Genre as noise: noise in genre

Abstract

Given a specific information need, documents of the wrong genre can be considered as noise. From this perspective, genre classification helps to separate relevant documents from noise. Orthographic errors represent a second, finer notion of noise. Since specific genres often include documents with many errors, an interesting question is whether this "micro-noise" can help to classify genre. In this paper we consider both problems. After introducing a comprehensive hierarchy of genres, we present an intuitive method to build specialized and distinctive classifiers that also work for very small training corpora. We then investigate the correlation between genre and micro noise. Using special error dictionaries, we estimate the typical error rates for each genre. We finally test if the error rate of a document represents a useful feature for genre classification.

Bringing genre into focus: Practical aspects of automatic genre classification

“…In addition to these dynamic elements of the genre palette itself, available user data should be exploited to improve classification performance. We proposed a learning algorithm that employs user behavior in a feedback loop to improve classification performance [3]. Several levels of cooperativeness were distinguished and led to different perspectives in the utilization of available user data.…”

Open-set web genre identification

“…. 2.5 Topic-neutral features to represent genres (Finn and Kushmerick, 2006) (Stubbe, Ringlstetter, and Schulz, 2007) 2.7 Popular science sub-genres description (Lieungnapar, Todd, and Trakulkasemsuk, 2017) Text mining roughly concerns knowledge discovery in texts, i.e. the process where Information Retrieval (IR), Natural Language Processing (NLP), and Machine Learning (ML) methods are used for extracting high-level information from texts.…”

“…The second option is to adopt the open-set classification setting where it is possible for some web pages not to be classified into any of the predefined genre categories (Stubbe, Ringlstetter, and Schulz, 2007;Pritsos and Stamatatos, 2013). This setup avoids the problem of class imbalance caused by numerous noisy pages and also avoids the problem of handling a diverse and highly heterogeneous class.…”

“…Among the few attempts to follow open-set classification in WGI, very few use pure open-set classifiers (Stubbe, Ringlstetter, and Schulz, 2007;Asheghi, 2015). An additional issue is how to handle the test web-pages belonging to unknown genres.…”

“…A more concrete open-set classification models for WGI is presented in (Stubbe, Ringlstetter, and Schulz, 2007). They use the idea of mono-classification for the WGI task, building a genre-specific classifier following a cascading sequence of genrespecific rules.…”