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Example runs for raw and parsed input
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marenger committed Jan 31, 2017
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40 changes: 40 additions & 0 deletions examples/example_run_parsed/test_input.neg
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1 Suddenly Suddenly CPOS RB FEATS 3 advmod ***
2 he he CPOS PRP FEATS 3 nsubj ***
3 turned turn CPOS VBD FEATS 0 root ***
4 upon upon CPOS IN FEATS 5 case ***
5 me me CPOS PRP FEATS 3 nmod:upon ***
6 with with CPOS IN FEATS 9 case ***
7 a a CPOS DT FEATS 9 det ***
8 mischievous mischievous CPOS JJ FEATS 9 amod ***
9 twinkle twinkle CPOS NN FEATS 3 nmod:with ***
10 in in CPOS IN FEATS 12 case ***
11 his his CPOS PRP$ FEATS 12 nmod:poss ***
12 eyes eye CPOS NNS FEATS 9 nmod:in ***
13 . . CPOS . FEATS 3 punct ***

1 No No CPOS DT FEATS 2 neg No _ _
2 woman woman CPOS NN FEATS 5 nsubj _ woman _
3 would would CPOS MD FEATS 5 aux _ would _
4 ever ever CPOS RB FEATS 5 advmod _ ever _
5 send send CPOS VB FEATS 0 root _ send _
6 a a CPOS DT FEATS 8 det _ a _
7 reply-paid reply-paid CPOS JJ FEATS 8 amod _ reply-paid _
8 telegram telegram CPOS NN FEATS 5 dobj _ telegram _
9 . . CPOS . FEATS 5 punct _ _ _

1 After After CPOS IN FEATS 3 case _ _ _ _ _ _
2 his his CPOS PRP$ FEATS 3 nmod:poss _ _ _ _ _ _
3 habit habit CPOS NN FEATS 5 nmod:after _ _ _ _ _ _
4 he he CPOS PRP FEATS 5 nsubj _ he _ _ _ _
5 said say CPOS VBD FEATS 0 root _ said _ _ _ _
6 nothing nothing CPOS NN FEATS 5 dobj nothing _ _ _ _ _
7 , , CPOS , FEATS 5 punct _ _ _ _ _ _
8 and and CPOS CC FEATS 5 cc _ _ _ _ _ _
9 after after CPOS IN FEATS 10 case _ _ _ _ _ _
10 mine mine CPOS NN FEATS 12 nmod:after _ _ _ _ _ _
11 I I CPOS PRP FEATS 12 nsubj _ _ _ _ I _
12 asked ask CPOS VBD FEATS 5 conj:and _ _ _ _ asked _
13 no no CPOS DT FEATS 14 neg _ _ _ no _ _
14 questions question CPOS NNS FEATS 12 dobj _ _ _ _ questions _
15 . . CPOS . FEATS 5 punct _ _ _ _ _ _

40 changes: 40 additions & 0 deletions examples/example_run_parsed/test_input.txt
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1 Suddenly Suddenly CPOS RB FEATS 3 advmod #
2 he he CPOS PRP FEATS 3 nsubj #
3 turned turn CPOS VBD FEATS 0 root #
4 upon upon CPOS IN FEATS 5 case #
5 me me CPOS PRP FEATS 3 nmod:upon #
6 with with CPOS IN FEATS 9 case #
7 a a CPOS DT FEATS 9 det #
8 mischievous mischievous CPOS JJ FEATS 9 amod #
9 twinkle twinkle CPOS NN FEATS 3 nmod:with #
10 in in CPOS IN FEATS 12 case #
11 his his CPOS PRP$ FEATS 12 nmod:poss #
12 eyes eye CPOS NNS FEATS 9 nmod:in #
13 . . CPOS . FEATS 3 punct #

1 No No CPOS DT FEATS 2 neg #
2 woman woman CPOS NN FEATS 5 nsubj #
3 would would CPOS MD FEATS 5 aux #
4 ever ever CPOS RB FEATS 5 advmod #
5 send send CPOS VB FEATS 0 root #
6 a a CPOS DT FEATS 8 det #
7 reply-paid reply-paid CPOS JJ FEATS 8 amod #
8 telegram telegram CPOS NN FEATS 5 dobj #
9 . . CPOS . FEATS 5 punct #

