Difference between revisions of "User:Nfeldba1/Final project"

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==The Project==
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For my final project, I expanded my transducer until it reached 85% coverage, which involved adding words, disambiguations, and extra suffixes.
  
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My code can be found here: https://github.com/nfeldbaum/Khasi_Transducer
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==The Results==
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I managed to reach 85.15% coverage on my initial 50,000 word corpus! However, I ended up adding a significant number of English words that were present to reach this amount. A truer measure of corpus coverage is the 80.50% coverage I get when I delete all the words that aren't part of Khasi.
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Instead of testing precision and recall against hand-annotated randomly selected forms, I decided to gather a further 25,000 words in order to test my transducer on a corpus it hadn't trained on. This new corpus can be found in the repository linked above under ling073-kha-corpus/kha.corpus.large.test.txt. On this test corpus, I achieved 83.39% coverage with English words included, and 80.50% coverage without English words - exactly the same as on my training corpus.

Revision as of 13:20, 11 May 2017

The Project

For my final project, I expanded my transducer until it reached 85% coverage, which involved adding words, disambiguations, and extra suffixes.

My code can be found here: https://github.com/nfeldbaum/Khasi_Transducer

The Results

I managed to reach 85.15% coverage on my initial 50,000 word corpus! However, I ended up adding a significant number of English words that were present to reach this amount. A truer measure of corpus coverage is the 80.50% coverage I get when I delete all the words that aren't part of Khasi.

Instead of testing precision and recall against hand-annotated randomly selected forms, I decided to gather a further 25,000 words in order to test my transducer on a corpus it hadn't trained on. This new corpus can be found in the repository linked above under ling073-kha-corpus/kha.corpus.large.test.txt. On this test corpus, I achieved 83.39% coverage with English words included, and 80.50% coverage without English words - exactly the same as on my training corpus.