requires some special treatments. The authors describe our approach for analyzing and preprocessing Arabic text then we describe the stemming process. Finally, the latent model (LDA) is adapted to extract Arabic latent topics, the authors extracted significant topics of all texts, each theme is described by a particular distribution of descriptors then each text is represented on the vectors of these topics. The experiment of classification is conducted on in house corpus; latent topics are learned with LDA for different topic numbers K (25, 50, 75, and 100) then the authors compare this result with classification in the full words space. The results show that performances, in terms of precision, recall and f-measure, of classification in the reduced topics space outperform classification in full words space and when using LSI reduction.

scholarly journals Latent Topic Estimation Based on Events in a Document

2012 ◽  
Vol 16 (5) ◽  
pp. 603-610
Author(s):  
Risa Kitajima ◽  
◽  
Ichiro Kobayashi
Keyword(s):  
Text Analysis ◽  
Latent Dirichlet Allocation ◽  
Topic Model ◽  
Latent Semantic Indexing ◽  
Document Retrieval ◽  
Retrieval Task ◽  
Latent Topic ◽  
Latent Topics ◽  
Definition Of ◽  
The Relationship

Several latent topic model-based methods such as Latent Semantic Indexing (LSI), Probabilistic LSI (pLSI), and Latent Dirichlet Allocation (LDA) have been widely used for text analysis. These methods basically assign topics to words, however, and the relationship between words in a document is therefore not considered. Considering this, we propose a latent topic extraction method that assigns topics to events that represent the relation between words in a document. There are several ways to express events, and the accuracy of estimating latent topics differs depending on the definition of an event. We therefore propose five event types and examine which event type works well in estimating latent topics in a document with a common document retrieval task. As an application of our proposed method, we also show multidocument summarization based on latent topics. Through these experiments, we have confirmed that our proposed method results in higher accuracy than the conventional method.

Graph Neural Collaborative Topic Model for Citation Recommendation

2022 ◽  
Vol 40 (3) ◽  
pp. 1-30
Author(s):  
Qianqian Xie ◽  
Yutao Zhu ◽  
Jimin Huang ◽  
Pan Du ◽  
Jian-Yun Nie
Keyword(s):  
Topic Model ◽  
Topic Models ◽  
Joint Modeling ◽  
Research Problem ◽  
High Order ◽  
Semantic Structure ◽  
Critical Research ◽  
First Order ◽  
Latent Topic ◽  
Graph Neural Networks

Due to the overload of published scientific articles, citation recommendation has long been a critical research problem for automatically recommending the most relevant citations of given articles. Relational topic models (RTMs) have shown promise on citation prediction via joint modeling of document contents and citations. However, existing RTMs can only capture pairwise or direct (first-order) citation relationships among documents. The indirect (high-order) citation links have been explored in graph neural network–based methods, but these methods suffer from the well-known explainability problem. In this article, we propose a model called Graph Neural Collaborative Topic Model that takes advantage of both relational topic models and graph neural networks to capture high-order citation relationships and to have higher explainability due to the latent topic semantic structure. Experiments on three real-world citation datasets show that our model outperforms several competitive baseline methods on citation recommendation. In addition, we show that our approach can learn better topics than the existing approaches. The recommendation results can be well explained by the underlying topics.

scholarly journals Mining heterogeneous clinical notes by multi-modal latent topic model

PLoS ONE ◽  
2021 ◽  
Vol 16 (4) ◽  
pp. e0249622
Author(s):  
Zhi Wen ◽  
Pratheeksha Nair ◽  
Chih-Ying Deng ◽  
Xing Han Lu ◽  
Edward Moseley ◽  
...  
Keyword(s):  
Mechanical Ventilation ◽  
Topic Model ◽  
Prolonged Mechanical Ventilation ◽  
Topic Models ◽  
Clinical Notes ◽  
Topic Distribution ◽  
Domain Expertise ◽  
Latent Topic ◽  
Poor Outcomes ◽  
Mimic Iii

