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Welcome!
Our group is involved in research at the intersection of machine learning and computational linguistics. A focus of ours is work on extracting and using non-referential semantic properties of text, such as rhetorical organization or style. We are particularly interested in examining how machine learning techniques can help to capture large-scale linguistic properties of texts that capture levels of meaning above those available at the word or clause level.Much of our research is on Computational Stylistics, in which we devise computational methods for capturing aspects of linguistic style variation, such as that between different individuals or groups of people.
Current projects include:
- Multiclass classifier techniques and feature extraction for authorship attribution of small messages.
- Individual and contextual factors in stylistic variation in 19th century fiction.
- Automated analysis of rhetorical variation in science writing in different fields.
- Language style as a predictor of personality type.
- Analyzing handwriting style by combining image analysis and linguistic stylistics.
- Coarse-scale discourse structure as a stylistic classification indicator.
- Clustering techniques for high-accuracy coreference chain extraction.
- Shallow parsing of systemic-functional linguistic structures.
- Clause chunking and named-entity recognition.
- Automated feature reduction techniques combining wrapped induction and higher-order feature statistics.
- Unsupervised Morphological Analysis
- Designing a corpus and standard tasks for authorship attribution
maintained by Sterling Stuart Stein.
If there are any problems or questions, please contact me.
