Extracting semantic information from an on-line Carnatic music forum
Publication Type:
Conference PaperSource:
Int. Soc. for Music Information Retrieval Conf. (ISMIR), Porto, Portugal, p.355-360 (2012)URL:
http://ismir2012.ismir.net/event/papers/355-ismir-2012.pdfAbstract:
By mining user-generated text content we can obtain music-related information that could not otherwise be extracted from audio signals or symbolic score representations. In this paper we propose a methodology for extracting musically-relevant semantic information from an online discussion forum, rasikas.org, dedicated to the Carnatic music tradition. For that we define a dictionary of relevant terms such as raagas, taalas, performers, composers, and instruments, and create a complex network representation by matching such dictionary against the forum posts. This network representation is used to identify popular terms within the forum, as well as relevant co-occurrences and semantic relationships. This way, for instance, we are able to guess the instrument of a performer with 95% accuracy, to discover the confusion between two raagas with different naming conventions, or to infer semantic relationships regarding lineage or musical influence. This contribution is a first step towards the creation of ontologies for a culture-specific art music tradition.
PDFFile:
An Evolutionary Optimization Approach for Categorical Data Protection
Publication Type:
Conference PaperSource:
Privacy and Anonymity in the Information Society 2012, Berlin (2012)Keywords:
genetic algorithms; data privacy; categorical data; data mining; information loss; disclosure riskAbstract:
The continuous growing amount of public sensible data has increased the risk of breaking the privacy of people or institutions in those datasets. Many protection methods have been developed to solve this problem by either distorting or generalizing data but taking into account the difficult trade-off between data utility (information loss) and protection against disclosure (disclosure risk).
In this paper we present an optimization approach for data protection based on an evolutionary algorithm which is guided by a combination of information loss and disclosure risk measures. In this way, state-of-the-art protection methods are combined to obtain new data protections with a better trade-off between these two measures. The paper presents several experimental results that assess the performance of our approach.
