Comparison of Collocation Extraction Measures for Document Indexing

Automatic extraction of collocations from a corpus is a well-known problem in the field of natural language processing. It is typically carried out by employing some kind of a statistical measure that indicates whether or not two words occur together more often than by chance. As there is an aboundance of these measures proposed by various authors, we have compared some of them on a task of extracting collocations from a corpus of Croatian legal documents for the purpose of document indexing. We propose and evaluate extensions of these measures for collocations consisting of three words

ArguLens: Anatomy of Community Opinions On Usability Issues Using Argumentation Models

In open-source software (OSS), the design of usability is often influenced by the discussions among community members on platforms such as issue tracking systems (ITSs). However, digesting the rich information embedded in issue discussions can be a major challenge due to the vast number and diversity of the comments. We propose and evaluate ArguLens, a conceptual framework and automated technique leveraging an argumentation model to support effective understanding and consolidation of community opinions in ITSs. Through content analysis, we anatomized highly discussed usability issues from a large, active OSS project, into their argumentation components and standpoints. We then experimented with supervised machine learning techniques for automated argument extraction. Finally, through a study with experienced ITS users, we show that the information provided by ArguLens supported the digestion of usability-related opinions and facilitated the review of lengthy issues. ArguLens provides the direction of designing valuable tools for high-level reasoning and effective discussion about usability