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A Semantic Cover Approach for Topic Modeling

Rajagopal Venkatesaramani, Doug Downey, Bradley Malin, Yevgeniy Vorobeychik
2019 Proceedings of the Eighth Joint Conference on Lexical and Computational Semantics (*  
We introduce a novel topic modeling approach based on constructing a semantic set cover for clusters of similar documents.  ...  Specifically, our approach first clusters documents using their Tf-Idf representation, and then covers each cluster with a set of topic words based on semantic similarity, defined in terms of a word embedding  ...  Topic Modeling Using a Semantic Cover We propose a simple topic modeling framework comprised of two steps. First, we cluster documents based on similarity.  ... 
doi:10.18653/v1/s19-1011 dblp:conf/starsem/Venkatesaramani19 fatcat:zaysztczn5g7jkkcoijxib6lji

Deep Learning for Semantic Composition

Xiaodan Zhu, Edward Grefenstette
2017 Proceedings of ACL 2017, Tutorial Abstracts  
In this tutorial, we will cover the fundamentals and selected research topics on neural networkbased modeling for semantic composition, which aims to learn distributed representations for larger spans  ...  We will then advance to discuss several selected topics. We first cover the models that consider compositional with non-compositional (e.g., holistically learned) semantics (Zhu et al., , 2015a .  ... 
doi:10.18653/v1/p17-5003 dblp:conf/acl/ZhuG17 fatcat:svm6zwik4jdljfm4dcjpw77ti4

Land cover harmonization using Latent Dirichlet Allocation

Zhan Li, Joanne C. White, Michael A. Wulder, Txomin Hermosilla, Andrew M. Davidson, Alexis J. Comber
2020 International Journal of Geographical Information Science  
We evaluated multiple harmonization approaches: using the LDA model alone and in combination with more commonly used information sources for harmonization (i.e. error matrices and semantic affinity scores  ...  To address these concerns and combine two 30-m resolution land cover products, we implemented a harmonization procedure using a Latent Dirichlet Allocation (LDA) model.  ...  of which require multi-disciplinary approaches.  ... 
doi:10.1080/13658816.2020.1796131 fatcat:ez6rj6i2tzer3cwj6zh6b6lt3y

Discovery of Semantic Relationships in PolSAR Images Using Latent Dirichlet Allocation

Radu Tanase, Reza Bahmanyar, Gottfried Schwarz, Mihai Datcu
2017 IEEE Geoscience and Remote Sensing Letters  
Our results demonstrate that topic semantics are close to human semantics used for basic land-cover types (e.g., grassland).  ...  We propose a multi-level semantics discovery approach for bridging the semantic gap when mining highresolution Polarimetric Synthetic Aperture Radar (PolSAR) remote sensing images.  ...  CONCLUSION In this letter, we propose a multi-level approach for semantic relationship discovery in PolSAR images.  ... 
doi:10.1109/lgrs.2016.2636663 fatcat:tjwmyyfrobaibb5sj7p65ma6mu

Modeling Frames in Argumentation

Yamen Ajjour, Milad Alshomary, Henning Wachsmuth, Benno Stein
2019 Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)  
For evaluation purposes, we provide a corpus with 12 326 debateportal arguments, organized along the frames of the debates' topics.  ...  We present a fully unsupervised approach to this task, which first removes topical information from the arguments and then identifies frames using clustering.  ...  For topic clusters, we focus on the semantic space LSA Debate since our approach performed the best in this semantic space.  ... 
doi:10.18653/v1/d19-1290 dblp:conf/emnlp/AjjourAWS19 fatcat:mm6dsgmlerd6bjjydwkmy3p3pu

Recommendation System based on Semantic Scholar Mining and Topic modeling: A behavioral analysis of researchers from six conferences [article]

Hamed Jelodar, Yongli Wang, Mahdi Rabbani, Ru-xin Zhao, Seyedvalyallah Ayobi, Peng Hu, Isma Masood
2018 arXiv   pre-print
In fact, We apply probabilistic topic modeling based on Gibbs sampling algorithms for a semantic mining from six conference publications in computer science from DBLP dataset.  ...  There are several approaches to implement recommendation systems, Latent Dirichlet Allocation (LDA) is one the popular techniques in Topic Modeling.  ...  Topic modeling In our approach, the goal of topic modeling is to search the topic words related to a novel document so as to obtain the summary candidate sentences for building a recommendation system.  ... 
arXiv:1812.08304v1 fatcat:ql264icqmnamzbyz45fweujuxi

FBK-TR: Applying SVM with Multiple Linguistic Features for Cross-Level Semantic Similarity

Ngoc Phuoc An Vo, Tommaso Caselli, Octavian Popescu
2014 Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014)  
In this paper, we, the FBK-TR team, describe our system participating in Task 3 "Cross-Level Semantic Similarity", at SemEval 2014.  ...  Recently, the task of measuring semantic similarity between given texts has drawn much attention from the Natural Language Processing community.  ...  The MALLET topic model package (McCallum, 2002 ) is a Java-based tool used for inferring hidden "topics" in new document collections using trained models.  ... 
doi:10.3115/v1/s14-2046 dblp:conf/semeval/VoCP14 fatcat:sfanj6q4drcrnjpjautxe6gvxe

