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A Semantic Cover Approach for Topic Modeling
2019
Proceedings of the Eighth Joint Conference on Lexical and Computational Semantics (*
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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
2017
Proceedings of ACL 2017, Tutorial Abstracts
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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
2020
International Journal of Geographical Information Science
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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
2017
IEEE Geoscience and Remote Sensing Letters
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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
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)
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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]
2018
arXiv
pre-print
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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
2014
Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014)
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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
2007
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining - KDD '07
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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
2020
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
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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
2017
Proceedings of the 9th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management
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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
2017
Proceedings of the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval - SIGIR '17
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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
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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]
2021
arXiv
pre-print
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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
1998
Science of Computer Programming
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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
2019
Information Processing & Management
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2019 pre-print arXiv:1902.00672v1 fatcat:xumpmstvpfchlejxc774df7gfa
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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