Cross-referencing Using Fine-grained Topic Modeling

Lund, Jeffrey; Armstrong, Piper; Fearn, Wilson; Cowley, Stephen J.; Hales, Emily; Seppi, Kevin

doi:10.18653/v1/n19-1399

“…In this study, we discussed some results and emerging trends and how they can be understandable from the perspective of earlier studies, including our comparisons. The difference between the two methods using bible text as corpora; the results give some indication about how evenly the distribution of words is between the documents (24) . The analysis shows that both techniques find the most significant percentage of instances and assessments of the context in which the words appear that contain words related to God's creation and his mandate for humanity.…”

Section: Discussionmentioning

confidence: 99%

A comparative analysis of Latent Semantic analysis and Latent Dirichlet allocation topic modeling methods using Bible data

Garbhapu¹,

Bodapati²

2020

IJST

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Objective: To compare the topic modeling techniques, as no free lunch theorem states that under a uniform distribution over search problems, all machine learning algorithms perform equally. Hence, here, we compare Latent Semantic Analysis (LSA) or Latent Dirichlet Allocation (LDA) to identify better performer for English bible data set which has not been studied yet. Methods: This comparative study divided into three levels: In the first level, bible data was extracted from the sources and preprocessed to remove the words and characters which were not useful to obtain the semantic structures or necessary patterns to make the meaningful corpus. In the second level, the preprocessed data were converted into a bag of words and numerical statistic TF-IDF (Term Frequency – Inverse Document Frequency) is used to assess how relevant a word is to a document in a corpus. In the third level, Latent Semantic analysis and Latent Dirichlet Allocations methods were applied over the resultant corpus to study the feasibility of the techniques. Findings: Based on our evaluation, we observed that the LDA achieves 60 to 75% superior performance when compared to LSA using document similarity within-corpus, document similarity with the unseen document. Additionally, LDA showed better coherence score (0.58018) than LSA (0.50395). Moreover, when compared to any word within-corpus, the word association showed better results with LDA. Some words have homonyms based on the context; for example, in the bible; bear has a meaning of punishment and birth. In our study, LDA word association results are almost near to human word associations when compared to LSA. Novelty: LDA was found to be the computationally efficient and interpretable method in adopting the English Bible dataset of New International Version that was not yet created. Keywords: Topic modeling; LSA; LDA; word association; document similarity;Bible data set

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“…These experiments relate to a large body of work that considers how preprocessing methods affect the downstream accuracy of various algorithms, ranging from topics in information retrieval (Chaudhari et al, 2015;Patil and Atique, 2013;Beil et al, 2002), text classification and regression (Forman, 2003;Yang and Pedersen, 1997;Vijayarani et al, 2015;Kumar and Harish, 2018;HaCohen-Kerner et al, 2020;Symeonidis et al, 2018;Weller et al, 2020), topic modeling (Blei et al, 2003;Lund et al, 2019;Schofield and Mimno, 2016;Schofield et al, 2017a,b), and even more complex tasks like question answering (Jijkoun et al, 2003;Carvalho et al, 2007) and machine translation (Habash, 2007;Habash and Sadat, 2006;Leusch et al, 2005;Weller et al, 2021;Mehta et al, 2020) to name a few. With the rise of noisy social media, text preprocessing has become important for tasks that use data from sources like Twitter and Reddit (Symeonidis et al, 2018;Singh and Kumari, 2016;Bao et al, 2014;Jianqiang, 2015;Weller and Seppi, 2020;Zirikly et al, 2019;Babanejad et al, 2020).…”

Section: Related Workmentioning

confidence: 99%

Exploring the Relationship Between Algorithm Performance, Vocabulary, and Run-Time in Text Classification

Fearn¹,

Weller²,

Seppi³

2021

Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Langua

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0

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Text classification is a significant branch of natural language processing, and has many applications including document classification and sentiment analysis. Unsurprisingly, those who do text classification are concerned with the run-time of their algorithms, many of which depend on the size of the corpus' vocabulary due to their bag-of-words representation. Although many studies have examined the effect of preprocessing techniques on vocabulary size and accuracy, none have examined how these methods affect a model's run-time.To fill this gap, we provide a comprehensive study that examines how preprocessing techniques affect the vocabulary size, model performance, and model run-time, evaluating ten techniques over four models and two datasets. We show that some individual methods can reduce run-time with no loss of accuracy, while some combinations of methods can trade 2-5% of the accuracy for up to a 65% reduction of run-time. Furthermore, some combinations of preprocessing techniques can even provide a 15% reduction in run-time while simultaneously improving model accuracy. 1 GroupMethod Vocab Size ↓ Train Time ↓ Test Time ↓ Accuracy ↑

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“…This hypothesis is also supported by the low correlation between vocabulary size and accuracy, indicating that what is in the vocabulary is more important than its size. These experiments relate to a large body of work that considers how preprocessing methods affect the downstream accuracy of various algorithms, ranging from topics in information retrieval (Chaudhari et al, 2015;Patil and Atique, 2013;Beil et al, 2002), text classification and regression (Forman, 2003;Yang and Pedersen, 1997;Vijayarani et al, 2015;Kumar and Harish, 2018;HaCohen-Kerner et al, 2020;Symeonidis et al, 2018;Weller et al, 2020), topic modeling (Blei et al, 2003;Lund et al, 2019;Schofield and Mimno, 2016;Schofield et al, 2017a,b), and even more complex tasks like question answering (Jijkoun et al, 2003;Carvalho et al, 2007) and machine translation (Habash, 2007;Habash and Sadat, 2006;Leusch et al, 2005;Weller et al, 2021;Mehta et al, 2020) 2020) analyze and cross-compare up to 16 different techniques for four machine learning algorithms. In contrast, our work is the first to examine these preprocessing techniques beyond accuracy, examining them in tandem with how they affect vocabulary size and run-time.…”

Section: Combination Techniquesmentioning

confidence: 99%

Exploring the Relationship Between Algorithm Performance, Vocabulary, and Run-Time in Text Classification

Fearn¹,

Weller²,

Seppi³

2021

Preprint

Self Cite

0

View full text Add to dashboard Cite

Text classification is a significant branch of natural language processing, and has many applications including document classification and sentiment analysis. Unsurprisingly, those who do text classification are concerned with the run-time of their algorithms, many of which depend on the size of the corpus' vocabulary due to their bag-of-words representation. Although many studies have examined the effect of preprocessing techniques on vocabulary size and accuracy, none have examined how these methods affect a model's run-time.To fill this gap, we provide a comprehensive study that examines how preprocessing techniques affect the vocabulary size, model performance, and model run-time, evaluating ten techniques over four models and two datasets. We show that some individual methods can reduce run-time with no loss of accuracy, while some combinations of methods can trade 2-5% of the accuracy for up to a 65% reduction of run-time. Furthermore, some combinations of preprocessing techniques can even provide a 15% reduction in run-time while simultaneously improving model accuracy. 1

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Cross-referencing Using Fine-grained Topic Modeling

Cited by 3 publications

References 8 publications

A comparative analysis of Latent Semantic analysis and Latent Dirichlet allocation topic modeling methods using Bible data

A comparative analysis of Latent Semantic analysis and Latent Dirichlet allocation topic modeling methods using Bible data

Exploring the Relationship Between Algorithm Performance, Vocabulary, and Run-Time in Text Classification

Exploring the Relationship Between Algorithm Performance, Vocabulary, and Run-Time in Text Classification

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