Automatic Language Identification for Metadata Records: Measuring the Effectiveness of Various Approaches Page: 65
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Table 10
Misclassification of all highest-scoring methods in each approach
Language: Approach:
N-Gram Freq N-Gram Freq Profile- Vector-Space Model Naive- Python's
Profile (500) Not Padded with (Pentagrams) Bayes Trigram LID
Blanks (500)
Bulgarian 100% 100% 100% 100% 100%
Czech 33.33% 0% 33.33% 33.33% 66.6%
Danish 66.66% 66.66% 66.66% 66.66% 66.66%
Dutch 100% 100% 100% 100% 100%
English 66.6% 100% 66.66% 66.66% 66.66%
Estonian 50% 75% 100% 75% 0%
Finnish 66.6% 100% 100% 100% 100%
French 100% 100% 62.50% 100% 87.5%
German 80% 100% 80% 80% 80%
Greek 100% 100% 100% 100% 100%
Hungarian 100% 100% 66.66% 100% 100%
Italian 66.66% 100% 66.66% 66.66% 66.66%
Latvian 66.66% 100% 0% 100% 66.66%
Lithuanian 100% 33.33% 66.66% 66.66% 0%
Polish 100% 100% 100% 66.66% 66.66%
Portuguese 100% 100% 60% 100% 60%
Romanian 66.66% 66.66% 100% 33.33% 66.66%
Slovak 66.66% 66.66% 66.66% 33.33% 33.33%
Slovene 66.66% 66.66% 66.66% 100% 100%
Spanish 60% 80% 20% 60% 40%
Swedish 66.66% 66.66% 33.33% 66.66% 100%
Total Accuracy: 77.77% 83.95% 69.14% 76.54% 69.14%65
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Knudson, Ryan Charles. Automatic Language Identification for Metadata Records: Measuring the Effectiveness of Various Approaches, dissertation, May 2015; Denton, Texas. (https://digital.library.unt.edu/ark:/67531/metadc801895/m1/71/: accessed July 16, 2024), University of North Texas Libraries, UNT Digital Library, https://digital.library.unt.edu; .