Classifying Written Texts Through Rhythmic Features

Mihaela Balint; Stefan Trausan-Matu; Mihai Dascalu

doi:10.1007/978-3-319-44748-3_12

Classifying Written Texts Through Rhythmic Features

Mihaela Balint, Stefan Trausan-Matu, Mihai Dascalu

Research output: Chapter in Book/Report/Conference proceeding › Conference Article in proceeding › Academic › peer-review

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Abstract

Rhythm analysis of written texts focuses on literary analysis and it mainly considers poetry. In this paper we investigate the relevance of rhythmic features for categorizing texts in prosaic form pertaining to different genres. Our contribution is threefold. First, we define a set of rhythmic features for written texts. Second, we extract these features from three corpora, of speeches, essays, and newspaper articles. Third, we perform feature selection by means of statistical analyses, and determine a subset of features which efficiently discriminates between the three genres. We find that using as little as eight rhythmic features, documents can be adequately assigned to a given genre with an accuracy of around 80 %, significantly higher than the 33 % baseline which results from random assignment.

Original language	English
Title of host publication	Artificial Intelligence: Methodology, Systems, and Applications. AIMSA 2016
Editors	C. Dichev, G. Agre
Publisher	Springer
Pages	121-129
ISBN (Electronic)	978-3-319-44748-3
ISBN (Print)	978-3-319-44747-6
DOIs	https://doi.org/10.1007/978-3-319-44748-3_12
Publication status	Published - 18 Aug 2016
Externally published	Yes
Event	International Conference on Artificial Intelligence: Methodology, Systems, and Applications: Artificial Intelligence: Methodology, Systems, and Applications - Varna, Bulgaria Duration: 7 Sept 2016 → 10 Sept 2016 https://link.springer.com/book/10.1007/978-3-319-44748-3 https://www.springer.com/la/book/9783319447476

Publication series

Series	Lecture Notes in Computer Science (LNCS)
Volume	9883

Series	Lecture Notes in Artificial Intelligence (subseries)
Volume	9883

Conference

Conference	International Conference on Artificial Intelligence: Methodology, Systems, and Applications
Abbreviated title	AIMSA 2016
Country/Territory	Bulgaria
City	Varna
Period	7/09/16 → 10/09/16
Internet address	https://link.springer.com/book/10.1007/978-3-319-44748-3 https://www.springer.com/la/book/9783319447476

Keywords

rhythm
text classification
natural language processing
discourse analysis

Access to Document

10.1007/978-3-319-44748-3_12

balint2016Submitted manuscript, 348 KBLicence: CC BY-NC

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Westera, W. (PI), Georgiadis, K. (CoI), Saveski, G. (CoI), van Lankveld, G. (CoI), Bahreini, K. (CoI), van der Vegt, W. (CoI), Berkhout, J. (CoI), Nyamsuren, E. (CoI), Kluijfhout, E. (CoI) & Nadolski, R. (CoI)
European Union
1/02/15 → 31/07/19
Project: Research

Cite this

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title = "Classifying Written Texts Through Rhythmic Features",

abstract = "Rhythm analysis of written texts focuses on literary analysis and it mainly considers poetry. In this paper we investigate the relevance of rhythmic features for categorizing texts in prosaic form pertaining to different genres. Our contribution is threefold. First, we define a set of rhythmic features for written texts. Second, we extract these features from three corpora, of speeches, essays, and newspaper articles. Third, we perform feature selection by means of statistical analyses, and determine a subset of features which efficiently discriminates between the three genres. We find that using as little as eight rhythmic features, documents can be adequately assigned to a given genre with an accuracy of around 80 %, significantly higher than the 33 % baseline which results from random assignment.",

keywords = "rhythm, text classification, natural language processing, discourse analysis",

author = "Mihaela Balint and Stefan Trausan-Matu and Mihai Dascalu",

year = "2016",

month = aug,

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doi = "10.1007/978-3-319-44748-3_12",

language = "English",

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series = "Lecture Notes in Computer Science (LNCS)",

