Search 250 million research papers

Topic Modeling

Sunil Kumar · 2024

Cited by 0

Topic Modeling

Matthew L. Jockers · Quantitative methods in the humanities and social sciences · 2014

Cited by 3

Topic Modeling

Ernie Teo · 2016

Cited by 0

Topic Modeling

Sarić, Sanja, Krušić, Lucija · GrazMuseum Online (GrazMuseum) · 2024

Im 'Kompetenznetzwerk Digitale Edition' (KONDE) beschäftigen sich sieben universitäre Partner und drei weitere Einrichtungen aus unterschiedlichen Blickwinkeln mit theoretischen und praktischen Aspekten der Digitalen Edition.

Cited by 0Open access

Topic Modeling

Chung, Byron H. · Scholarship@Western (Western University) · 2022

Given the growing recognition of the role of teachers as agents in 21st century learning environments, this proposed study builds on our work on ‘Teacher Design Knowledge’ (TDK) as critical capabilities for teachers in designing and implementing innovative curriculum, pedagogy, and technology through researcher-practitioner collaboration (SSHRC Insight Grant 2017-2024, Kim, M. S., PI). As one of TDK competencies, teacher data literacies (TDL), the ability to derive meaningful information from multimodal data (e.g., drawings, 3D modeling, gaze, gestures, emotions) in student learning, is a relatively new concept for teachers in contexts of cultural and linguistic diversity. Although the COVID-19 pandemic has accelerated digital transformation of education, most research methodological tools in data literacies have been still predominantly focusing on pre-collected datasets of written texts. Educational researchers have increasingly called for adapting research methodologies to analyze real-world, ecological, and multimodal datasets from culturally and linguistically diverse (CLD) students. There has been no consensus in research methods in multimodal data of CLD students. To respond to these challenges, this study utilizes a topic modeling approach TowaRds Automated aNalySis of research Methods In Teacher daTa Literacies ('TRANSMITTAL').

Cited by 0

Topic modeling

Alejandro Buitrago López · OSF Preprints (OSF Preprints) · 2024

Cited by 0

Topic modeling

Alejandro Buitrago López · OSF Preprints (OSF Preprints) · 2024

Cited by 0

Topic modeling

Hanna Wallach · 2006

Some models of textual corpora employ text generation methods involving n-gram statistics, while others use latent topic variables inferred using the "bag-of-words" assumption, in which word order is ignored. Previously, these methods have not been combined. In this work, I explore a hierarchical generative probabilistic model that incorporates both n-gram statistics and latent topic variables by extending a unigram topic model to include properties of a hierarchical Dirichlet bigram language model. The model hyperparameters are inferred using a Gibbs EM algorithm. On two data sets, each of 150 documents, the new model exhibits better predictive accuracy than either a hierarchical Dirichlet bigram language model or a unigram topic model. Additionally, the inferred topics are less dominated by function words than are topics discovered using unigram statistics, potentially making them more meaningful.

Cited by 1,057

Topic Modeling

Sunil Kumar · RePEc: Research Papers in Economics

Abstract Topic modeling is a popular technique in natural language processing (NLP) that allows us to discover underlying topics or themes within a collection of documents. With the increasing amount of textual data generated in various domains, such as social media, news, and scientific literature, topic modeling has become an important tool for data analysis and information retrieval.

Cited by 0

Topic Modeling

Hae-Wol Cho · Osong Public Health and Research Perspectives · 2019

A topic model is a type of statistical model to determine abstract "topics" that occur in a collection of documents. Topic modeling is a frequently used text-mining tool employed in the discovery of hidden semantic structure in a body of text.

Cited by 10Open access

Topic modeling

Shulong Tan A, Jiajun Bu A, Xuzhen Qin A et al. · 2013

Latent dirichlet allocation Transfer learning a b s t r a c t Due to the scarcity of user interest information in the target domain, recommender systems generally suffer from the sparsity problem. To alleviate this limitation, one natural way is to transfer user interests in other domains to the target domain. However, objects in different domains may be in different media types, which make it very difficult to find the correlations between them. In this paper, we propose a Bayesian hierarchical approach based on Latent Dirichlet Allocation (LDA) to transfer user interests cross domains or media. We model documents (corresponding to media objects) from different domains and user interests in a common topic space, and learn topic distributions for documents and user interests together. Specifically, to learn the model, we combine multi-type media information: media descriptions, user-generated text data and ratings. With this model, recommendation can be done in multiple ways, via predicting ratings, comparing topic distributions of documents and user interests directly and so on. Experiments on two real world datasets demonstrate that our proposed method is effective in addressing the sparsity problem by transferring user interests cross domains. & 2013 Elsevier B.V. All rights reserved. 1.