Automatic labeling of multinomial topic models

Qiaozhu Mei, Xuehua Shen, ChengXiang Zhai
2007 Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining - KDD '07  
A common, major challenge in applying all such topic models to any text mining problem is to label a multinomial topic model accurately so that a user can interpret the discovered topic.  ...  So far, such labels have been generated manually in a subjective way. In this paper, we propose probabilistic approaches to automatically labeling multinomial topic models in an objective way.  ...  Definition 2 (Topic Label) A topic label, or a "label ", l, for a topic model θ, is a sequence of words which is semantically meaningful and covers the latent meaning of θ.  ... 
doi:10.1145/1281192.1281246 dblp:conf/kdd/MeiSZ07 fatcat:gpxwjhaeirfnlgrrx2vm5gxqvu

Feature-free Explainable Data Mining in SAR Images Using Latent Dirichlet Allocation

Chandrabali Karmakar, Corneliu Octavian Dumitru, Gottfried Schwarz, Mihai Datcu
2020 IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing  
We create the interpretable visualizations of the data utilizing these topics and compute the topic distributions for each land-cover class.  ...  In this article, we propose a promising approach for the application-oriented content classification of spaceborne radar imagery that presents an interesting alternative to popular current machine learning  ...  Bahmanyar for his support.  ... 
doi:10.1109/jstars.2020.3039012 fatcat:eynukzau7jgebnxxquh35c4axa

ETL4Social-Data: Modeling Approach for Topic Hierarchy

Afef Walha, Faiza Ghozzi, Faïez Gargouri
2017 Proceedings of the 9th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management  
For that, we propose an ETL4Social modeling approach that designs ETL processes suitable to social data characteristics.  ...  ETL4Social is considered a standard-based modeling approach using Business Process Modeling and Notation (BPMN).  ...  Modeling such processes is a hot topic for many researchers that proposed a novel approaches for transforming the social data to SDW.  ... 
doi:10.5220/0006588901070118 dblp:conf/ic3k/WalhaGG17 fatcat:tis2f6pfavhslls4mzoaunpdcq

Probabilistic Topic Models for Text Data Retrieval and Analysis

ChengXiang Zhai
2017 Proceedings of the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval - SIGIR '17  
As a new family of e ective general approaches to text data retrieval and analysis, probabilistic topic models, notably Probabilistic Latent Semantic Analysis (PLSA), Latent Dirichlet Allocations (LDA)  ...  , and many extensions of them, have been studied actively in the past decade with widespread applications. ese topic models are powerful tools for extracting and analyzing latent topics contained in text  ...  In the past decade, a special kind of statistical approaches, called probabilistic topic models, represented by Probabilistic Latent Semantic Analysis (PLSA) [2] and Latent Dirichlet Allocations (LDA  ... 
doi:10.1145/3077136.3082067 dblp:conf/sigir/Zhai17 fatcat:qp4ppcciefasxdx7efxp4fncxi

Page 446 of Computational Linguistics Vol. 17, Issue 4 [page]

1991 Computational Linguistics  
Each chapter includes a “Further reading” section that directs the reader to items included in the bibliography (27 pages) that are relevant to the major topics covered in the chapter.  ...  Beginning with Chapter 6, the focus shifts to developing a model for human lan- guage processing, giving careful attention to linguistic data and psychological research in order to clarify the motivation  ... 

GraphTMT: Unsupervised Graph-based Topic Modeling from Video Transcripts [article]

Lukas Stappen, Jason Thies, Gerhard Hagerer, Björn W. Schuller, Georg Groh
2021 arXiv   pre-print
Existing work tends to apply topic models on written text datasets. In this paper, we propose a topic extractor on video transcripts.  ...  Unlike most topic models, this approach works without knowing the true number of topics, which is important when no such assumption can or should be made.  ...  In this work, we propose a Graph-based Topic Modeling approach for Transcripts (GraphTMT).  ... 
arXiv:2105.01466v4 fatcat:5dategxljrfy7opaprrv5z7lla

Mathematical Theory of Domains

Alex Simpson
1998 Science of Computer Programming  
Such structures first arose in the development of denotational semantics of programming languages, where the notion of approximation was crucial for modelling recursion and recursively defined datatypes  ...  Other recent textbooks in the area have been primarily concerned with the denotational semantics of programming languages, introducing domain theory as a necessary tool for the provision of such.  ...  The second part of the book also covers two more standard topics: powerdomains and domain-theoretic models of lambda-calculi.  ... 
doi:10.1016/s0167-6423(98)00009-4 fatcat:2fxltgdj7fbwhemr3qve2ojnmm

Query-oriented text summarization based on hypergraph transversals

H. Van Lierde, Tommy W.S. Chow
2019 Information Processing & Management  
This approach fails to select a subset of jointly relevant sentences and it may produce redundant summaries that are missing important topics of the corpus.  ...  A thorough comparative analysis with related models on DUC benchmark datasets demonstrates the effectiveness of our approach, which outperforms existing graph- or hypergraph-based methods by at least 6%  ...  We may also further extend our topic model to take the polysemy of terms into acount: since each term may carry multiple meanings, a given term could refer to different topics depending on its context.  ... 
doi:10.1016/j.ipm.2019.03.003 fatcat:p62jlcn33ng2fd23kkoorfmjpe
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