An integrated approach to discover tag semantics

Tag-based systems have become very common for online classification thanks to their intrinsic advantages such as self-organization and rapid evolution. However, they are still affected by some issues that limit their utility, mainly due to the inherent ambiguity in the semantics of tags. Synonyms, homonyms, and polysemous words, while not harmful for the casual user, strongly affect the quality of search results and the performances of tag-based recommendation systems. In this paper we rely on the concept of tag relatedness in order to study small groups of similar tags and detect relationships between them. This approach is grounded on a model that builds upon an edge-colored multigraph of users, tags, and resources. To put our thoughts in practice, we present a modular and extensible framework of analysis for discovering synonyms, homonyms and hierarchical relationships amongst sets of tags. Some initial results of its application to the delicious database are presented, showing that such an approach could be useful to solve some of the well known problems of folksonomies

Learning Unsupervised Hierarchies of Audio Concepts

Music signals are difficult to interpret from their low-level features, perhaps even more than images: e.g. highlighting part of a spectrogram or an image is often insufficient to convey high-level ideas that are genuinely relevant to humans. In computer vision, concept learning was therein proposed to adjust explanations to the right abstraction level (e.g. detect clinical concepts from radiographs). These methods have yet to be used for MIR. In this paper, we adapt concept learning to the realm of music, with its particularities. For instance, music concepts are typically non-independent and of mixed nature (e.g. genre, instruments, mood), unlike previous work that assumed disentangled concepts. We propose a method to learn numerous music concepts from audio and then automatically hierarchise them to expose their mutual relationships. We conduct experiments on datasets of playlists from a music streaming service, serving as a few annotated examples for diverse concepts. Evaluations show that the mined hierarchies are aligned with both ground-truth hierarchies of concepts -- when available -- and with proxy sources of concept similarity in the general case.Comment: ISMIR 202

Classification design : understanding the decisions between theory and consequence

Classification systems are systems of terms and term relationships intended to sort and gather like concepts and documents. These systems are ubiquitous as the substrate of our interactions with library collections, retail websites, and bureaucracies. Through their design and impact, classification systems share with other technologies an unavoidable though often ignored relationship to politics, power, and authority (Fleischmann & Wallace, 2007). Despite concern among scholars that classification systems embody values and bias, there is little work examining how these qualities are built into a classification system. Specifically, we do not adequately understand classification construction, in which classification designers make decisions by applying classification theory to the specific context of a project (Park, 2008). If systems embody values— particularly values that might either cause harm (Berman, 1971) or provide an additional means of communicating the creator’s position (Feinberg, 2007)— we must understand how and when the system takes on these qualities. This dissertation bridges critical classification theory with design-oriented classification theory. Where critical classification theory is concerned with the outcomes of classification system design, design-oriented classification theory is concerned with the correct processes by which to build a classification system. To connect the consequences of classification system design to designers’ methods and intentions, I use the research lens of infrastructure studies, particularly infrastructural inversion (Star & Ruhleder, 1996) or making visible the work behind infrastructures such as classification systems. Accordingly, my research focuses on designers’ decisions and rethinks our assumptions regarding the factors that classification designers consider in making their design decisions. I adopted an ethnographic approach to the study of classification design that would make visible design decisions and designers’ consideration of factors. Using this approach, I studied the daily design work of volunteer classification designers who maintain a curated folksonomy. Using the grounded theory method (Strauss & Corbin, 1998), I analyzed the designers’ decisions. My analysis identified the implications of the designers’ convergences and divergences from established classification methods for the character of the system and for the connection between classification theory and classification methods. I show how the factors—and the prioritization of factors—that these designers considered in making their decisions were consistent with the values and needs of the community. Therefore, I argue that classification designers have an important role in creating the values or bias of a classification system. In particular, designers’ divergence from universal guidelines and designers’ choices among sources of evidence represent opportunities to align a classification system to its community. I recommend that classification research focus on such instances of divergence and choice to understand the connection between classification design and the values of classification systems. The Introduction motivates the problem space around values in classification systems and outlines my approach in focusing on classification design. The Literature Review outlines the dominant theories in classification scholarship according to three elements of classification design: what decisions designers make, what information designers use in their decisions, and what skills designers apply to their decisions. In the Methods chapter, I introduce the site of my ethnographic research (The Fanwork Repository), detail my ethnographic methods, summarize the types of data I collected, and describe my grounded analysis. Three findings chapters examine one type of complex decision each: Names, Works, and Guidelines, respectively. In the fourth findings chapter, Synthesis, I define 10 factors designers considered across these complex design decisions. I then discuss how the factors figured into complex design decisions, how the factors overlapped and conflicted in design decisions, and how designers understood their role in making complex design decisions. In the Discussion chapter I connect the findings from the site of my ethnography to classification scholarship. In the Conclusion, I consider the contribution of examining classification systems as infrastructure, highlight the differences in accounts of classification design decisions made visible through classification theory and infrastructure studies approaches, and present suggestions for future research in classification design and the study of classification systems as infrastructure.Informatio