Cited by 0

Topic Modeling

Matthew L. Jockers, Rosamond Thalken · Quantitative methods in the humanities and social sciences · 2020

Cited by 3

Topic Modeling Assignment

Houston, Natalie M., Swafford, Joanna · Humanities Commons CORE (Modern Language Association / Columbia University) · 2020

Curatorial note from Digital Pedagogy in the Humanities: As Elijah Meeks and Scott Weingart suggest in their introduction to a special issue of Journal of Digital Humanities, "[t]opic modeling could stand in as a synecdoche of digital humanities" because of its algorithmic complexity and potential obscurity. Joanna Swafford's two-part topic modeling assignment for undergraduates makes it accessible by giving a clear explanation of the several steps required to prepare textual data, import it into the graphical user interface tool for MALLET, and explore the results. This assignment requires students to try the topic modeling process with different parameters and to assess the results of their experiments. This assignment helps students learn a specific method of text analysis as well as skills in visualizing and interpreting its results in relation to the historical and literary topics of the course.

Cited by 0Open access

LDA Topic Modeling:

Colin Allen, Jaimie Murdock · University of Pittsburgh Press eBooks · 2022

Cited by 2

Topic modeling datasets

I. Vera · Zenodo (CERN European Organization for Nuclear Research) · 2023

Lemmatized texts of three datasets (Lenta, 20 Newsgroups, and WoS) used in the numerical experiments of work "Topic models with elements of neural networks: investigation of stability, coherence, and determining the optimal number of topics".

Cited by 0Open access

Erowid topic modeling

Vince Polito, Colin Klein, Alex O. Holcombe · OSF Preprints (OSF Preprints) · 2020

Cited by 0

Topic modeling results

Sara Perlstein, Marc Verboord · Figshare · 2021

The topic modeling results of all countries (DK, DE, NL, and SE) and all three phases, analyzed with ConText (iSchool, Illinois 2014).

Cited by 0Open access

Generalized Topic Modeling

Avrim Blum, Nika Haghtalab · arXiv (Cornell University) · 2016

Recently there has been significant activity in developing algorithms with provable guarantees for topic modeling. In standard topic models, a topic (such as sports, business, or politics) is viewed as a probability distribution $\vec a_i$ over words, and a document is generated by first selecting a mixture $\vec w$ over topics, and then generating words i.i.d. from the associated mixture $A{\vec w}$. Given a large collection of such documents, the goal is to recover the topic vectors and then to correctly classify new documents according to their topic mixture. In this work we consider a broad generalization of this framework in which words are no longer assumed to be drawn i.i.d. and instead a topic is a complex distribution over sequences of paragraphs. Since one could not hope to even represent such a distribution in general (even if paragraphs are given using some natural feature representation), we aim instead to directly learn a document classifier. That is, we aim to learn a predictor that given a new document, accurately predicts its topic mixture, without learning the distributions explicitly. We present several natural conditions under which one can do this efficiently and discuss issues such as noise tolerance and sample complexity in this model. More generally, our model can be viewed as a generalization of the multi-view or co-training setting in machine learning.

Cited by 2Open access

Coordinated Topic Modeling

Pritom Saha Akash, Jie Huang, Kevin Chen–Chuan Chang · arXiv (Cornell University) · 2022

We propose a new problem called coordinated topic modeling that imitates human behavior while describing a text corpus. It considers a set of well-defined topics like the axes of a semantic space with a reference representation. It then uses the axes to model a corpus for easily understandable representation. This new task helps represent a corpus more interpretably by reusing existing knowledge and benefits the corpora comparison task. We design ECTM, an embedding-based coordinated topic model that effectively uses the reference representation to capture the target corpus-specific aspects while maintaining each topic's global semantics. In ECTM, we introduce the topic- and document-level supervision with a self-training mechanism to solve the problem. Finally, extensive experiments on multiple domains show the superiority of our model over other baselines.

Cited by 0Open access

Topic modeling results

Perlstein, Sara, Verboord, Marc · Figshare · 2021

The topic modeling results of all countries (DK, DE, NL, and SE) and all three phases, analyzed with ConText (iSchool, Illinois 2014).

Cited by 0Open access