WSD issues resulting in bad lemmatization/PoS tag sequence about corenlp HOT 8 OPEN

I'd like to push back a little on the idea that number_RBR is a common usage. Of the 1053 usages of number in our training data, 1052 are number_NN, and the last one is Revolution Number_NNP 9, which I believe should also be nn based on the latest tagging guidelines. Also, it should be JJR, not RBR, right? In your example, his right side is numb_JJ is clearly an adjective.

It's hard to even come up with examples that make sense. Nevertheless, on the walk to work I came up with a few. If we add these to the training data, the models might pick up that number is sometimes an adjective... but it's going to be drowning in over 1000 examples of nouns, so I'm not sure it will make much difference.

As my arthritis gets worse, my thigh gets number_JJR
Cocaine makes my lips number_JJR than meth
The only time I felt number_JJR was when I rubbed one out three times in a row

proposed parses for these:

( (S
   (SBAR
     (IN As)
     (S
       (NP (PRP$ my) (NN arthritis))
       (VP
         (VBZ gets)
         (ADJP (JJR worse)))))
   (, ,)
   (NP (PRP$ my) (NN thigh))
   (VP
     (VBZ gets)
     (ADJP (JJR number)))))

( (S
   (NP (NN Cocaine))
   (VP
     (VBZ makes)
     (S
       (NP (PRP$ my) (NNS lips))
       (ADJP
         (ADJP (JJR number))
         (PP
           (IN than)
           (NP (NN meth))))))))

( (S
   (NP
     (NP (DT The) (JJ only) (NN time))
     (SBAR
       (S
         (NP (PRP I))
         (VP
           (VBD felt)
           (ADJP (JJR number))))))
   (VP
     (VBD was)
     (SBAR
       (WHADVP (WRB when))
       (S
         (NP (PRP I))
         (VP
           (VBD rubbed)
           (NP (NN one))
           (PRT (RP out))
           (NP
             (NP (CD three) (NNS times))
             (PP
               (IN in)
               (NP (DT a) (NN row))))))))))

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also, as a followup, the CoreNLP lemmatizer already properly handles number_JJR

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First, thanks so much for the speedy and thoughtful reply!

Second, I think you are totally right that it should be JJR not RBR (that was my mistake, apologies), and that is indeed quite a difference in statistical distribution within the training data, so I'm not surprised that the tagger struggles. However, for whatever reason, I am seeing different behavior from you. On corenlp.run, for example, it's definitely not labeling things as JJR and is lemmatizing as "number":

And running locally I'm getting the same result.

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Aha! I understand, thanks for the clarification. Those proposed parses look fine to me; I agree that it's not likely to overcome that degree of word-sense imbalance in the data but it certainly can't hurt to include a few bonus examples for the PoS tagger.

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Oh and also, now I'm confused by your comment "also, as a followup, the CoreNLP lemmatizer already properly handles number_JJR" - as best as I can tell, it definitely is not handling that scenario, and is lemmatizing it to "number_NN".

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also, as a followup, the CoreNLP lemmatizer already properly handles number_JJR

If by chance you give the lemmatizer number with the tag JJR, it returns the lemma numb. I used it to convert those trees to a UD representation in the commit I made above, for example.

stanfordnlp/handparsed-treebank@c1a405b

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