scholarly journals Discovering Tonal Profiles with Latent Dirichlet Allocation

2021 ◽  
Vol 4 ◽  
pp. 205920432110488
Author(s):  
Fabian C. Moss ◽  
Martin Rohrmeier
Keyword(s):  
Latent Dirichlet Allocation ◽  
Topic Model ◽  
A Priori ◽  
Music History ◽  
Arbitrary Parameter ◽  
Generative Process ◽  
Complementary Approach ◽  
The 19Th Century ◽  
Chord Recognition ◽  
Dirichlet Allocation

Music analysis, in particular harmonic analysis, is concerned with the way pitches are organized in pieces of music, and a range of empirical applications have been developed, for example, for chord recognition or key finding. Naturally, these approaches rely on some operationalization of the concepts they aim to investigate. In this study, we take a complementary approach and discover latent tonal structures in an unsupervised manner. We use the topic model Latent Dirichlet Allocation and apply it to a large historical corpus of musical pieces from the Western classical tradition. This method conceives topics as distributions of pitch classes without assuming a priori that they correspond to either chords, keys, or other harmonic phenomena. To illustrate the generative process assumed by the model, we create an artificial corpus with arbitrary parameter settings and compare the sampled pieces to real compositions. The results we obtain by applying the topic model to the musical corpus show that the inferred topics have music-theoretically meaningful interpretations. In particular, topics cover contiguous segments on the line of fifths and mostly correspond to diatonic sets. Moreover, tracing the prominence of topics over the course of music history over [Formula: see text]600 years reflects changes in the ways pitch classes are employed in musical compositions and reveals particularly strong changes at the transition from common-practice to extended tonality in the 19th century.

scholarly journals Joint Modeling of Topics, Citations, and Topical Authority in Academic Corpora

2017 ◽  
Vol 5 ◽  
pp. 191-204 ◽  
Author(s):  
Jooyeon Kim ◽  
Dongwoo Kim ◽  
Alice Oh
Keyword(s):  
Latent Dirichlet Allocation ◽  
Topic Model ◽  
Scientific Progress ◽  
Joint Modeling ◽  
Research Topics ◽  
Scientific Publications ◽  
Generative Process ◽  
Author Citation ◽  
Improved Accuracy ◽  
Dirichlet Allocation

Much of scientific progress stems from previously published findings, but searching through the vast sea of scientific publications is difficult. We often rely on metrics of scholarly authority to find the prominent authors but these authority indices do not differentiate authority based on research topics. We present Latent Topical-Authority Indexing (LTAI) for jointly modeling the topics, citations, and topical authority in a corpus of academic papers. Compared to previous models, LTAI differs in two main aspects. First, it explicitly models the generative process of the citations, rather than treating the citations as given. Second, it models each author’s influence on citations of a paper based on the topics of the cited papers, as well as the citing papers. We fit LTAI into four academic corpora: CORA, Arxiv Physics, PNAS, and Citeseer. We compare the performance of LTAI against various baselines, starting with the latent Dirichlet allocation, to the more advanced models including author-link topic model and dynamic author citation topic model. The results show that LTAI achieves improved accuracy over other similar models when predicting words, citations and authors of publications.

scholarly journals Intelligent radar software defect classification approach based on the latent Dirichlet allocation topic model

2021 ◽  
Vol 2021 (1) ◽  
Author(s):  
Xi Liu ◽  
Yongfeng Yin ◽  
Haifeng Li ◽  
Jiabin Chen ◽  
Chang Liu ◽  
...  
Keyword(s):  
Latent Dirichlet Allocation ◽  
Topic Model ◽  
Recall Rate ◽  
Defect Classification ◽  
Software Defects ◽  
Classification Approach ◽  
Software Defect ◽  
Model Combining ◽  
Dirichlet Allocation

