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https://github.com/wenkokke/dep2con

several algorithms for converting dependency structures into constituency structures.
https://github.com/wenkokke/dep2con

constituency-tree dependency-tree parse-trees tool

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several algorithms for converting dependency structures into constituency structures.

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README 

        
### Conversion from dependency structures to constituency structures



#### Words



First off, the algorithm uses trees of words and part-of-speech

tags. Part-of-speech tags are simple `String`s. Words are records with

three fields: a text[^1], a part of speech tag and a serial (a 1-index

into the sentence).



> data Word =

>      Word { text   :: String

>           , pos    :: String

>           , serial :: Integer

>           }



Words are parsed as follows:



    Word := QuotedString '/' QuotedString '/' Integer



So, for instance, they are represented as:



    "dog"/"NN"/2



#### Dependency Trees



Dependency trees---our input format---are represented as nodes with a

governor and a list of dependants. A leaf is represented by a `Node`

with no dependants.



> data Tree =

>      Node { governor   ::  Word

>           , dependants :: [Tree]

>           }



Dependency trees are parsed as follows:



    Node := Word | '(' Word Node* ')'



So the above word is still a valid tree, but so is the following:



    ("ROOT"/"ROOT"/0

      ("likes"/"VBZ"/4

        ("dog"/"NN"/2 "my"/"PRP"/1)

        ("also"/"RB"/3)

        ("eating"/"VBG"/5 "sausage"/"NN"/6)

      )

    )



Which represents the following tree:



```tree

["ROOT" ["likes" ["dog" "my"] "also" ["eating" "sausage"]]]

```



#### Constituency Trees



The main difference between constituency trees---our output

format---and dependency trees is that dependency trees store words at

every node, whereas in constituency trees only store words in the

leaves, and the nodes are marked with part-of-speech tags.



```haskell

data Tree

   = Leaf Word

   | Node POS [Tree]

```



The printing algorithm for constituency trees is very similar to the

one for parsing dependency trees, so the following is a valid

constituency tree (produced by our conversion algorithm).



    ("ROOT"

      ("VP"

        ("NP"

          ("PRP" "my"/"PRP"/1)

          ("NN" "dog"/"NN"/2)

        )

        ("RB" "also"/"RB"/3)

        ("VBZ" "likes"/"VBZ"/4)

        ("VP"

          ("VBG" "eating"/"VBG"/5)

          ("NN" "sausage"/"NN"/6)

        )

      )

    )



This string represents the following tree:



```tree

["ROOT" ["VP" ["NP" ["PRP" "my"] ["NN" "dog"] ] ["RB" "also"] ["VBZ" "likes"] ["VP" ["VBG" "eating"] ["NN" "sausage"] ] ] ]

```



#### Conversion à la Collins



For the conversion algorithm we use the simple algorithm as proposed

by Collins et al., which tries to produce the simplest possible

constituency trees from dependency trees. The algorithm is as follows:



  - if we encounter a node *without* dependencies, we simply convert it

    into a node bearing the part-of-speech tag and a leaf bearing the

    governor;



  - if we encounter a node *with* dependencies, we do several things:



      * we compute x, the part-of-speech tag of the governor;

      * we compute xp, the phrasal projection of x (using `toXP`);

      * we recursively apply the algorithm to the dependencies;

      * we create a new node for x, using the current governor, and

        insert it into the dependencies;

      * lastly, we combine all of the above in a new node for xp.



Here is the algorithm written out in Haskell:



```haskell

collins :: Dep.Tree -> Con.Tree

collins (Dep.Node gov [])   = Con.Node (pos gov) [Con.Leaf gov]

collins (Dep.Node gov deps) = Con.Node xp (insert gov' deps')

  where

    x     = pos gov :: POS

    xp    = toXP x  :: POS

    gov'  = Con.Node x [Con.Leaf gov] ::  Con.Tree

    deps' = map collins deps          :: [Con.Tree]

       -- ^ apply `collins` to each dependency

```



### References



Michael Collins, Jan Hajic, Lance Ramshaw, and Christoph

Tillmann. [A Statistical Parser for Czech][^Collins1999]. Proceedings

of ACL-1999, pages 505-512, 1999.



Dan Klein and Christopher D. Manning. 2003. [Accurate Unlexicalized

Parsing][^Klein2003]. Proceedings of the 41st Meeting of the

Association for Computational Linguistics, pp. 423-430.



Richard Socher, John Bauer, Christopher D. Manning and Andrew

Y. Ng. 2013. [Parsing With Compositional Vector Grammars][^Socher2013].

Proceedings of ACL 2013



Fei Xia and Martha Palmer, 2001. [Converting Dependency Structures to

Phrase Structures][^Xia2001], Proceedings of the 1st Human Language

Technology Conference (HLT-2001), San Diego, Mar 18-21, 2001.



[^Collins1999]: http://www.aclweb.org/anthology/P99-1065

[^Klein2003]: http://nlp.stanford.edu/~manning/papers/unlexicalized-parsing.pdf

[^Socher2013]: http://nlp.stanford.edu/pubs/SocherBauerManningNg_ACL2013.pdf

[^Xia2001]: http://www.aclweb.org/anthology-new/H/H01/H01-1014.pdf



[^1]: While we refer to this field as the "text", which is how it

      would be used whilst using `dep2con` standalone, when we

      integrate it with `SemAnTE` we will use it to store ids.