Input
| paraid | paratopic | paratext |
|---|---|---|
| 1 | Decision Trees | Decision tree learning uses a decision tree as a predictive model which maps observations about an item to conclusions about the items target value. It is one of the predictive modeling approaches used in statistics, data mining and machine learning. Tree models where the target variable can take a finite set of values are called classification trees. In these tree structures, leaves represent class labels and branches represent conjunctions of features that lead to those class labels. Decision trees where the target variable can take continuous values (typically real numbers) are called regression trees. |
| 2 | Simple Regression | In statistics, simple linear regression is the least squares estimator of a linear regression model with a single explanatory variable. In other words, simple linear regression fits a straight line through the set of n points in such a way that makes the sum of squared residuals of the model (that is, vertical distances between the points of the data set and the fitted line) as small as possible. |
| 3 | Logistic Regression | Logistic regression was developed by statistician David Cox in 1958[2][3] (although much work was done in the single independent variable case almost two decades earlier). The binary logistic model is used to estimate the probability of a binary response based on one or more predictor (or independent) variables (features). As such it is not a classification method. It could be called a qualitative response/discrete choice model in the terminology of economics. |
| 4 | Cluster analysis | Cluster analysis or clustering is the task of grouping a set of objects in such a way that objects in the same group (called a cluster) are more similar (in some sense or another) to each other than to those in other groups (clusters). It is a main task of exploratory data mining, and a common technique for statistical data analysis, used in many fields, including machine learning, pattern recognition, image analysis, information retrieval, and bioinformatics. Cluster analysis itself is not one specific algorithm, but the general task to solve. It can be achieved by various algorithms that differ significantly in their notion of what constitutes a cluster and how to efficiently find them. |
| 5 | Association rule learning | Association rule learning is a method for discovering interesting relations between variables in large databases. It is intended to identify strong rules discovered in databases using different measures of interestingness. Based on the concept of strong rules, Rakesh Agrawal et al.[2] introduced association rules for discovering regularities between products in large-scale transaction data recorded by point-of-sale (POS) systems in supermarkets. For example, the rule {onions, potatoes} => {burger} found in the sales data of a supermarket would indicate that if a customer buys onions and potatoes together, they are likely to also buy hamburger meat. |
SQL Call
TextChunker requires each sentence to have a unique identifier, and the input to TextChunker must be partitioned by that identifier.
SELECT * FROM TextChunker (
ON (
SELECT * FROM POSTagger (
ON (
SELECT paraid*1000+sentence_sn AS sentence_id, sentence FROM SentenceExtractor (
ON paragraphs_input
USING
TextColumn ('paratext')
Accumulate ('paraid')
) AS dt1
)
USING
TextColumn ('sentence')
Accumulate ('sentence_id')
) AS dt2
) PARTITION BY sentence_id ORDER BY word_sn
USING
WordColumn('word')
POSColumn('pos_tag')
) AS dt;
Output
partition_key chunk_sn chunk chunk_tag
------------- -------- ---------------------------------------------------------------------------------------------------- ---------
1001 1 decision tree learning NP
1001 2 uses VP
1001 3 a decision tree NP
1001 4 as PP
1001 5 a predictive model NP
1001 6 which NP
1001 7 maps VP
1001 8 observations NP
1001 9 about PP
1001 10 an item NP
1001 11 to PP
1001 12 conclusions NP
1001 13 about PP
1001 14 the items target value NP
1001 15 . O
1001 16 it NP
1001 17 is VP
1001 18 one NP
1001 19 of PP
1001 20 the predictive modelling approaches NP
1001 21 used VP
1001 22 in PP
1001 23 statistics , data mining and machine learning . tree models NP
1001 24 where ADVP
1001 25 the target variable NP
1001 26 can take VP
1001 27 a finite set NP
1001 28 of PP
1001 29 values NP
1001 30 are called VP
1001 31 classification trees NP
1001 32 . O
1001 33 in PP
1001 34 these tree structures NP
1001 35 , O
1001 36 leaves VP
1001 37 represent class labels and branches NP
1001 38 represent VP
1001 39 conjunctions NP
1001 40 of PP
1001 41 features NP
1001 42 that NP
1001 43 lead VP
1001 44 to PP
1001 45 those class labels . decision trees NP
1001 46 where ADVP
1001 47 the target variable NP
1001 48 can take VP
1001 49 continuous values NP
1001 50 ( typically real numbers NP