AbstractExisting software intelligent defect classification approaches do not consider radar characters and prior statistics information. Thus, when applying these appaoraches into radar software testing and validation, the precision rate and recall rate of defect classification are poor and have effect on the reuse effectiveness of software defects. To solve this problem, a new intelligent defect classification approach based on the latent Dirichlet allocation (LDA) topic model is proposed for radar software in this paper. The proposed approach includes the defect text segmentation algorithm based on the dictionary of radar domain, the modified LDA model combining radar software requirement, and the top acquisition and classification approach of radar software defect based on the modified LDA model. The proposed approach is applied on the typical radar software defects to validate the effectiveness and applicability. The application results illustrate that the prediction precison rate and recall rate of the poposed approach are improved up to 15 ~ 20% compared with the other defect classification approaches. Thus, the proposed approach can be applied in the segmentation and classification of radar software defects effectively to improve the identifying adequacy of the defects in radar software.

scholarly journals A Multi-Label Supervised Topic Model Conditioned on Arbitrary Features for Gene Function Prediction

Genes ◽  
2019 ◽  
Vol 10 (1) ◽  
pp. 57 ◽  
Author(s):  
Lin Liu ◽  
Lin Tang ◽  
Xin Jin ◽  
Wei Zhou
Keyword(s):  
Gene Function ◽  
Latent Dirichlet Allocation ◽  
Topic Model ◽  
A Priori ◽  
Function Prediction ◽  
Biological Data ◽  
Machine Learning Algorithms ◽  
Multinomial Regression ◽  
Gene Function Prediction ◽  
Validation Experiment

With the continuous accumulation of biological data, more and more machine learning algorithms have been introduced into the field of gene function prediction, which has great significance in decoding the secret of life. Recently, a multi-label supervised topic model named labeled latent Dirichlet allocation (LLDA) has been applied to gene function prediction, and obtained more accurate and explainable predictions than conventional methods. Nonetheless, the LLDA model is only able to construct a bag of amino acid words as a classification feature, and does not support any other features, such as hydrophobicity, which has a profound impact on gene function. To achieve more accurate probabilistic modeling of gene function, we propose a multi-label supervised topic model conditioned on arbitrary features, named Dirichlet multinomial regression LLDA (DMR-LLDA), for introducing multiple types of features into the process of topic modeling. Based on DMR framework, DMR-LLDA applies an exponential a priori construction, previously with weighted features, on the hyper-parameters of gene-topic distribution, so as to reflect the effects of extra features on function probability distribution. In the five-fold cross validation experiment of a yeast datasets, DMR-LLDA outperforms the compared model significantly. All of these experiments demonstrate the effectiveness and potential value of DMR-LLDA for predicting gene function.

The surveillance of a supreme audit institution on related party transactions

2020 ◽  
Vol 32 (4) ◽  
pp. 577-603
Author(s):  
Gustavo Cesário ◽  
Ricardo Lopes Cardoso ◽  
Renato Santos Aranha
Keyword(s):  
Public Sector ◽  
Latent Dirichlet Allocation ◽  
Topic Model ◽  
Conflicts Of Interest ◽  
International Standards ◽  
Content Type ◽  
Related Party Transactions ◽  
The Public ◽  
Audit Reports ◽  
Dirichlet Allocation

PurposeThis paper aims to analyse how the supreme audit institution (SAI) monitors related party transactions (RPTs) in the Brazilian public sector. It considers definitions and disclosure policies of RPTs by international accounting and auditing standards and their evolution since 1980.Design/methodology/approachBased on archival research on international standards and using an interpretive approach, the authors investigated definitions and disclosure policies. Using a topic model based on latent Dirichlet allocation, the authors performed a content analysis on over 59,000 SAI decisions to assess how the SAI monitors RPTs.FindingsThe SAI investigates nepotism (a kind of RPT) and conflicts of interest up to eight times more frequently than related parties. Brazilian laws prevent nepotism and conflicts of interest, but not RPTs in general. Indeed, Brazilian public-sector accounting standards have not converged towards IPSAS 20, and ISSAI 1550 does not adjust auditing procedures to suit the public sector.Research limitations/implicationsThe SAI follows a legalistic auditing approach, indicating a need for regulation of related public-sector parties to improve surveillance. In addition to Brazil, other code law countries might face similar circumstances.Originality/valuePublic-sector RPTs are an under-investigated field, calling for attention by academics and standard-setters. Text mining and latent Dirichlet allocation, while mature techniques, are underexplored in accounting and auditing studies. Additionally, the Python script created to analyse the audit reports is available at Mendeley Data and may be used to perform similar analyses with minor adaptations.

