This example uses the function POSTagger (ML Engine) to create the input table for TextMorph, whose output table is input to the function TextTagger (ML Engine).
POSTagger Input: pos_input
| id | txt |
|---|---|
| s1 | Roger Federer born on 8 August 1981, is a greatest tennis player, who has been continuously ranked inside the top 10 since October 2002 and has won Wimbledon, USOpen, Australian and FrenchOpen titles mutiple times |
POSTagger SQL Call
CREATE MULTISET TABLE postagger_output AS (
SELECT * FROM POSTagger (
ON pos_input
USING
Accumulate ('id')
TextColumn ('txt')
) AS dt
) WITH DATA;
POSTagger Output and TextMorph Input: postagger_output
SELECT * FROM postagger_output;
id word_sn word pos_tag -- ------- ------------ ------- s1 3 born VBN s1 5 8 CD s1 6 august NN s1 7 1981 CD s1 9 is VBZ s1 10 a DT s1 11 greatest JJS s1 12 tennis NN s1 13 player NN s1 14 , O s1 15 who WP s1 17 been VBN s1 18 continuously RB s1 19 ranked VBN s1 20 inside IN s1 21 the DT s1 22 top JJ s1 23 10 CD s1 24 since IN s1 25 october JJ s1 26 2002 CD s1 27 and CC s1 28 has VBZ s1 29 won VBN s1 30 wimbledon NN s1 31 , O s1 33 , O s1 34 australian JJ s1 35 and CC s1 36 frenchopen JJ s1 37 titles NNS s1 38 mutiple JJ s1 39 times NNS s1 32 usopen JJ s1 16 has VBZ s1 8 , O s1 4 on IN s1 2 federer NN s1 1 roger NN
TextMorph SQL Call
CREATE MULTISET TABLE textmorph_output AS (
SELECT * FROM TextMorph (
ON postagger_output
USING
WordColumn ('word')
POSTagColumn ('pos_tag')
Accumulate ('id', 'word_sn', 'word', 'pos_tag')
) AS dt
) WITH DATA;
TextMorph Output and TextTagger Input: textmorph_output
SELECT * FROM textmorph_output ORDER BY id, word_sn;
id word_sn word pos_tag morph pos -- ------- ------------ ------- ------------ ---- s1 1 roger NN roger noun s1 2 federer NN federer noun s1 3 born VBN bear verb s1 4 on IN on NULL s1 5 8 CD 8 NULL s1 6 august NN august noun s1 7 1981 CD 1981 NULL s1 8 , O , NULL s1 9 is VBZ be verb s1 10 a DT a NULL s1 11 greatest JJS great adj s1 12 tennis NN tennis noun s1 13 player NN player noun s1 14 , O , NULL s1 15 who WP who NULL s1 16 has VBZ have verb s1 17 been VBN be verb s1 18 continuously RB continuously adv s1 19 ranked VBN rank verb s1 20 inside IN inside NULL s1 21 the DT the NULL s1 22 top JJ top adj s1 23 10 CD 10 NULL s1 24 since IN since NULL s1 25 october JJ october adj s1 26 2002 CD 2002 NULL s1 27 and CC and NULL s1 28 has VBZ have verb s1 29 won VBN win verb s1 30 wimbledon NN wimbledon noun s1 31 , O , NULL s1 32 usopen JJ usopen adj s1 33 , O , NULL s1 34 australian JJ australian adj s1 35 and CC and NULL s1 36 frenchopen JJ frenchopen adj s1 37 titles NNS title noun s1 38 mutiple JJ mutiple adj s1 39 times NNS time noun
TextTagger SQL Call
SELECT * FROM TextTagger (
ON textmorph_output
USING
TaggingRules('equal(morph, "Australian") as grandslam',
'equal(morph, "wimbledon") as grandslam',
'equal(morph, "USOpen") as grandslam',
'equal(morph, "FrenchOpen") as grandslam')
Accumulate ('id', 'word_sn', 'morph')
) AS dt ORDER BY id, word_sn;
TextTagger Output
id word_sn morph tag -- ------- ------------ --------- s1 1 roger s1 2 federer s1 3 bear s1 4 on s1 5 8 s1 6 august s1 7 1981 s1 8 , s1 9 be s1 10 a s1 11 great s1 12 tennis s1 13 player s1 14 , s1 15 who s1 16 have s1 17 be s1 18 continuously s1 19 rank s1 20 inside s1 21 the s1 22 top s1 23 10 s1 24 since s1 25 october s1 26 2002 s1 27 and s1 28 have s1 29 win s1 30 wimbledon grandslam s1 31 , s1 32 usopen grandslam s1 33 , s1 34 australian grandslam s1 35 and s1 36 frenchopen grandslam s1 37 title s1 38 mutiple s1 39 time
Download a zip file of all examples and a SQL script file that creates their input tables from the attachment in the left sidebar.