1001 51 ) NP
1001 52 are called VP
1001 53 regression trees NP
1001 54 . O
2001 1 in PP
2001 2 statistics NP
2001 3 , O
2001 4 simple linear regression NP
2001 5 is VP
2001 6 the least squares estimator NP
2001 7 of PP
2001 8 a linear regression model NP
2001 9 with PP
2001 10 a single explanatory variable . NP
2001 11 in PP
2001 12 other words NP
2001 13 , O
2001 14 simple linear regression NP
2001 15 fits VP
2001 16 a straight line NP
2001 17 through PP
2001 18 the set NP
2001 19 of PP
2001 20 n points NP
2001 21 in PP
2001 22 such a way NP
2001 23 that NP
2001 24 makes VP
2001 25 the sum NP
2001 26 of PP
2001 27 squared residuals NP
2001 28 of PP
2001 29 the model ( NP
2001 30 that NP
2001 31 is VP
2001 32 , vertical distances NP
2001 33 between PP
2001 34 the points NP
2001 35 of PP
2001 36 the data NP
2001 37 set VP
2001 38 and O
2001 39 the fitted line NP
2001 40 ) VP
2001 41 as small ADJP
2001 42 as PP
2001 43 possible ADJP
2001 44 . O
3001 1 logistic regression NP
3001 2 was developed VP
3001 3 by PP
3001 4 statistician david cox NP
3001 5 in PP
3001 6 1958[2][3](although much work NP
3001 7 was done VP
3001 8 in PP
3001 9 the single independent variable case NP
3001 10 almost ADVP
3001 11 two decades NP
3001 12 earlier) VP
3001 13 . O
3001 14 the binary logistic model NP
3001 15 is used to estimate VP
3001 16 the probability NP
3001 17 of PP
3001 18 a binary response NP
3001 19 based VP
3001 20 on PP
3001 21 one or more predictor ( or independent ) variables ( features) . NP
3001 22 as PP
3001 23 such ADJP
3001 24 it NP
3001 25 is VP
3001 26 not O
3001 27 a classification method NP
3001 28 . VP
3001 29 it NP
3001 30 could be called VP
3001 31 a qualitative response/discrete choice model NP
3001 32 in PP
3001 33 the terminology NP
3001 34 of PP
3001 35 economics NP
3001 36 . O
4001 1 cluster analysis or clustering NP
4001 2 is VP
4001 3 the task NP
4001 4 of PP
4001 5 grouping VP
4001 6 a set NP
4001 7 of PP
4001 8 objects NP
4001 9 in PP
4001 10 such a way NP
4001 11 that NP
4001 12 objects VP
4001 13 in PP
4001 14 the same group NP
4001 15 ( called VP
4001 16 a cluster ) NP
4001 17 are VP
4001 18 more similar ADJP
4001 19 ( O
4001 20 in PP
4001 21 some sense NP
4001 22 or O
4001 23 another ) NP
4001 24 to PP
4001 25 each other NP
4001 26 than PP
4001 27 to PP
4001 28 those NP
4001 29 in PP
4001 30 other groups NP
4001 31 ( clusters) NP
4001 32 . O
4001 33 it NP
4001 34 is VP
4001 35 a main task NP
4001 36 of PP
4001 37 exploratory data mining NP
4001 38 , O
4001 39 and O
4001 40 a common technique NP
4001 41 for PP
4001 42 statistical data analysis NP
4001 43 , used VP
4001 44 in PP
4001 45 many fields NP
4001 46 , O
4001 47 including PP
4001 48 machine learning NP
4001 49 , O
4001 50 pattern recognition , image analysis , information retrieval , and bioinformatics . cluster analysis NP
4001 51 itself NP
4001 52 is VP
4001 53 not O
4001 54 one specific algorithm NP
4001 55 , O
4001 56 but O
4001 57 the general task NP
4001 58 to be solved VP
4001 59 . O
4001 60 it NP
4001 61 can be achieved VP
4001 62 by PP
4001 63 various algorithms NP
4001 64 that NP
4001 65 differ VP
4001 66 significantly ADVP
4001 67 in PP
4001 68 their notion NP
4001 69 of PP
4001 70 what NP
4001 71 constitutes VP
4001 72 a cluster NP
4001 73 and O
4001 74 how ADVP
4001 75 to efficiently find VP
4001 76 them NP
4001 77 . O
5001 1 association rule learning NP
5001 2 is VP
5001 3 a method NP
5001 4 for PP
5001 5 discovering VP
5001 6 interesting relations NP
5001 7 between PP
5001 8 variables NP
5001 9 in PP
5001 10 large databases NP
5001 11 . O
5001 12 it NP
5001 13 is intended to identify VP
5001 14 strong rules NP
5001 15 discovered VP
5001 16 in PP
5001 17 databases NP
5001 18 using VP
5001 19 different measures NP
5001 20 of PP
5001 21 interestingness NP
5001 22 . based VP
5001 23 on PP
5001 24 the concept NP
5001 25 of PP
5001 26 strong rules NP
5001 27 , O
5001 28 rakesh agrawal et al.[2 ] introduced association rules NP
5001 29 for PP
5001 30 discovering regularities NP
5001 31 between PP
5001 32 products NP
5001 33 in PP
5001 34 large-scale transaction data NP
5001 35 recorded VP
5001 36 by PP
5001 37 point-of-sale ( pos ) systems NP
5001 38 in PP
5001 39 supermarkets NP
5001 40 . O
5001 41 for PP
5001 42 example NP
5001 43 , O
5001 44 the rule { onions , potatoes}=>{burger NP
5001 45 } found VP
5001 46 in PP
5001 47 the sales data NP
5001 48 of PP
5001 49 a supermarket NP
5001 50 would indicate VP
5001 51 that SBAR
5001 52 if SBAR
5001 53 a customer NP
5001 54 buys VP
5001 55 onions NP
5001 56 and O
5001 57 potatoes VP
5001 58 together ADVP
5001 59 , O
5001 60 they NP
5001 61 are VP
5001 62 likely ADJP
5001 63 to also buy VP
5001 64 hamburger meat NP
5001 65 . O
Download a zip file of all examples and a SQL script file that creates their input tables from the attachment in the left sidebar.