Movie Genre Detection Using Topological Data Analysis

Doshi, Pratik; Zadrozny, Wlodek

doi:10.1007/978-3-030-00810-9_11

Cited by 20 publications

(22 citation statements)

References 15 publications

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“…More specifically, this paper shows TDA works on entailment improving the task of classification for establishing entailment on the COLIEE 2018 task by over 5% (F-measure) compared to the results classification without topology that is using only tf/idf and similarity. Furthermore, this result does not assume the existence of the implicit skeleton connecting consecutive sentences (as was done in Doshi and Zadrozny (2018), following Zhu (2013)).…”

Section: Our Resultsmentioning

confidence: 97%

“…This can be seen as two sets of open problems: (a) we do not know exactly the correspondence between text and homological features; (b) we do not have instruments to capture these relationships. We understand these relationship on some the abstract, mathematical level, even for text; in Zhu (2013) and Doshi and Zadrozny (2018) experiments, because of the simple setups, the 1-dimensional persistence measures the tie backs of content words. However, this is less clear for entailment, and we do not have instruments that would allow us to go back from the classifier decision and show the meaning of the topological features in documents we were using.…”

Section: Discussion and Open Problemsmentioning

confidence: 99%

“…that it is not simply measuring repetitions), and also showed that children, adolescent and adult writing styles can be differentiated using TDA. Doshi and Zadrozny (2018) used Zhu's tools and methods to show that topological features can improve the accuracy of classification (movie plots). They also discuss the paucity of applications of TDA to text, and the fact that not all of these applications show improvements over the state of the art: in particular this was the case for sentiment analysis and clustering Michel et al (2017).…”

Section: Related Work On Applying Topological Data Analysis To Discoumentioning

confidence: 99%

“…Interestingly, neither of these works uses topological features to augments the usual tf/idf representations of documents: Doshi and Zadrozny (2018) use counts of words (from a previously identified vocabularies) to form a matrix which is the only input to topological persistence, and then they make a rule based decision based only on the presence of barcodes; and Gholizadeh et al (2018) use time series. To use topological data analysis (TDA), Zhu (2013) assumes that text is implicitly coherent (SIFTS method), and so do Doshi and Zadrozny (2018). Namely, they assume implicit connection between consecutive sentences in each document.…”

Section: Topological Data Analysis For Discourse Semantics? 1 Introdumentioning

confidence: 99%

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Proceedings of the 13th International Conference on Computational Semantics - Student Papers

2019

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Under the standard approach to counterfactuals, to determine the meaning of a counterfactual sentence, we consider the "closest" possible world(s) where the antecedent is true, and evaluate the consequent. Building on the standard approach, some researchers have found that the set of worlds to be considered is dependent on context; it evolves with the discourse. Others have focused on how to define the "distance" between possible worlds, using ideas from causal modeling. This paper integrates the two ideas. We present a semantics for counterfactuals that uses a distance measure based on causal laws, that can also change over time. We show how our semantics can be implemented in the Haskell programming language. In the closest possible worlds in which kangaroos have no tails, they do not use crutches, and do topple over. However, in the closest worlds in which kangaroos both have no tails and use crutches, they do not topple over. The above sequence makes sense. But the next sequence of counterfactuals, with the order of the sentences reversed (a reverse Sobel sequence), is semantically infelicitous: (3) If kangaroos had no tails but used crutches, they would not topple over. #If kangaroos had no tails, they would topple over.

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Section: Our Resultsmentioning

confidence: 97%

Section: Discussion and Open Problemsmentioning

confidence: 99%

Section: Related Work On Applying Topological Data Analysis To Discoumentioning

confidence: 99%

Section: Topological Data Analysis For Discourse Semantics? 1 Introdumentioning

confidence: 99%

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Proceedings of the 13th International Conference on Computational Semantics - Student Papers

2019

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“…The difference in the number of holes was still significant, though the smallest angular threshold for the appearance of holes was not anymore significantly different in the groups. Doshi and Zadrozny in [13] utilized SIFTS for movie genre detection on the IMDB data set of movie plot summaries. The authors showed how persistent homology can significantly improve the classification results and concluded that it TDA can be a reliable tool for discourse classification.…”

Section: Related Workmentioning

confidence: 99%

Topological Signature of 19th Century Novelists: Persistent Homology in Text Mining

Gholizadeh¹,

Seyeditabari²,

Zadrozny³

2018

BDCC

Self Cite

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Topological Data Analysis (TDA) refers to a collection of methods that find the structure of shapes in data. Although recently, TDA methods have been used in many areas of data mining, it has not been widely applied to text mining tasks. In most text processing algorithms, the order in which different entities appear or co-appear is being lost. Assuming these lost orders are informative features of the data, TDA may play a significant role in the resulted gap on text processing state of the art. Once provided, the topology of different entities through a textual document may reveal some additive information regarding the document that is not reflected in any other features from conventional text processing methods. In this paper, we introduce a novel approach that hires TDA in text processing in order to capture and use the topology of different same-type entities in textual documents. First, we will show how to extract some topological signatures in the text using persistent homology-i.e., a TDA tool that captures topological signature of data cloud. Then we will show how to utilize these signatures for text classification.

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A multi-label movie genre classification scheme based on the movie’s subtitles

Rajput

Grover

2022

Multimed Tools Appl

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Movie Genre Detection Using Topological Data Analysis

Cited by 20 publications

References 15 publications

Proceedings of the 13th International Conference on Computational Semantics - Student Papers

Proceedings of the 13th International Conference on Computational Semantics - Student Papers

Topological Signature of 19th Century Novelists: Persistent Homology in Text Mining

A multi-label movie genre classification scheme based on the movie’s subtitles

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