Latent knowledge can be extracted from the electronic notes that are recorded during patient encounters with the health system. Using these clinical notes to decipher a patient’s underlying comorbidites, symptom burdens, and treatment courses is an ongoing challenge. Latent topic model as an efficient Bayesian method can be used to model each patient’s clinical notes as “documents” and the words in the notes as “tokens”. However, standard latent topic models assume that all of the notes follow the same topic distribution, regardless of the type of note or the domain expertise of the author (such as doctors or nurses). We propose a novel application of latent topic modeling, using multi-note topic model (MNTM) to jointly infer distinct topic distributions of notes of different types. We applied our model to clinical notes from the MIMIC-III dataset to infer distinct topic distributions over the physician and nursing note types. Based on manual assessments made by clinicians, we observed a significant improvement in topic interpretability using MNTM modeling over the baseline single-note topic models that ignore the note types. Moreover, our MNTM model led to a significantly higher prediction accuracy for prolonged mechanical ventilation and mortality using only the first 48 hours of patient data. By correlating the patients’ topic mixture with hospital mortality and prolonged mechanical ventilation, we identified several diagnostic topics that are associated with poor outcomes. Because of its elegant and intuitive formation, we envision a broad application of our approach in mining multi-modality text-based healthcare information that goes beyond clinical notes. Code available at https://github.com/li-lab-mcgill/heterogeneous_ehr.

scholarly journals Geographic Feature Type Topic Model (GFTTM): grounding topics in the landscape

2015 ◽  
Author(s):  
Benjamin Adams
Keyword(s):  
Physical Environment ◽  
Information Sources ◽  
Topic Model ◽  
Topic Models ◽  
Observation Data ◽  
Probabilistic Topic Models ◽  
Geographic Feature ◽  
Latent Topics ◽  
Type Data ◽  
Physical Features

Probabilistic topic models are a class of unsupervised machine learning models used for understanding the latent topics in a corpus of documents. A new method for combining geographic feature data with text from geo-referenced documents to create topic models that are grounded in the physical environment is proposed. The Geographic Feature Type Topic Model (GFTTM) models each document in a corpus as a mixture of feature type topics and abstract topics. Feature type topics are conditioned on additional observation data of the relative densities of geographic feature types co-located with the document's location referent, whereas abstract topics are trained independently of that information. The GFTTM is evaluated using geo-referenced Wikipedia articles and feature type data from volunteered geographic information sources. A technique for the measurement of semantic similarity of feature types and places based on the mixtures of topics associated with the types is also presented. The results of the evaluation demonstrate that GFTTM finds two distinct types of topics that can be used to disentangle how places are described in terms of its physical features and more abstract topics such as history and culture.

scholarly journals A Novel Feature-based Bayesian Model for Query Focused Multi-document Summarization

2013 ◽  
Vol 1 ◽  
pp. 89-98 ◽  
Author(s):  
Jiwei Li ◽  
Sujian Li
Keyword(s):  
Supervised Learning ◽  
Bayesian Model ◽  
Topic Model ◽  
Topic Models ◽  
Experimental Results ◽  
Learning Methods ◽  
Document Summarization ◽  
Feature Based

Supervised learning methods and LDA based topic model have been successfully applied in the field of multi-document summarization. In this paper, we propose a novel supervised approach that can incorporate rich sentence features into Bayesian topic models in a principled way, thus taking advantages of both topic model and feature based supervised learning methods. Experimental results on DUC2007, TAC2008 and TAC2009 demonstrate the effectiveness of our approach.

