Learning Hedonic Games via Probabilistic Topic Modeling

2019 ◽  
pp. 62-76 ◽  
Author(s):  
Athina Georgara ◽  
Thalia Ntiniakou ◽  
Georgios Chalkiadakis
Keyword(s):  
Topic Modeling ◽  
Probabilistic Topic Modeling ◽  
Hedonic Games

scholarly journals Improving Topic Coherence Using Entity Extraction Denoising

2018 ◽  
Vol 110 (1) ◽  
pp. 85-101 ◽  
Author(s):  
Ronald Cardenas ◽  
Kevin Bello ◽  
Alberto Coronado ◽  
Elizabeth Villota
Keyword(s):  
Topic Modeling ◽  
Human Perception ◽  
Relevant Information ◽  
Entity Recognition ◽  
Entity Extraction ◽  
Fine Grained ◽  
Job Advertisement ◽  
Coherence Score ◽  
Probabilistic Topic Modeling ◽  
Promising Solution

Abstract Managing large collections of documents is an important problem for many areas of science, industry, and culture. Probabilistic topic modeling offers a promising solution. Topic modeling is an unsupervised machine learning method and the evaluation of this model is an interesting problem on its own. Topic interpretability measures have been developed in recent years as a more natural option for topic quality evaluation, emulating human perception of coherence with word sets correlation scores. In this paper, we show experimental evidence of the improvement of topic coherence score by restricting the training corpus to that of relevant information in the document obtained by Entity Recognition. We experiment with job advertisement data and find that with this approach topic models improve interpretability in about 40 percentage points on average. Our analysis reveals as well that using the extracted text chunks, some redundant topics are joined while others are split into more skill-specific topics. Fine-grained topics observed in models using the whole text are preserved.

scholarly journals A Study on the Characteristics of Academic Topics Related to Renewable Energy Using the Structural Topic Modeling and the Weak Signal Concept

Energies ◽  
2021 ◽  
Vol 14 (5) ◽  
pp. 1497
Author(s):  
Chankook Park ◽  
Minkyu Kim
Keyword(s):  
Renewable Energy ◽  
Energy Storage ◽  
Topic Modeling ◽  
Academic Research ◽  
High Rate ◽  
Weak Signals ◽  
Energy Potential ◽  
Rate Of Increase ◽  
Probabilistic Topic Modeling ◽  
To Receive

It is important to examine in detail how the distribution of academic research topics related to renewable energy is structured and which topics are likely to receive new attention in the future in order for scientists to contribute to the development of renewable energy. This study uses an advanced probabilistic topic modeling to statistically examine the temporal changes of renewable energy topics by using academic abstracts from 2010–2019 and explores the properties of the topics from the perspective of future signs such as weak signals. As a result, in strong signals, methods for optimally integrating renewable energy into the power grid are paid great attention. In weak signals, interest in large-capacity energy storage systems such as hydrogen, supercapacitors, and compressed air energy storage showed a high rate of increase. In not-strong-but-well-known signals, comprehensive topics have been included, such as renewable energy potential, barriers, and policies. The approach of this study is applicable not only to renewable energy but also to other subjects.

scholarly journals Probabilistic topic modeling for the analysis and classification of genomic sequences

2015 ◽  
Vol 16 (S6) ◽  
Author(s):  
Massimo La Rosa ◽  
Antonino Fiannaca ◽  
Riccardo Rizzo ◽  
Alfonso Urso
Keyword(s):  
Topic Modeling ◽  
Genomic Sequences ◽  
Probabilistic Topic Modeling

Semantic search for public opinions on urban affairs: A probabilistic topic modeling-based approach

2016 ◽  
Vol 52 (3) ◽  
pp. 430-445 ◽  
Author(s):  
Baojun Ma ◽  
Nan Zhang ◽  
Guannan Liu ◽  
Liangqiang Li ◽  
Hua Yuan
Keyword(s):  
Topic Modeling ◽  
Semantic Search ◽  
Urban Affairs ◽  
Public Opinions ◽  
Probabilistic Topic Modeling

Efficient algorithms for graph regularized PLSA for probabilistic topic modeling

2019 ◽  
Vol 86 ◽  
pp. 236-247 ◽  
Author(s):  
Xin Wang ◽  
Ming-Ching Chang ◽  
Lan Wang ◽  
Siwei Lyu
Keyword(s):  
Topic Modeling ◽  
Efficient Algorithms ◽  
Probabilistic Topic Modeling

scholarly journals Probabilistic Topic Modeling for Comparative Analysis of Document Collections

2020 ◽  
Vol 14 (2) ◽  
pp. 1-27
Author(s):  
Ting Hua ◽  
Chang-Tien Lu ◽  
Jaegul Choo ◽  
Chandan K. Reddy
Keyword(s):  
Comparative Analysis ◽  
Topic Modeling ◽  
Document Collections ◽  
Probabilistic Topic Modeling
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