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Referencing Scientific Articles by LDA Technology

Boussaadi Smail
2021 Zenodo  
Referencing scientific articles by topics.  ...  LDA Model Latent Dirichlet Allocation is an unsupervised method of topic modeling.  ...  The coherence metric is used to evaluate the quality of modeling ability of the model for each data aggregation. A higher coherence score offers meaningful and interpretable topics.  ... 
doi:10.5281/zenodo.5802437 fatcat:hozipg77jbeormofxv4qctqafa

Capturing Interdisciplinarity in Academic Abstracts

Federico Nanni, Laura Dietz, Stefano Faralli, Gloran Glavaš, Simone Paolo Ponzetto
2016 D-Lib Magazine  
) scheme [15] and (2) vectors capturing the distributions of latent topics in the abstract, obtained using the topic models.  ...  Latent dirichlet allocation. The Journal of Machine Learning Research , 3:993-1022, 2003. [2] J. Chuang, D. Ramage, C. Manning, and J.  ... 
doi:10.1045/september2016-nanni fatcat:5mve2bsf5rdt3kvn2ncfia3dkq

TWAG: A Topic-Guided Wikipedia Abstract Generator [article]

Fangwei Zhu, Shangqing Tu, Jiaxin Shi, Juanzi Li, Lei Hou, Tong Cui
2021 arXiv   pre-print
However, previous works generally view the abstract as plain text, ignoring the fact that it is a description of a certain entity and can be decomposed into different topics.  ...  Then, we predict the topic distribution of each abstract sentence, and decode the sentence from topic-aware representations with a Pointer-Generator network.  ...  Inspired by this work, Perez-Beltrachini et al. (2019) uses a convolutional encoder and a hierarchical decoder, and utilizes the Latent Dirichlet Allocation model (LDA) to render the decoder topic-aware  ... 
arXiv:2106.15135v1 fatcat:gr75lnsp7jhhtfvxw3dzidoqcu

Partially labeled topic models for interpretable text mining

Daniel Ramage, Christopher D. Manning, Susan Dumais
2011 Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining - KDD '11  
In this paper, we present two new partially supervised generative models of labeled text, Partially Labeled Dirichlet Allocation (PLDA) and the Partially Labeled Dirichlet Process (PLDP).  ...  These models make use of the unsupervised learning machinery of topic models to discover the hidden topics within each label, as well as unlabeled, corpus-wide latent topics.  ...  Popular unsupervised approaches like Latent Dirichlet Allocation [5] , Latent Semantic Indexing [11] and related methods [20, 3] are well suited for exploratory text analysis-e.g.  ... 
doi:10.1145/2020408.2020481 dblp:conf/kdd/RamageMD11 fatcat:iksnzhsbofctffqfy43l6ma2d4

Abstract Representations of Plot Structure

Micha Elsner
2015 Linguistic Issues in Language Technology  
Despite its importance, however, there are few computational studies of the large-scale structure of novels—and many popular representations for discourse modeling do not work very well for novelistic texts  ...  This paper describes a high-level representation of plot structure which tracks the frequency of mentions of different characters, topics and emotional words over time.  ...  This paper describes representations based on sentiment words, the frequencies of mentions of the characters themselves, and word clusters created by Latent Dirichlet Allocation (henceforth, LDA; Blei  ... 
doi:10.33011/lilt.v12i.1381 fatcat:m45vqsfrmzfabodp5nza7jsclu

Evaluating the Impact of OCR Errors on Topic Modeling [chapter]

Stephen Mutuvi, Antoine Doucet, Moses Odeo, Adam Jatowt
2018 Lecture Notes in Computer Science  
Based on experiments performed on OCR text corpora, we observe that OCR noise negatively impacts the stability and coherence of topics generated by topic modeling algorithms and we quantify the strength  ...  In this paper, we explore the impact of OCR errors on the identification of topics from a corpus comprising text from historical OCRed documents.  ...  One of the most commonly used probabilistic algorithms for topic modeling is the Latent Dirichlet Allocation (LDA) [3] .  ... 
doi:10.1007/978-3-030-04257-8_1 fatcat:qikszebtf5gjbhz55corlljyfy

Deneysel Yazılım Mühendisliğindeki Araştırma Eğilimleri için Metin Madenciliği

Gul TOKDEMİR
2021 Journal of Polytechnic  
Using a probabilistic topic modelling technique (Latent Dirichlet Allocation) , it brings forward the main topics of research within this domain.  ...  ABSTRACT This paper intends to examine the research trends in Empirical Software Engineering domain within the last two decades using text mining.  ...  In the following sections, we present our research methodology; topic modeling based on Latent Dirichlet Allocation (LDA), a probabilistic text mining technique.  ... 
doi:10.2339/politeknik.831391 fatcat:kycrunv2czdzjmsvo6dykf2owm

What Judges Want to Know From Forensic Evaluators in Child Custody and Child Protection Cases: Analyzing Forensic Assignments With Latent Dirichlet Allocation

Jelena Zumbach, Renate Volbert
2021 Frontiers in Psychology  
Latent Dirichlet allocation (LDA) is used to analyze the referral questions that these judges pose to forensic evaluators in terms of (a) underlying topics (latent dimensions) that can be identified within  ...  This analysis is based on unclassified text data extracted from German court files.  ...  Most of the data examined were social media or company feedback posts (Nguyen et al., 2015; Mou et al., 2019) and titles, abstracts, or full texts of scientific studies (Mora et al., 2018) .  ... 
doi:10.3389/fpsyg.2021.603597 pmid:33927663 pmcid:PMC8076539 fatcat:q2mqrl3zjndcvpgdobfcesesv4

