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Relevant bibliographies by topics / Dirichlet allocation

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Journal articles

Academic literature on the topic 'Dirichlet allocation'

Author: Grafiati

Published: 15 February 2025

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Journal articles on the topic "Dirichlet allocation"

1

Du, Lan, Wray Buntine, Huidong Jin, and Changyou Chen. "Sequential latent Dirichlet allocation." Knowledge and Information Systems 31, no. 3 (2011): 475–503. http://dx.doi.org/10.1007/s10115-011-0425-1.

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2

Schwarz, Carlo. "Ldagibbs: A Command for Topic Modeling in Stata Using Latent Dirichlet Allocation." Stata Journal: Promoting communications on statistics and Stata 18, no. 1 (2018): 101–17. http://dx.doi.org/10.1177/1536867x1801800107.

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In this article, I introduce the ldagibbs command, which implements latent Dirichlet allocation in Stata. Latent Dirichlet allocation is the most popular machine-learning topic model. Topic models automatically cluster text documents into a user-chosen number of topics. Latent Dirichlet allocation represents each document as a probability distribution over topics and represents each topic as a probability distribution over words. Therefore, latent Dirichlet allocation provides a way to analyze the content of large unclassified text data and an alternative to predefined document classifications

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3

Yoshida, Takahiro, Ryohei Hisano, and Takaaki Ohnishi. "Gaussian hierarchical latent Dirichlet allocation: Bringing polysemy back." PLOS ONE 18, no. 7 (2023): e0288274. http://dx.doi.org/10.1371/journal.pone.0288274.

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Topic models are widely used to discover the latent representation of a set of documents. The two canonical models are latent Dirichlet allocation, and Gaussian latent Dirichlet allocation, where the former uses multinomial distributions over words, and the latter uses multivariate Gaussian distributions over pre-trained word embedding vectors as the latent topic representations, respectively. Compared with latent Dirichlet allocation, Gaussian latent Dirichlet allocation is limited in the sense that it does not capture the polysemy of a word such as “bank.” In this paper, we show that Gaussia

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4

Archambeau, Cedric, Balaji Lakshminarayanan, and Guillaume Bouchard. "Latent IBP Compound Dirichlet Allocation." IEEE Transactions on Pattern Analysis and Machine Intelligence 37, no. 2 (2015): 321–33. http://dx.doi.org/10.1109/tpami.2014.2313122.

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5

Pion-Tonachini, Luca, Scott Makeig, and Ken Kreutz-Delgado. "Crowd labeling latent Dirichlet allocation." Knowledge and Information Systems 53, no. 3 (2017): 749–65. http://dx.doi.org/10.1007/s10115-017-1053-1.

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S.S., Ramyadharshni, and Pabitha Dr.P. "Topic Categorization on Social Network Using Latent Dirichlet Allocation." Bonfring International Journal of Software Engineering and Soft Computing 8, no. 2 (2018): 16–20. http://dx.doi.org/10.9756/bijsesc.8390.

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7

Syed, Shaheen, and Marco Spruit. "Exploring Symmetrical and Asymmetrical Dirichlet Priors for Latent Dirichlet Allocation." International Journal of Semantic Computing 12, no. 03 (2018): 399–423. http://dx.doi.org/10.1142/s1793351x18400184.

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Latent Dirichlet Allocation (LDA) has gained much attention from researchers and is increasingly being applied to uncover underlying semantic structures from a variety of corpora. However, nearly all researchers use symmetrical Dirichlet priors, often unaware of the underlying practical implications that they bear. This research is the first to explore symmetrical and asymmetrical Dirichlet priors on topic coherence and human topic ranking when uncovering latent semantic structures from scientific research articles. More specifically, we examine the practical effects of several classes of Diri

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8

Li, Gen, and Hazri Jamil. "Teacher professional learning community and interdisciplinary collaborative teaching path under the informationization basic education model." Yugoslav Journal of Operations Research, no. 00 (2024): 29. http://dx.doi.org/10.2298/yjor2403029l.

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The construction of a learning community cannot be separated from the participation of information technology. The current teacher learning community has problems of low interaction efficiency and insufficient enthusiasm for group cooperative teaching. This study adopts the Latent Dirichlet allocation method to process text data generated by teacher interaction from the evolution of knowledge topics in the learning community network space. At the same time, the interaction data of the network community learning space is used to extract the interaction characteristics between teachers, and a co

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9

Garg, Mohit, and Priya Rangra. "Bibliometric Analysis of Latent Dirichlet Allocation." DESIDOC Journal of Library & Information Technology 42, no. 2 (2022): 105–13. http://dx.doi.org/10.14429/djlit.42.2.17307.

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Latent Dirichlet Allocation (LDA) has emerged as an important algorithm in big data analysis that finds the group of topics in the text data. It posits that each text document consists of a group of topics, and each topic is a mixture of words related to it. With the emergence of a plethora of text data, the LDA has become a popular algorithm for topic modeling among researchers from different domains. Therefore, it is essential to understand the trends of LDA researches. Bibliometric techniques are established methods to study the research progress of a topic. In this study, bibliographic dat

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10

Chauhan, Uttam, and Apurva Shah. "Topic Modeling Using Latent Dirichlet allocation." ACM Computing Surveys 54, no. 7 (2022): 1–35. http://dx.doi.org/10.1145/3462478.

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We are not able to deal with a mammoth text corpus without summarizing them into a relatively small subset. A computational tool is extremely needed to understand such a gigantic pool of text. Probabilistic Topic Modeling discovers and explains the enormous collection of documents by reducing them in a topical subspace. In this work, we study the background and advancement of topic modeling techniques. We first introduce the preliminaries of the topic modeling techniques and review its extensions and variations, such as topic modeling over various domains, hierarchical topic modeling, word emb

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