Comments (4)
GoogleCodeExporter
commented on June 25, 2024
Yes, should work fine.
Original comment by [email protected] on 18 Jul 2013 at 2:48
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GoogleCodeExporter
commented on June 25, 2024
Original comment by [email protected] on 18 Jul 2013 at 2:49
- Changed state: Invalid
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GoogleCodeExporter
commented on June 25, 2024
I get some behavior I am confused about. For instance, certain topics with very
small vocabularies give very high log probs for sentences of words they have
never seen before, higher than for language models that have seen them.
Original comment by [email protected] on 18 Jul 2013 at 3:00
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GoogleCodeExporter
commented on June 25, 2024
Could you give me a concrete example?
Original comment by [email protected] on 18 Jul 2013 at 3:29
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Related Issues (20)
- ArrayIndexOutOfBoundsException while calling getLogProb HOT 8
- Runtime exception: Hash map is full with 100 keys. Should never happen. HOT 16
- What's the log base of NgramLanguageModel.getLogProb() ? HOT 5
- Can I feed this library raw counts instead of text files, and have it compute the Kneser Ney probabilities for me? HOT 1
- ArrayOutOfBoundsException when reading in a large ARPA file. HOT 7
- Cannot train unigram model with Kneser-Ney HOT 2
- Unrealistic perplexity HOT 3
- broken link on http://tomato.banatao.berkeley.edu:8080/berkeleylm_binaries/ HOT 1
- StarPos and EndPos for ngram log probability HOT 3
- Getting NAN on last trigram when using google binary HOT 1
- Trying to build a language model on higher-order n-grams. HOT 3
- Frequency Map HOT 1
- Calculating log probability over larger document
- Unknown Values
- Old mvn file
- no start token with LmReaders.readNgramMapFromBinary?
- no license information
- Documentation, usage, etc.
- creating and reading arpa files is 1. locale dependant 2. seems to have problems with multiple tabs in the text 3. seems to have some problem with the lack of newlines HOT 3
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