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Graph Neural Collaborative Topic Model for Citation Recommendation
ACM Trans. Inf. Syst., 2021Due 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.
Qianqian Xie +4 more
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2021
Als zentrales Konstrukt der Kommunikationswissenschaft ist das Thema heute relevanter denn je. Für das Fach besitzt es als Ordnungseinheit der öffentlichen Meinung seit jeher eine besondere Bedeutung, verdankt seinen aktuellen Bedeutungszuwachs über die sozialwissenschaftliche Forschung hinaus aber erst dem Technologiewandel: Mit ihm stieg der Bedarf ...
Manika Lamba, Margam Madhusudhan
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Als zentrales Konstrukt der Kommunikationswissenschaft ist das Thema heute relevanter denn je. Für das Fach besitzt es als Ordnungseinheit der öffentlichen Meinung seit jeher eine besondere Bedeutung, verdankt seinen aktuellen Bedeutungszuwachs über die sozialwissenschaftliche Forschung hinaus aber erst dem Technologiewandel: Mit ihm stieg der Bedarf ...
Manika Lamba, Margam Madhusudhan
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Combine Topic Modeling with Semantic Embedding: Embedding Enhanced Topic Model
IEEE Transactions on Knowledge and Data Engineering, 2020Topic model and word embedding reflect two perspectives of text semantics. Topic model maps documents into topic distribution space by utilizing word collocation patterns within and across documents, while word embedding represents words within a ...
P. Zhang +4 more
semanticscholar +1 more source
Proceedings of the 23rd international conference on Machine learning - ICML '06, 2006
A family of probabilistic time series models is developed to analyze the time evolution of topics in large document collections. The approach is to use state space models on the natural parameters of the multinomial distributions that represent the topics.
Blei, David M., Lafferty, John D.
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A family of probabilistic time series models is developed to analyze the time evolution of topics in large document collections. The approach is to use state space models on the natural parameters of the multinomial distributions that represent the topics.
Blei, David M., Lafferty, John D.
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Neurocomputing, 2012
In this paper the problem of performing external validation of the semantic coherence of topic models is considered. The Fowlkes-Mallows index, a known clustering validation metric, is generalized for the case of overlapping partitions and multi-labeled collections, thus making it suitable for validating topic modeling algorithms.
Ramirez, E +3 more
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In this paper the problem of performing external validation of the semantic coherence of topic models is considered. The Fowlkes-Mallows index, a known clustering validation metric, is generalized for the case of overlapping partitions and multi-labeled collections, thus making it suitable for validating topic modeling algorithms.
Ramirez, E +3 more
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Deep LDA : A new way to topic model
Journal of Information and Optimization Sciences, 2019Probabilistic topic models like Latent Semantic Indexing (LSI), Latent Dirichlet Allocation (LDA) and Biterm Topic Model (BTM) have been successfully implemented and used in many areas like movie reviews, recommender systems and text summarization etc ...
M. Bhat +3 more
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Probabilistic Topic Model for Hybrid Recommender Systems: A Stochastic Variational Bayesian Approach
Marketing science (Providence, R.I.), 2018This paper proposes a novel covariate-guided heterogeneous supervised topic model for online movie recommendation and develops a stochastic variational Bayesian framework to achieve fast, scalable,...
Asim Ansari, Yang Li, Jonathan Z. Zhang
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The structural topic model for online review analysis
Journal of Hospitality and Tourism Technology, 2018Purpose The purpose of this study was to explore influences of review-related information on topical proportions and the pattern of word appearances in each topic (topical content) using structural topic model (STM).
E. Park, Bongsug Chae, J. Kwon
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On a Topic Model for Sentences
Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, 2016Probabilistic topic models are generative models that describe the content of documents by discovering the latent topics underlying them. However, the structure of the textual input, and for instance the grouping of words in coherent text spans such as ...
Georgios Balikas +2 more
semanticscholar +1 more source
Semantic-enhanced topic evolution analysis: a combination of the dynamic topic model and word2vec
Scientometrics, 2022Qian Gao +4 more
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