1 After After CPOS IN FEATS 3 case #
2 his his CPOS PRP$ FEATS 3 nmod:poss #
3 habit habit CPOS NN FEATS 5 nmod:after $
4 he he CPOS PRP FEATS 5 nsubj #
5 said say CPOS VBD FEATS 0 root #
6 nothing nothing CPOS NN FEATS 5 dobj #
7 , , CPOS , FEATS 5 punct #
8 and and CPOS CC FEATS 5 cc #
9 after after CPOS IN FEATS 10 case #
10 mine mine CPOS NN FEATS 12 nmod:after #
11 I I CPOS PRP FEATS 12 nsubj #
12 asked ask CPOS VBD FEATS 5 conj:and #
13 no no CPOS DT FEATS 14 neg #
14 questions question CPOS NNS FEATS 12 dobj #
15 . . CPOS . FEATS 5 punct #

1 change: 1 addition & 0 deletions examples/example_run_parsed/test_run_parsed.txt
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Martines-MacBook-Pro:negtool martineenger$ python negtool.py -m parsed -f test_input.txt
157 changes: 157 additions & 0 deletions examples/example_run_raw/test_input_raw.neg
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1 Suddenly suddenly RB _ 3 advmod ***
2 he he PRP _ 3 nsubj ***
3 turned turn VBD _ 0 ROOT ***
4 upon upon IN _ 5 case ***
5 me I PRP _ 3 nmod ***
6 with with IN _ 9 case ***
7 a a DT _ 9 det ***
8 mischievous mischievous JJ _ 9 amod ***
9 twinkle twinkle NN _ 3 nmod ***
10 in in IN _ 12 case ***
11 his he PRP$ _ 12 nmod:poss ***
12 eyes eye NNS _ 9 nmod ***
13 . . . _ 3 punct ***

1 No no DT _ 2 neg No _ _
2 woman woman NN _ 5 nsubj _ woman _
3 would would MD _ 5 aux _ would _
4 ever ever RB _ 5 advmod _ ever _
5 send send VB _ 0 ROOT _ send _
6 a a DT _ 8 det _ a _
7 reply-paid reply-paid JJ _ 8 amod _ reply-paid _
8 telegram telegram NN _ 5 dobj _ telegram _
9 . . . _ 5 punct _ _ _

1 If if IN _ 7 mark _ _ _
2 the the DT _ 3 det _ the _
3 corpus corpus NN _ 7 nsubjpass _ corpus _
4 can can MD _ 7 aux _ can _
5 not not RB _ 7 neg not _ _
6 be be VB _ 7 auxpass _ be _
7 found find VBN _ 14 advcl _ found _
8 , , , _ 14 punct _ _ _
9 then then RB _ 10 advmod _ _ _
10 accessing access VBG _ 14 csubj _ _ _
11 this this DT _ 12 det _ _ _
12 object object NN _ 10 dobj _ _ _
13 will will MD _ 14 aux _ _ _
14 raise raise VB _ 0 ROOT _ _ _
15 an a DT _ 16 det _ _ _
16 exception exception NN _ 14 dobj _ _ _
17 , , , _ 14 punct _ _ _
18 displaying display VBG _ 14 advcl _ _ _
19 installation installation NN _ 20 compound _ _ _
20 instructions instruction NNS _ 18 dobj _ _ _
21 for for IN _ 25 case _ _ _
22 the the DT _ 25 det _ _ _
23 NLTK NLTK NNP _ 25 compound _ _ _
24 data datum NNS _ 25 compound _ _ _
25 package package NN _ 20 nmod _ _ _
26 . . . _ 14 punct _ _ _