Latent Topic Model for Indexing Arabic Documents

2014 ◽  
Vol 4 (2) ◽  
pp. 57-72
Author(s):  
Rami Ayadi ◽  
Mohsen Maraoui ◽  
Mounir Zrigui
Keyword(s):  
Topic Model ◽  
Inflectional Morphology ◽  
Arabic Text ◽  
Text Representation ◽  
Text Documents ◽  
Latent Topic ◽  
Latent Topics ◽  
F Measure

In this paper, the authors present latent topic model to index and represent the Arabic text documents reflecting more semantics. Text representation in a language with high inflectional morphology such as Arabic is not a trivial task and requires some special treatments. The authors describe their approach for analyzing and preprocessing Arabic text then they describe the stemming process. Finally, the latent model (LDA) is adapted to extract Arabic latent topics, the authors extracted significant topics of all texts, each theme is described by a particular distribution of descriptors then each text is represented on the vectors of these topics. The experiment of classification is conducted on in house corpus; latent topics are learned with LDA for different topic numbers K (25, 50, 75, and 100) then they compare this result with classification in the full words space. The results show that performances, in terms of precision, recall and f-measure, of classification in the reduced topics space outperform classification in full words space and when using LSI reduction.

scholarly journals Geographic Feature Type Topic Model (GFTTM): grounding topics in the landscape

2015 ◽  
Author(s):  
Benjamin Adams
Keyword(s):  
Physical Environment ◽  
Information Sources ◽  
Topic Model ◽  
Topic Models ◽  
Observation Data ◽  
Probabilistic Topic Models ◽  
Geographic Feature ◽  
Latent Topics ◽  
Type Data ◽  
Physical Features

Probabilistic topic models are a class of unsupervised machine learning models used for understanding the latent topics in a corpus of documents. A new method for combining geographic feature data with text from geo-referenced documents to create topic models that are grounded in the physical environment is proposed. The Geographic Feature Type Topic Model (GFTTM) models each document in a corpus as a mixture of feature type topics and abstract topics. Feature type topics are conditioned on additional observation data of the relative densities of geographic feature types co-located with the document's location referent, whereas abstract topics are trained independently of that information. The GFTTM is evaluated using geo-referenced Wikipedia articles and feature type data from volunteered geographic information sources. A technique for the measurement of semantic similarity of feature types and places based on the mixtures of topics associated with the types is also presented. The results of the evaluation demonstrate that GFTTM finds two distinct types of topics that can be used to disentangle how places are described in terms of its physical features and more abstract topics such as history and culture.

Analysis and Visualization Latent Topic on COVID-19 Vaccine Tweet use two-stage topic modeling (Preprint)

2021 ◽  
Author(s):  
Faizah Faizah ◽  
Bor-Shen Lin
Keyword(s):  
Topic Modeling ◽  
Public Perception ◽  
Latent Dirichlet Allocation ◽  
World Health ◽  
Two Stage ◽  
The Public ◽  
Global Pandemic ◽  
Difficult Time ◽  
Latent Topic ◽  
Latent Topics

BACKGROUND The World Health Organization (WHO) declared COVID-19 as a global pandemic on January 30, 2020. However, the pandemic has not been over yet. Furthermore, in the first quartal of 2021, some countries face the third wave of the pandemic. During the difficult time, the development of the vaccines for COVID-19 accelerates rapidly. Understanding the public perception of the COVID-19 Vaccine according to the data collected from social media can widen the perspective on the state of the global pandemic OBJECTIVE This study explores and analyzes the latent topic on COVID-19 Vaccine Tweet posted by individuals from various countries by using two-stage topic modeling. METHODS A two-stage analysis in topic modeling was proposed to investigating people’s reactions in five countries. The first stage is Latent Dirichlet Allocation that produces the latent topics with the corresponding term distributions that facilitate the investigators to understand the main issues or opinions. The second stage then performs agglomerative clustering on the latent topics based on Hellinger distance, which merges close topics hierarchically into topic clusters to visualize those topics in either tree or graph views. RESULTS In general, the topic discussion regarding the COVID-19 Vaccine in five countries is similar. Topic themes such as "first vaccine" and & "vaccine effect" dominate the public discussion. The remarkable point is that people in some countries have some topic themes, such as "politician opinion" and " stay home" in Canada, "emergency" in India, and & "blood clots" in the United Kingdom. The analysis also shows the most popular COVID-19 Vaccine, which is gaining more public interest. CONCLUSIONS With LDA and Hierarchical clustering, two-stage topic modeling is powerful for visualizing the latent topics and understanding the public perception regarding the COVID-19 Vaccine.

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