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#nowplaying Madonna: a large-scale evaluation on estimating similarities between music artists and between movies from microblogs.

Schedl M - Inf Retr Boston (2012)

Bottom Line: For the music collections, we present results of genre classification experiments using as benchmark genre information from allmusic.com.For the movie collection, we present results of multi-class classification experiments using as benchmark categories from IMDb.We further compare the results to those obtained when using Web pages as data source.

View Article: PubMed Central - PubMed

Affiliation: Department of Computational Perception, Johannes Kepler University, Altenberger Straße 69, 4040 Linz, Austria.

ABSTRACT
Different term weighting techniques such as [Formula: see text] or BM25 have been used intensely for manifold text-based information retrieval tasks. Their use for modeling term profiles for named entities and subsequent calculation of similarities between these named entities have been studied to a much smaller extent. The recent trend of microblogging made available massive amounts of information about almost every topic around the world. Therefore, microblogs represent a valuable source for text-based named entity modeling. In this paper, we present a systematic and comprehensive evaluation of different term weighting measures, normalization techniques, query schemes, index term sets, and similarity functions for the task of inferring similarities between named entities, based on data extracted from microblog posts. We analyze several thousand combinations of choices for the above mentioned dimensions, which influence the similarity calculation process, and we investigate in which way they impact the quality of the similarity estimates. Evaluation is performed using three real-world data sets: two collections of microblogs related to music artists and one related to movies. For the music collections, we present results of genre classification experiments using as benchmark genre information from allmusic.com. For the movie collection, we present results of multi-class classification experiments using as benchmark categories from IMDb. We show that microblogs can indeed be exploited to model named entity similarity with remarkable accuracy, provided the correct settings for the analyzed aspects are used. We further compare the results to those obtained when using Web pages as data source.

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Distribution of different settings among the top-ranked variants on music set C224a. a Query scheme, b term set, cTF formulation, dIDF formulation, e similarity function, f normalization method
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Fig5: Distribution of different settings among the top-ranked variants on music set C224a. a Query scheme, b term set, cTF formulation, dIDF formulation, e similarity function, f normalization method

Mentions: Figure 4 displays the distribution of each analyzed aspect among all 23,100 experimental setups investigated for set C224a. Figure 5 shows this distribution among the 1,809 top-ranked variants. Figure 6 shows the top-ranked algorithmic choices for artist set C3ka and Fig. 7, eventually, shows this distribution for the movie data set C1km.Fig. 4


#nowplaying Madonna: a large-scale evaluation on estimating similarities between music artists and between movies from microblogs.

Schedl M - Inf Retr Boston (2012)

Distribution of different settings among the top-ranked variants on music set C224a. a Query scheme, b term set, cTF formulation, dIDF formulation, e similarity function, f normalization method
© Copyright Policy
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC4008152&req=5

Fig5: Distribution of different settings among the top-ranked variants on music set C224a. a Query scheme, b term set, cTF formulation, dIDF formulation, e similarity function, f normalization method
Mentions: Figure 4 displays the distribution of each analyzed aspect among all 23,100 experimental setups investigated for set C224a. Figure 5 shows this distribution among the 1,809 top-ranked variants. Figure 6 shows the top-ranked algorithmic choices for artist set C3ka and Fig. 7, eventually, shows this distribution for the movie data set C1km.Fig. 4

Bottom Line: For the music collections, we present results of genre classification experiments using as benchmark genre information from allmusic.com.For the movie collection, we present results of multi-class classification experiments using as benchmark categories from IMDb.We further compare the results to those obtained when using Web pages as data source.

View Article: PubMed Central - PubMed

Affiliation: Department of Computational Perception, Johannes Kepler University, Altenberger Straße 69, 4040 Linz, Austria.

ABSTRACT
Different term weighting techniques such as [Formula: see text] or BM25 have been used intensely for manifold text-based information retrieval tasks. Their use for modeling term profiles for named entities and subsequent calculation of similarities between these named entities have been studied to a much smaller extent. The recent trend of microblogging made available massive amounts of information about almost every topic around the world. Therefore, microblogs represent a valuable source for text-based named entity modeling. In this paper, we present a systematic and comprehensive evaluation of different term weighting measures, normalization techniques, query schemes, index term sets, and similarity functions for the task of inferring similarities between named entities, based on data extracted from microblog posts. We analyze several thousand combinations of choices for the above mentioned dimensions, which influence the similarity calculation process, and we investigate in which way they impact the quality of the similarity estimates. Evaluation is performed using three real-world data sets: two collections of microblogs related to music artists and one related to movies. For the music collections, we present results of genre classification experiments using as benchmark genre information from allmusic.com. For the movie collection, we present results of multi-class classification experiments using as benchmark categories from IMDb. We show that microblogs can indeed be exploited to model named entity similarity with remarkable accuracy, provided the correct settings for the analyzed aspects are used. We further compare the results to those obtained when using Web pages as data source.

No MeSH data available.