Termite

Jason Chuang, Christopher D. Manning, Jeffrey Heer
2012 Proceedings of the International Working Conference on Advanced Visual Interfaces - AVI '12  
Topic models aid analysis of text corpora by identifying latent topics based on co-occurring words.  ...  In this paper we present Termite, a visual analysis tool for assessing topic model quality. Termite uses a tabular layout to promote comparison of terms both within and across latent topics.  ...  RELATED WORK Latent Dirichlet allocation (LDA) [3] is a popular approach for uncovering latent topics: multinomial probability distributions over terms, generated by soft clustering of words based on  ... 
doi:10.1145/2254556.2254572 dblp:conf/avi/ChuangMH12 fatcat:rjujvpqxsrgntct3rhvcpy3ute

Multi-Task Topic Analysis Framework for Hallmarks of Cancer with Weak Supervision

Batbaatar, Pham, Ryu
2020 Applied Sciences  
In the ToM task, we employed a latent topic model such as latent Dirichlet allocation (LDA) and probabilistic latent semantic analysis (PLSA) model to catch the semantic information learned by the CNN  ...  large number of unlabeled documents with the pre-trained model in the CHL task; and (3) topic modeling (ToM)—used to discover topics for each hallmark category.  ...  Traditional topic models such as latent Dirichlet allocation (LDA) [16] and probabilistic latent semantic analysis (PLSA) [17] have been successfully employed in various text corpora.  ... 
doi:10.3390/app10030834 fatcat:4pain7wgyvg57mnabnzzoyyz4m

Yoga-Veganism: Correlation Mining of Twitter Health Data [article]

Tunazzina Islam
2019 arXiv   pre-print
We need to mine the data to explore hidden patterns or unknown correlations, find out the dominant topic in data and understand people's interest through the discussions.  ...  We evaluate accuracy by comparing with ground truth using manual annotation both for train and test data.  ...  Latent Dirichlet Allocation (LDA).  ... 
arXiv:1906.07668v1 fatcat:pdilp6j7nffwzd3frg4u3bzhp4

Using Topic Modeling Methods for Short-Text Data: A Comparative Analysis

Rania Albalawi, Tet Hin Yeap, Morad Benyoucef
2020 Frontiers in Artificial Intelligence  
As a result, latent Dirichlet allocation and non-negative matrix factorization methods delivered more meaningful extracted topics and obtained good results.  ...  These methods are latent semantic analysis, latent Dirichlet allocation, non-negative matrix factorization, random projection, and principal component analysis.  ...  ) , and latent Dirichlet allocation (LDA) (Blei et al., 2003) .  ... 
doi:10.3389/frai.2020.00042 pmid:33733159 pmcid:PMC7861298 fatcat:b3usadyc3rgtpnppnywkvol4ie

Recommending patents based on latent topics

Ralf Krestel, Padhraic Smyth
2013 Proceedings of the 7th ACM conference on Recommender systems - RecSys '13  
We investigate the use of latent Dirichlet allocation and Dirichlet multinomial regression to represent patent documents and to compute similarity scores.  ...  The availability of large volumes of granted patents and applications, all publicly available on the Web, enables the use of sophisticated text mining and information retrieval methods to facilitate access  ...  FINDING RELATED PATENTS We address the problem of finding related or similar patents by investigating a combination of (1) latent Dirichlet allocation (LDA) [4] , (2) Dirichlet multinomial regression  ... 
doi:10.1145/2507157.2507232 dblp:conf/recsys/KrestelS13 fatcat:2tl74fa4lbex7oyd2yemraspny

Understanding Customer Churn Prediction Research with Structural Topic Models

FRIDRICH MARTIN
2020 ECONOMIC COMPUTATION AND ECONOMIC CYBERNETICS STUDIES AND RESEARCH  
As a result, the structural model of 38 topics is formed and examined considering topic prevalence, its changes over time, and the scientific impact (citations).  ...  At last, we discuss possible future steps in topic modeling within the domain.  ...  Latent Dirichlet allocation Latent Dirichlet allocation (LDA) is the most prominent non-linear generative probability topic model.  ... 
doi:10.24818/18423264/54.4.20.19 fatcat:i563r3rqr5bnpd32z353qkdhwa

5335 days of Implementation Science: using natural language processing to examine publication trends and topics

Jonathan P Scaccia, Victoria C Scott
2021 Implementation Science  
Following standard preprocessing steps, we use topic modeling with Latent Dirichlet allocation (LDA) to cluster the abstracts following a minimization algorithm.  ...  We examined 30 topics and computed topic model statistics of quality. Analyses revealed that published articles largely reflect (i) characteristics of research, or (ii) domains of practice.  ...  To examine study question 1, we used a topic modeling approach using Latent Dirichlet allocation (LDA). The use of "topic" here has a technical definition.  ... 
doi:10.1186/s13012-021-01120-4 pmid:33902657 pmcid:PMC8077727 fatcat:e4ypxj6iq5hw7owv4l2gfe5b2q
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