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Argamon, S.; Whitelaw, C.; Chase, P.; Hota, S.R.; Garg, N.; Levitan, S.: Stylistic text classification using functional lexical features (2007)
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- Abstract
- Most text analysis and retrieval work to date has focused on the topic of a text; that is, what it is about. However, a text also contains much useful information in its style, or how it is written. This includes information about its author, its purpose, feelings it is meant to evoke, and more. This article develops a new type of lexical feature for use in stylistic text classification, based on taxonomies of various semantic functions of certain choice words or phrases. We demonstrate the usefulness of such features for the stylistic text classification tasks of determining author identity and nationality, the gender of literary characters, a text's sentiment (positive/ negative evaluation), and the rhetorical character of scientific journal articles. We further show how the use of functional features aids in gaining insight about stylistic differences among different kinds of texts.
- Source
- Journal of the American Society for Information Science and Technology. 58(2007) no.6, S.802-822
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Argamon, S.: Introduction to the special topic section on the computational analysis of style (2006)
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- Abstract
- Welcome to this special topic section on the computational analysis of style, which brings together for the first time diverse research work in this unique and exciting area. This marks the emergence, within the information sciences, of a new research community focused on understanding style as it is expressed in and understood from human artifacts and behaviors of various kinds, including (among others) natural language texts, visual art, music, architecture, cinema, and games. Researchers working on these problems come from backgrounds as diverse as computational linguistics, cognitive science, computer graphics/vision, architecture, computer music, decision theory, and machine learning. What all of us have in common is an interest in understanding and analyzing style using computational tools. Style-related work in every medium involves the problem of how to formalize some notion of what "style" is, and of developing a modeling language that supports the representation of differing styles; however, due to the widely varying technical requirements of work in different media, little communication has traditionally existed between different "style researchers." The main goal of this special topic section of the Journal of the American Society for Information Science and Technology is to help bridge this gap.
- Source
- Journal of the American Society for Information Science and Technology. 57(2006) no.11, S.1503-1505
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Koppel, M.; Schler, J.; Argamon, S.: Computational methods in authorship attribution (2009)
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- Abstract
- Statistical authorship attribution has a long history, culminating in the use of modern machine learning classification methods. Nevertheless, most of this work suffers from the limitation of assuming a small closed set of candidate authors and essentially unlimited training text for each. Real-life authorship attribution problems, however, typically fall short of this ideal. Thus, following detailed discussion of previous work, three scenarios are considered here for which solutions to the basic attribution problem are inadequate. In the first variant, the profiling problem, there is no candidate set at all; in this case, the challenge is to provide as much demographic or psychological information as possible about the author. In the second variant, the needle-in-a-haystack problem, there are many thousands of candidates for each of whom we might have a very limited writing sample. In the third variant, the verification problem, there is no closed candidate set but there is one suspect; in this case, the challenge is to determine if the suspect is or is not the author. For each variant, it is shown how machine learning methods can be adapted to handle the special challenges of that variant.
- Source
- Journal of the American Society for Information Science and Technology. 60(2009) no.1, S.9-26