1 It it PRP _ 7 nsubj ***
2 is be VBZ _ 7 cop ***
3 , , , _ 7 punct ***
4 however however RB _ 7 advmod ***
5 , , , _ 7 punct ***
6 unfortunately unfortunately RB _ 7 advmod ***
7 impossible impossible JJ _ 0 ROOT ***
8 entirely entirely RB _ 7 advmod ***
9 to to TO _ 10 mark ***
10 separate separate VB _ 7 xcomp ***
11 the the DT _ 12 det ***
12 sentational sentational NN _ 10 dobj ***
13 from from IN _ 15 case ***
14 the the DT _ 15 det ***
15 criminal criminal JJ _ 10 nmod ***
16 . . . _ 7 punct ***

1 It it PRP _ 6 nsubj ***
2 was be VBD _ 6 cop ***
3 a a DT _ 6 det ***
4 blazing blaze VBG _ 6 amod ***
5 hot hot JJ _ 6 amod ***
6 day day NN _ 0 ROOT ***
7 in in IN _ 8 case ***
8 August August NNP _ 6 nmod ***
9 . . . _ 6 punct ***

1 But but CC _ 6 cc ***
2 the the DT _ 4 det ***
3 morning morning NN _ 4 compound ***
4 paper paper NN _ 6 nsubj ***
5 was be VBD _ 6 cop ***
6 uninteresting uninteresting JJ _ 0 ROOT ***
7 . . . _ 6 punct ***

1 Everybody everybody NN _ 5 nsubj ***
2 was be VBD _ 5 cop ***
3 out out IN _ 5 case ***
4 of of IN _ 5 case ***
5 town town NN _ 0 ROOT ***
6 , , , _ 5 punct ***
7 and and CC _ 5 cc ***
8 I I PRP _ 9 nsubj ***
9 yearned yearn VBD _ 5 conj ***
10 for for IN _ 12 case ***
11 the the DT _ 12 det ***
12 glades glade NNS _ 9 nmod ***
13 of of IN _ 16 case ***
14 the the DT _ 16 det ***
15 New New NNP _ 16 compound ***
16 Forest Forest NNP _ 12 nmod ***
17 or or CC _ 16 cc ***
18 the the DT _ 19 det ***
19 shingle shingle NN _ 16 conj ***
20 of of IN _ 21 case ***
21 Southsea Southsea NNP _ 19 nmod ***
22 . . . _ 5 punct ***

1 A a DT _ 4 det _ _ _
2 depleted deplete VBN _ 4 amod _ _ _
3 bank bank NN _ 4 compound _ _ _
4 account account NN _ 6 nsubj _ _ _
5 had have VBD _ 6 aux _ _ _
6 caused cause VBN _ 0 ROOT _ _ _
7 me I PRP _ 6 dobj _ _ _
8 to to TO _ 9 mark _ _ _
9 postpone postpone VB _ 6 xcomp _ _ _
10 my my PRP$ _ 11 nmod:poss _ _ _
11 holiday holiday NN _ 9 dobj _ _ _
12 , , , _ 6 punct _ _ _
13 and and CC _ 6 cc _ _ _
14 as as IN _ 17 case _ _ _
15 to to TO _ 17 case _ _ _
16 my my PRP$ _ 17 nmod:poss _ _ _
17 companion companion NN _ 25 nmod _ _ _
18 , , , _ 17 punct _ _ _
19 neither neither CC _ 21 cc:preconj neither _ _
20 the the DT _ 21 det _ the _
21 country country NN _ 17 conj _ country _
22 nor nor CC _ 17 cc nor _ _
23 the the DT _ 24 det _ the _
24 sea sea NN _ 17 conj _ sea _
25 presented present VBD _ 6 conj _ presented _
26 the the DT _ 28 det _ the _
27 slightest slightest JJS _ 28 amod _ slightest _
28 attraction attraction NN _ 25 dobj _ attraction _
29 to to TO _ 30 case _ to _
30 him he PRP _ 25 nmod _ him _
31 . . . _ 6 punct _ _ _

