On the role of poetic versus nonpoetic features in "kindred" and diachronic poetry attribution
Author attribution studies have demonstrated remarkable success in applying orthographic and lexicographic features of text in a variety of discrimination problems. What might poetic features, such as syllabic stress and mood, contribute? We address this question in the context of two different attr...
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Veröffentlicht in: | Journal of the American Society for Information Science and Technology 2012-11, Vol.63 (11), p.2165-2181 |
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description | Author attribution studies have demonstrated remarkable success in applying orthographic and lexicographic features of text in a variety of discrimination problems. What might poetic features, such as syllabic stress and mood, contribute? We address this question in the context of two different attribution problems: (a) kindred: differentiate Langston Hughes’ early poems from those of kindred poets and (b) diachronic: differentiate Hughes’ early from his later poems. Using a diverse set of 535 generic text features, each categorized as poetic or nonpoetic, correlation‐based greedy forward search ranked the features and a support vector machine classified the poems. A small subset of features (∼10) achieved cross‐validated precision and recall as high as 87%. Poetic features (rhyme patterns particularly) were nearly as effective as nonpoetic in kindred discrimination, but less effective diachronically. In other words, Hughes used both poetic and nonpoetic features in distinctive ways and his use of nonpoetic features evolved systematically while he continued to experiment with poetic features. These findings affirm qualitative studies attesting to structural elements from Black oral tradition and Black folk music (blues) and to the internal consistency of Hughes’ early poetry. |
doi_str_mv | 10.1002/asi.22727 |
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What might poetic features, such as syllabic stress and mood, contribute? We address this question in the context of two different attribution problems: (a) kindred: differentiate Langston Hughes’ early poems from those of kindred poets and (b) diachronic: differentiate Hughes’ early from his later poems. Using a diverse set of 535 generic text features, each categorized as poetic or nonpoetic, correlation‐based greedy forward search ranked the features and a support vector machine classified the poems. A small subset of features (∼10) achieved cross‐validated precision and recall as high as 87%. Poetic features (rhyme patterns particularly) were nearly as effective as nonpoetic in kindred discrimination, but less effective diachronically. In other words, Hughes used both poetic and nonpoetic features in distinctive ways and his use of nonpoetic features evolved systematically while he continued to experiment with poetic features. 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What might poetic features, such as syllabic stress and mood, contribute? We address this question in the context of two different attribution problems: (a) kindred: differentiate Langston Hughes’ early poems from those of kindred poets and (b) diachronic: differentiate Hughes’ early from his later poems. Using a diverse set of 535 generic text features, each categorized as poetic or nonpoetic, correlation‐based greedy forward search ranked the features and a support vector machine classified the poems. A small subset of features (∼10) achieved cross‐validated precision and recall as high as 87%. Poetic features (rhyme patterns particularly) were nearly as effective as nonpoetic in kindred discrimination, but less effective diachronically. In other words, Hughes used both poetic and nonpoetic features in distinctive ways and his use of nonpoetic features evolved systematically while he continued to experiment with poetic features. These findings affirm qualitative studies attesting to structural elements from Black oral tradition and Black folk music (blues) and to the internal consistency of Hughes’ early poetry.</description><subject>Artificial intelligence</subject><subject>Bibliometrics. Scientometrics</subject><subject>Bibliometrics. Scientometrics. Evaluation</subject><subject>Computational linguistics</subject><subject>Correlation analysis</subject><subject>Discrimination</subject><subject>Exact sciences and technology</subject><subject>Feature extraction</subject><subject>Hughes, Langston (1902-1967)</subject><subject>Information and communication sciences</subject><subject>Information science. Documentation</subject><subject>Lexicography</subject><subject>Library and information science. 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General aspects</topic><topic>Machine learning</topic><topic>Natural language processing</topic><topic>Oral Tradition</topic><topic>Orthography</topic><topic>Poetry</topic><topic>Qualitative research</topic><topic>Rhyme</topic><topic>Sciences and techniques of general use</topic><topic>Stress</topic><topic>Structural Elements (Construction)</topic><topic>Structural members</topic><topic>Studies</topic><topic>Support vector machines</topic><toplevel>online_resources</toplevel><creatorcontrib>Fegley, Brent D.</creatorcontrib><creatorcontrib>Torvik, Vetle I.</creatorcontrib><collection>Istex</collection><collection>Pascal-Francis</collection><collection>CrossRef</collection><collection>Computer and Information Systems Abstracts</collection><collection>Technology Research Database</collection><collection>Library & Information Sciences Abstracts (LISA)</collection><collection>Library & Information Science Abstracts (LISA)</collection><collection>ProQuest Computer Science Collection</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>Computer and Information Systems Abstracts Academic</collection><collection>Computer and Information Systems Abstracts Professional</collection><jtitle>Journal of the American Society for Information Science and Technology</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Fegley, Brent D.</au><au>Torvik, Vetle I.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>On the role of poetic versus nonpoetic features in "kindred" and diachronic poetry attribution</atitle><jtitle>Journal of the American Society for Information Science and Technology</jtitle><addtitle>J Am Soc Inf Sci Tec</addtitle><date>2012-11</date><risdate>2012</risdate><volume>63</volume><issue>11</issue><spage>2165</spage><epage>2181</epage><pages>2165-2181</pages><issn>1532-2882</issn><issn>2330-1635</issn><eissn>1532-2890</eissn><eissn>2330-1643</eissn><abstract>Author attribution studies have demonstrated remarkable success in applying orthographic and lexicographic features of text in a variety of discrimination problems. What might poetic features, such as syllabic stress and mood, contribute? We address this question in the context of two different attribution problems: (a) kindred: differentiate Langston Hughes’ early poems from those of kindred poets and (b) diachronic: differentiate Hughes’ early from his later poems. Using a diverse set of 535 generic text features, each categorized as poetic or nonpoetic, correlation‐based greedy forward search ranked the features and a support vector machine classified the poems. A small subset of features (∼10) achieved cross‐validated precision and recall as high as 87%. Poetic features (rhyme patterns particularly) were nearly as effective as nonpoetic in kindred discrimination, but less effective diachronically. In other words, Hughes used both poetic and nonpoetic features in distinctive ways and his use of nonpoetic features evolved systematically while he continued to experiment with poetic features. These findings affirm qualitative studies attesting to structural elements from Black oral tradition and Black folk music (blues) and to the internal consistency of Hughes’ early poetry.</abstract><cop>New York, NY</cop><pub>Blackwell Publishing Ltd</pub><doi>10.1002/asi.22727</doi><tpages>17</tpages></addata></record> |
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subjects | Artificial intelligence Bibliometrics. Scientometrics Bibliometrics. Scientometrics. Evaluation Computational linguistics Correlation analysis Discrimination Exact sciences and technology Feature extraction Hughes, Langston (1902-1967) Information and communication sciences Information science. Documentation Lexicography Library and information science. General aspects Machine learning Natural language processing Oral Tradition Orthography Poetry Qualitative research Rhyme Sciences and techniques of general use Stress Structural Elements (Construction) Structural members Studies Support vector machines |
title | On the role of poetic versus nonpoetic features in "kindred" and diachronic poetry attribution |
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