![](bg5.png)
Figure 3: The experimental specificity result.
Figure 4: The experimental exhaustivity result.
Figure 4 show the experimental result of the
exhaustivity. The two curves displayed shows the
LR+ values by comparing our test outcome with to
the outcome of the two bases before mentioned
(wikipedia and imdb) for 60 films. We obtain a high
degree of exhaustivity (the most values are between
10 and 25).
When we examine these measures calculated on
the evaluation corpus, we note that the experimental
results are interesting) by comparing the correct
description rate of our test outcome (~83%) with the
correct description rate to the work of (Stanislas, O.
et al., 2010) (80%).
5 CONCLUSIONS
This paper presents an automatic audiovisual
documents genre description. The objective of this
approach is to overcome the semantic analysis gap
in order to extract the audiovisual genre. In this
context, we use the pre-production documents to
combine the statistical analysis and the semantic
analysis. Our statistical analysis is based on the TF-
IDF. We propose adapting this metric to the
semantic lexical database WordNet. In addition, we
exploit four semantic similarity measures to estimate
the proximity between terms and genres. In our
future work, we plan to explore the structuring and
the homogenization of the descriptions by XML
descriptors (metadata) integrated in the MPEG7
standard.
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