Changes between Version 8 and Version 9 of private/NlpInPracticeCourse/NamedEntityRecognition

Timestamp:

Oct 12, 2015, 9:53:28 AM (9 years ago)

Author:

Ales Horak

Comment:

--

private/NlpInPracticeCourse/NamedEntityRecognition

@@ -56 +56 @@
          Totals 0.7814  0.7711  0.7763  994     278     295
 }}} 
-In the output, the first column is the input tokens, the second column is the correct (gold) answers. Observe the differences. Copy the training result to `<YOUR_FILE>`. Try to estimate in how many the model missed an entity, detected incorrectly the boundaries, or classified an entity incorrectly.
+In the output, the first column is the input tokens, the second column is the correct (gold) answers. Observe the differences. Copy the training result to `<YOUR_FILE>`. Try to estimate in how many cases the model missed an entity, detected incorrectly the boundaries, or classified an entity incorrectly.
 10. evaluate the model on `dtest` with only NEs that are not present in the train data: `java -cp stanford-ner.jar edu.stanford.nlp.ie.crf.CRFClassifier -loadClassifier cnec-3class-model.ser.gz -testFile named_ent_dtest_unknown.tsv`. Copy the result to `<YOUR_FILE>`.
 11. test on your own input: `java -mx600m -cp stanford-ner.jar edu.stanford.nlp.ie.crf.CRFClassifier -loadClassifier cnec-3class-model.ser.gz -textFile sample.txt`. Copy the result to `<YOUR_FILE>`.
 
 (optional) 12. try to improve the train data
-suggestions: set useKnownLCWords to false, add gazetteers, remove punctuation, try to change the wordshape (something following the pattern: `dan[12](bio)?(UseLC)?, jenny1(useLC)?, chris[1234](useLC)?, cluster1)` or word shape features (see the documentation). Copy the result to `<YOUR_FILE>`.
+suggestions: set `useKnownLCWords` to false, add gazetteers, remove punctuation, try to change the wordshape (something following the pattern: `dan[12](bio)?(UseLC)?, jenny1(useLC)?, chris[1234](useLC)?, cluster1)` or word shape features (see the documentation). Copy the result to `<YOUR_FILE>`.
 (optional) 13. evaluate the model on dtest, final evaluation on etest