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https://github.com/ayoungprogrammer/Lango

Language Lego
https://github.com/ayoungprogrammer/Lango

nlp parse-trees stanford-corenlp stanford-parser

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Language Lego

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# Lango



[![Gitter](https://badges.gitter.im/lango-nlp/Lobby.svg)](https://gitter.im/lango-nlp/Lobby?utm_source=badge&utm_medium=badge&utm_campaign=pr-badge)



Lango is a natural language processing library for working with the building blocks of language. It includes tools for:



* matching [constituent parse trees](https://en.wikipedia.org/wiki/Parse_tree#Constituency-based_parse_trees). 

* modeling conversations (TODO)



Need help? Ask me for help on [Gitter](https://gitter.im/lango-nlp/Lobby)



## Installation



### Install package with pip



```

pip install lango

```



### Download Stanford CoreNLP



Make sure you have Java installed for the Stanford CoreNLP to work.



[Download Stanford CoreNLP](http://stanfordnlp.github.io/CoreNLP/#download)



Extract to any folder



### Run the Stanford CoreNLP server



Run the following command in the folder where you extracted Stanford CoreNLP

```

java -mx4g -cp "*" edu.stanford.nlp.pipeline.StanfordCoreNLPServer

```



## Docs



- [Blog Post](http://blog.ayoungprogrammer.com/2016/07/natural-language-understanding-by.html/)

- [Read the docs](http://lango.readthedocs.io/en/latest/)

- [Examples](http://github.com/ayoungprogrammer/lango/tree/master/examples)



## Matching



Matching is done by comparing a set rules and matching it with a parse tree. You

can see parse trees for sentences from examples/parser_input.py. 



The set of rules is recursive and can match multiple parts of the parse tree.



Rules can be broken down into smaller parts:

- Tag

- Token

- Token Tree

- Rules



### Tag



A tag is a POS (part of speech) tag to match. A list of POS tags used by the Stanford Parser can be found [here](https://www.ling.upenn.edu/courses/Fall_2003/ling001/penn_treebank_pos.html).



```

Format:

tag = string



Example:

'NP'

'VP'

'PP'

```



### Token



A token is a string comprising of a tag and modifiers/labels for matching. We specify a match_label to match the tag to. We can specify opts for extracting the string from a tree. We can specify eq for matching the tree to a string.



```

Example string:

The red car



opts:

-o Get object by removing "a", "the", etc. (Ex. red car)

-r Get raw string (Ex. The red car)

```



```

Format: (only tag is required)

token = tag:match_label-opts=eq



Example: 

'VP'

'NP:subject-o'

'NP:np'

'VP=run'

'VP:action=run'

```



### Token Tree



A token tree is a recursive tree of tokens. The tree matches the structure of a parse tree.



```

Format:

token_tree = ( token token_tree token_tree ... )



Examples: 

'( NP ( DT ) ( NP:subject-o ) )'

'( NP )'

'( PP ( TO=to ) ( NP:object-o ) )'

```



### Rules



Rules are a dictionary of token trees to dictionaries of matching labels to a 

nested set of rules. 



```

Format:

rules = {token_tree: {match_label: rules}}



Example: 

{

    '( S ( NP:np ) ( VP ( VBD:action-o ) ( PP:pp ) ) )': {

        'np': {

            '( NP:subject-o )': {}

        },

        'pp': {

            '( PP ( TO=to ) ( NP:to_object-o ) )': {},

            '( PP ( IN=from ) ( NP:from_object-o ) )': {},

        }

    },

}

```



When matching a rule to a parse tree, the token tree is first matched. Then, all

matching tags are matched to nested rules corresponding to their matching label.



All nested match labels must have a subrule match or the rules will not match.



The first rule to match is returned so the order of match is based on key 

ordering (use OrderedDict if order matters). Once a rule is matched, it calls

the callback function with the context as arguments.



### Example



Suppose we have the sentence "Sam ran to his house" and we wanted to match the

subject ("Sam"), the object ("his house") and the action ("ran"). 



Sample parse tree for "Sam ran to his house" from the Stanford Parser. 



```

(S

  (NP 

    (NNP Sam)

    )

  (VP

    (VBD ran)

      (PP 

        (TO to)

        (NP

          (PRP$ his)

          (NN house)

          )

        )

    )

  )

```



Simplified image of tree:



![tree](/docs/_static/img/sent_tree.png)



```

Matching:

Parse Tree: 

(S (NP (NNP Sam) ) (VP (VBD ran) (PP (TO to) (NP (PRP$ his) (NN house))))



Matched token tree: '( S ( NP:np ) ( VP ( VBD:action-o ) ( PP:pp ) ) )'

Matched context: 

  np: (NP (NNP Sam))

  action-o: 'ran'

  pp: (PP (TO to) (NP (PRP$ his) (NN house)))

```



Rule for '( S ( NP:np ) ( VP ( VBD:action-o ) ( PP:pp ) ) )':



![tree](/docs/_static/img/rule_tree_1.png)



Matching 'NP' matches the whole NP tree and converts to a word:



```

Matched token tree for np: '( NP:subject-o )'

Matched context:

  subject-o: 'Sam'

```



Matching 'PP' requires matching the nested rules:



```

Match token tree for pp: '( PP ( TO=to ) ( NP:to_object-o ) )'

Match context:

  object-o: 'his house'



Match token tree for pp: '( PP ( IN=from ) ( NP:from_object-o ) )'

No match found

```

PP of the sample sentence:



![tree](/docs/_static/img/sent_tree_pp.png)



Nested PP rules:



![tree](/docs/_static/img/rule_tree_2.png)

![tree](/docs/_static/img/rule_tree_3.png)



Only the first rule matches for 'PP'.



Now that we have a match for all nested rules, we can return the context:

```

Returned context:

  action: 'ran'

  subject: 'sam'

  to_object: 'his house'

```



Full code:



```python

from lango.parser import StanfordServerParser

from lango.matcher import match_rules



parser = StanfordServerParser()



rules = {

  '( S ( NP:np ) ( VP ( VBD:action-o ) ( PP:pp ) ) )': {

    'np': {

        '( NP:subject-o )': {}

    },

    'pp': {

        '( PP ( TO=to ) ( NP:to_object-o ) )': {},

        '( PP ( IN=from ) ( NP:from_object-o ) )': {}

    }

  }

}



def fun(subject, action, to_object=None, from_object=None):

    print "%s,%s,%s,%s" % (subject, action, to_object, from_object)



tree = parser.parse('Sam ran to his house')

match_rules(tree, rules, fun)

# output should be: sam, ran, his house, None



tree = parser.parse('Billy walked from his apartment')

match_rules(tree, rules, fun)

# output should be: billy, walked, None, his apartment

```