Besonders erfolgreich für diese Aufgabe sind Methoden maschinellen Lernens, die das Problem als Klassifikationsaufgabe auffassen und anhand von Trainingskorpora typische sprachliche Merkmale der Texte der jeweiligen Autor/innen lernen. Dabei zeigt sich, dass „low-level features like character n-grams are very successful for representing texts for stylistic purposes” (Stamatatos, 2009, S. 24). <?biblio ADDIN ZOTERO_ITEM CSL_CITATION {"citationID":"sxfqPHnj","properties":{"formattedCitation":"(Stamatatos, 2009, S. 24)","plainCitation":"(Stamatatos, 2009, S. 24)"},"citationItems":[{"id":9112,"uris":["http://zotero.org/users/677080/items/BIH6LCQ6"],"uri":["http://zotero.org/users/677080/items/BIH6LCQ6"],"itemData":{"id":9112,"type":"article-journal","title":"A Survey of Modern Authorship Attribution Methods","container-title":"J. Am. Soc. Inf. Sci. Technol.","page":"538–556","volume":"60","issue":"3","source":"ACM Digital Library","abstract":"Authorship attribution supported by statistical or computational methods has a long history starting from the 19th century and is marked by the seminal study of Mosteller and Wallace (1964) on the authorship of the disputed “Federalist Papers.” During the last decade, this scientific field has been developed substantially, taking advantage of research advances in areas such as machine learning, information retrieval, and natural language processing. The plethora of available electronic texts (e.g., e-mail messages, online forum messages, blogs, source code, etc.) indicates a wide variety of applications of this technology, provided it is able to handle short and noisy text from multiple candidate authors. In this article, a survey of recent advances of the automated approaches to attributing authorship is presented, examining their characteristics for both text representation and text classification. The focus of this survey is on computational requirements and settings rather than on linguistic or literary issues. We also discuss evaluation methodologies and criteria for authorship attribution studies and list open questions that will attract future work in this area. © 2009 Wiley Periodicals, Inc.","DOI":"10.1002/asi.v60:3","ISSN":"1532-2882","author":[{"family":"Stamatatos","given":"Efstathios"}],"issued":{"date-parts":[["2009",3]]}},"locator":"24"}],"schema":"https://github.com/citation-style-language/schema/raw/master/csl-citation.json"} ?> Das bedeutet, solche Modelle, die auf der Distribution von Buchstaben-N-Grammen beruhen, sind, gemessen an einem Goldstandard, am erfolgreichsten. Allein: Solche Modelle lassen sich nicht linguistisch deuten, da völlig unklar ist, was sie eigentlich messen. Ist es Stil, Thema, Textsorte, …? Es handelt sich also weder um eine valide, noch um eine deutbare Kategorie (insbesondere, wenn das statistische Modell nicht einsehbar ist). Für spezifische Aufgaben der Autorschaftsattribution mag das ausreichend sein, aber bereits für forensische Anwendungen, beispielsweise vor Gericht, ist eine solche Modellierung fragwürdig und gefährlich. Und für eine linguistische Deutung des Phänomens Autorschaftsstil ist sie gänzlich unbrauchbar.<ref xmlns="http://www.tei-c.org/ns/1.0" target="ftn2" n="2" />