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editor = "C. Dichev and G. Agre",

booktitle = "Artificial Intelligence: Methodology, Systems, and Applications. AIMSA 2016",

note = "International Conference on Artificial Intelligence: Methodology, Systems, and Applications : Artificial Intelligence: Methodology, Systems, and Applications, AIMSA 2016 ; Conference date: 07-09-2016 Through 10-09-2016",

url = "https://link.springer.com/book/10.1007/978-3-319-44748-3, https://www.springer.com/la/book/9783319447476",

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Balint, M, Trausan-Matu, S & Dascalu, M 2016, Classifying Written Texts Through Rhythmic Features. in C Dichev & G Agre (eds), Artificial Intelligence: Methodology, Systems, and Applications. AIMSA 2016. Springer, Lecture Notes in Computer Science (LNCS), vol. 9883, Lecture Notes in Artificial Intelligence (subseries), vol. 9883, pp. 121-129, International Conference on Artificial Intelligence: Methodology, Systems, and Applications, Varna, Bulgaria, 7/09/16. https://doi.org/10.1007/978-3-319-44748-3_12

Classifying Written Texts Through Rhythmic Features. / Balint, Mihaela; Trausan-Matu, Stefan; Dascalu, Mihai.
Artificial Intelligence: Methodology, Systems, and Applications. AIMSA 2016. ed. / C. Dichev; G. Agre. Springer, 2016. p. 121-129 (Lecture Notes in Computer Science (LNCS), Vol. 9883). (Lecture Notes in Artificial Intelligence (subseries), Vol. 9883).

Research output: Chapter in Book/Report/Conference proceeding › Conference Article in proceeding › Academic › peer-review

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T1 - Classifying Written Texts Through Rhythmic Features

AU - Balint, Mihaela

AU - Trausan-Matu, Stefan

AU - Dascalu, Mihai

PY - 2016/8/18

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N2 - Rhythm analysis of written texts focuses on literary analysis and it mainly considers poetry. In this paper we investigate the relevance of rhythmic features for categorizing texts in prosaic form pertaining to different genres. Our contribution is threefold. First, we define a set of rhythmic features for written texts. Second, we extract these features from three corpora, of speeches, essays, and newspaper articles. Third, we perform feature selection by means of statistical analyses, and determine a subset of features which efficiently discriminates between the three genres. We find that using as little as eight rhythmic features, documents can be adequately assigned to a given genre with an accuracy of around 80 %, significantly higher than the 33 % baseline which results from random assignment.

AB - Rhythm analysis of written texts focuses on literary analysis and it mainly considers poetry. In this paper we investigate the relevance of rhythmic features for categorizing texts in prosaic form pertaining to different genres. Our contribution is threefold. First, we define a set of rhythmic features for written texts. Second, we extract these features from three corpora, of speeches, essays, and newspaper articles. Third, we perform feature selection by means of statistical analyses, and determine a subset of features which efficiently discriminates between the three genres. We find that using as little as eight rhythmic features, documents can be adequately assigned to a given genre with an accuracy of around 80 %, significantly higher than the 33 % baseline which results from random assignment.

KW - rhythm

KW - text classification

KW - natural language processing

KW - discourse analysis

U2 - 10.1007/978-3-319-44748-3_12

DO - 10.1007/978-3-319-44748-3_12

M3 - Conference Article in proceeding

SN - 978-3-319-44747-6

T3 - Lecture Notes in Computer Science (LNCS)

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BT - Artificial Intelligence: Methodology, Systems, and Applications. AIMSA 2016

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Balint M, Trausan-Matu S, Dascalu M. Classifying Written Texts Through Rhythmic Features. In Dichev C, Agre G, editors, Artificial Intelligence: Methodology, Systems, and Applications. AIMSA 2016. Springer. 2016. p. 121-129. (Lecture Notes in Computer Science (LNCS), Vol. 9883). (Lecture Notes in Artificial Intelligence (subseries), Vol. 9883). doi: 10.1007/978-3-319-44748-3_12

Classifying Written Texts Through Rhythmic Features

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Keywords

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Cite this