1 After after IN _ 3 case _ _ _ _ _ _
2 his he PRP$ _ 3 nmod:poss _ _ _ _ _ _
3 habit habit NN _ 5 nmod _ _ _ _ _ _
4 he he PRP _ 5 nsubj _ he _ _ _ _
5 said say VBD _ 0 ROOT _ said _ _ _ _
6 nothing nothing NN _ 5 dobj nothing _ _ _ _ _
7 , , , _ 6 punct _ _ _ _ _ _
8 and and CC _ 6 cc _ _ _ _ _ _
9 after after IN _ 10 case _ _ _ _ _ _
10 mine mine NN _ 12 nmod _ _ _ _ _ _
11 I I PRP _ 12 nsubj _ _ _ _ I _
12 asked ask VBD _ 6 conj _ _ _ _ asked _
13 no no DT _ 14 neg _ _ _ no _ _
14 questions question NNS _ 12 dobj _ _ _ _ questions _
15 . . . _ 5 punct _ _ _ _ _ _

17 changes: 17 additions & 0 deletions examples/example_run_raw/test_input_raw.txt
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Suddenly he turned upon me with a mischievous twinkle in his eyes.

No woman would ever send a reply-paid telegram.

If the corpus can not be found, then accessing this object will raise an exception, displaying installation instructions for the NLTK data package.

It is, however, unfortunately impossible entirely to separate the sentational from the criminal.

It was a blazing hot day in August.

But the morning paper was uninteresting.

Everybody was out of town, and I yearned for the glades of the New Forest or the shingle of Southsea.

A depleted bank account had caused me to postpone my holiday, and as to my companion, neither the country nor the sea presented the slightest attraction to him.

After his habit he said nothing, and after mine I asked no questions.
24 changes: 24 additions & 0 deletions examples/example_run_raw/test_run_raw.txt
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Martines-MacBook-Pro:negtool martineenger$ python negtool.py -m raw -f test_input_raw.txt -d /Users/martineenger/Downloads/stanford-corenlp-full-2016-10-31
[main] INFO edu.stanford.nlp.pipeline.StanfordCoreNLP - Adding annotator tokenize
[main] INFO edu.stanford.nlp.pipeline.TokenizerAnnotator - No tokenizer type provided. Defaulting to PTBTokenizer.
[main] INFO edu.stanford.nlp.pipeline.StanfordCoreNLP - Adding annotator ssplit
[main] INFO edu.stanford.nlp.pipeline.StanfordCoreNLP - Adding annotator pos
[main] INFO edu.stanford.nlp.tagger.maxent.MaxentTagger - Reading POS tagger model from edu/stanford/nlp/models/pos-tagger/english-left3words/english-left3words-distsim.tagger ... done [2,4 sec].
[main] INFO edu.stanford.nlp.pipeline.StanfordCoreNLP - Adding annotator lemma
[main] INFO edu.stanford.nlp.pipeline.StanfordCoreNLP - Adding annotator depparse
[main] INFO edu.stanford.nlp.parser.nndep.DependencyParser - Loading depparse model file: edu/stanford/nlp/models/parser/nndep/english_UD.gz ...
[main] INFO edu.stanford.nlp.parser.nndep.Classifier - PreComputed 99996, Elapsed Time: 20.65 (s)
[main] INFO edu.stanford.nlp.parser.nndep.DependencyParser - Initializing dependency parser ... done [22,9 sec].

Processing file /Users/martineenger/Dropbox/Masteroppgave/Thesis/src/final/negtool/test_input_raw.txt ... writing to /Users/martineenger/Dropbox/Masteroppgave/Thesis/src/final/negtool/test_input_raw.txt.conll
Annotating file /Users/martineenger/Dropbox/Masteroppgave/Thesis/src/final/negtool/test_input_raw.txt ... done [1,3 sec].

Annotation pipeline timing information:
TokenizerAnnotator: 0,1 sec.
WordsToSentencesAnnotator: 0,0 sec.
POSTaggerAnnotator: 0,2 sec.
MorphaAnnotator: 0,1 sec.
DependencyParseAnnotator: 0,9 sec.
TOTAL: 1,3 sec. for 148 tokens at 116,3 tokens/sec.
Pipeline setup: 26,5 sec.
Total time for StanfordCoreNLP pipeline: 28,2 sec.

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