Linguistic and Personological Features of the Doka & Martin Grieving Style Continuum

2021 ◽  
Author(s):  
James Geisler ◽  
Cass Dykeman
Keyword(s):  
Corpus Linguistics ◽  
Language Use ◽  
Latent Dirichlet Allocation ◽  
Topic Model ◽  
Psychological Processes ◽  
Clinical Vignettes ◽  
Present Tense ◽  
Dirichlet Allocation

While there is extensive research on the adaptive grief styles developed by Doka and Martin, this study is the first of its kind to explore the language used among each style of grief. This study used clinical vignettes from a variety of sources on instrumental and intuitive grieving in an attempt to decipher the language use across various linguistic and psychological processes. Following this analysis, latent Dirichlet allocation (LDA) was used fitting a two-topic model to analyze differences between topics while additionally performing a supervised LDA analysis. The strongest data from this study relate to intuitive grief, which found a higher use of present-tense language in comparison to the instrumental grief style. In addition, results found that the language used by intuitive grievers is slightly more distinguishable than that of its instrumental counterpart. Several implications for counseling and research were developed in response to these findings.Keywords: corpus linguistics, grieving, instrumental grieving, intuitive grieving, LIWC, latent Dirichlet allocation (LDA).

scholarly journals Predicting Component Failures Using Latent Dirichlet Allocation

2015 ◽  
Vol 2015 ◽  
pp. 1-15
Author(s):  
Hailin Liu ◽  
Ling Xu ◽  
Mengning Yang ◽  
Meng Yan ◽  
Xiaohong Zhang
Keyword(s):  
Latent Dirichlet Allocation ◽  
Semantic Information ◽  
Topic Model ◽  
Source Code ◽  
Statistical Correlation ◽  
Similarity Matrix ◽  
Component Failure ◽  
Program Behavior ◽  
Component Failures ◽  
Dirichlet Allocation

Latent Dirichlet Allocation (LDA) is a statistical topic model that has been widely used to abstract semantic information from software source code. Failure refers to an observable error in the program behavior. This work investigates whether semantic information and failures recorded in the history can be used to predict component failures. We use LDA to abstract topics from source code and a new metric (topic failure density) is proposed by mapping failures to these topics. Exploring the basic information of topics from neighboring versions of a system, we obtain a similarity matrix. Multiply the Topic Failure Density (TFD) by the similarity matrix to get the TFD of the next version. The prediction results achieve an average 77.8% agreement with the real failures by considering the top 3 and last 3 components descending ordered by the number of failures. We use the Spearman coefficient to measure the statistical correlation between the actual and estimated failure rate. The validation results range from 0.5342 to 0.8337 which beats the similar method. It suggests that our predictor based on similarity of topics does a fine job of component failure prediction.

Ldagibbs: A Command for Topic Modeling in Stata Using Latent Dirichlet Allocation

2018 ◽  
Vol 18 (1) ◽  
pp. 101-117 ◽  
Author(s):  
Carlo Schwarz
Keyword(s):  
Machine Learning ◽  
Probability Distribution ◽  
Topic Modeling ◽  
Latent Dirichlet Allocation ◽  
Topic Model ◽  
Topic Models ◽  
Text Documents ◽  
Text Data ◽  
Dirichlet Allocation

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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