Stylistic Experiments for Information Retrieval

Information retrieval systems are built to handle texts as topical items: texts are tabulated by occurrence frequencies of content words in them, under the assumption that text topic is reasonably well modeled by content word occurrence. But texts have several interesting characteristics beyond topic. The experiments described in this text investigate {\em stylistic variation}. Roughly put, style is the difference between two ways of saying the same thing --- and systematic stylistic variation can be used to characterize the {\em genre} of documents. These experiments investigate if stylistic information is distinguishable using simple language engineering methods, and if in that case this type of information can be used to improve information retrieval systems. A first set of experiments shows that simple measures of stylistic variation can be used to distinguish genres from each other quite adequately; how well depends on what the genres in question are. A second set of experiments evaluates the utility of stylistic measures for the purposes of information retrieval, to identify common characteristics of relevant and non-relevant documents. The conclusion is that the requests for information as typically expressed to retrieval systems are too terse and inspecific for non-topical information to improve retrieval results. Systems for information access need to be designed from the beginning to handle richer information about the texts and documents at hand: information about stylistic variation cannot easily be added to an existing system. A third set of experiments explores how an interactive system can be designed to incorporate stylistic information in the interface between user and system. These experiments resulted in the design an interface for categorizing retrieval results by genre, and displaying the retrieval results using this categorization. This interface is integrated into a prototype for retrieving information from the World Wide Web

Stylistic Variation in an Information Retrieval Experiment

Texts exhibit considerable stylistic variation. This paper reports an experiment where a corpus of documents (N= 75 000) is analyzed using various simple stylistic metrics. A subset (n = 1000) of the corpus has been previously assessed to be relevant for answering given information retrieval queries. The experiment shows that this subset differs significantly from the rest of the corpus in terms of the stylistic metrics studied.Comment: Proceedings of NEMLAP-

Feature Type Analysis in Automated Genre Classification

In this paper, we compare classifiers based on language model, image, and stylistic features for automated genre classification. The majority of previous studies in genre classification have created models based on an amalgamated representation of a document using a multitude of features. In these models, the inseparable roles of different features make it difficult to determine a means of improving the classifier when it exhibits poor performance in detecting selected genres. By independently modeling and comparing classifiers based on features belonging to three types, describing visual, stylistic, and topical properties, we demonstrate that different genres have distinctive feature strengths.

Towards a style-specific basis for computational beat tracking

Outlined in this paper are a number of sources of evidence, from psychological, ethnomusicological and engineering grounds, to suggest that current approaches to computational beat tracking are incomplete. It is contended that the degree to which cultural knowledge, that is, the specifics of style and associated learnt representational schema, underlie the human faculty of beat tracking has been severely underestimated. Difficulties in building general beat tracking solutions, which can provide both period and phase locking across a large corpus of styles, are highlighted. It is probable that no universal beat tracking model exists which does not utilise a switching model to recognise style and context prior to application

Timbre-invariant Audio Features for Style Analysis of Classical Music

Copyright: (c) 2014 Christof Weiß et al. This is an open-access article distributed under the terms of the Creative Commons Attribution 3.0 Unported License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited

People on Drugs: Credibility of User Statements in Health Communities

Online health communities are a valuable source of information for patients and physicians. However, such user-generated resources are often plagued by inaccuracies and misinformation. In this work we propose a method for automatically establishing the credibility of user-generated medical statements and the trustworthiness of their authors by exploiting linguistic cues and distant supervision from expert sources. To this end we introduce a probabilistic graphical model that jointly learns user trustworthiness, statement credibility, and language objectivity. We apply this methodology to the task of extracting rare or unknown side-effects of medical drugs --- this being one of the problems where large scale non-expert data has the potential to complement expert medical knowledge. We show that our method can reliably extract side-effects and filter out false statements, while identifying trustworthy users that are likely to contribute valuable medical information