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Text Classification by Language Function

verfasst von
Henning Wachsmuth, Kathrin Bujna

The term “genre” covers different aspects of both texts and documents, and it has led to many classification schemes. This makes different approaches to genre identification incomparable and the task itself unclear. We introduce the linguistically motivated text classification task language function analysis, LFA, which focuses on one well-defined aspect of genres. The aim of LFA is to determine whether a text is predominantly expressive, appellative, or informative. LFA can be used in search and mining applications to efficiently filter documents of interest. Our approach to LFA relies on fast machine learning classifiers with features from different research areas. We evaluate this approach on a new corpus with 4,806 product texts from two domains. Within one domain, we correctly classify up to 82% of the texts, but differences in feature distribution limit accuracy on out-of-domain data.

Externe Organisation(en)
Universität Paderborn
Aufsatz in Konferenzband
Anzahl der Seiten
ASJC Scopus Sachgebiete
Sprache und Linguistik, Artificial intelligence, Software, Linguistik und Sprache
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