Implementation of CRF parser in another language
yiakwy opened this issue · comments
Hi, recently I want to reimplement the CRF parser in another language like (C++/flex, python/boost and so on). I have read the paper "Less grammar, more features" which proposes more features for CRF model. I am trying to figure out how the parser is implemented using CRF model.
- syntactic parser, implement transition functions (anchor rule production in Context Free Gramma) to build up nodes of a syntactic tree. (part 1)
- CRF based model training with proposed features to provide baseline for syntactic transition function. (part 2)
I am trying to read the source code to understand how this works. I wish some the author can help me to figure out how these two parts are implemented.
For the first part, since I am going to use Neural CRF, more details about data preprocessing are appreciated.
Have a nice weekend!
it would be good if you could list specific confusions. There's a lot going
on. For the basic linear model,
```
score(A -> B C, i, j, k) = weights dot features(A->B C, i, j, k)
```
For the neural model, the score is that plus `s(i, j, k) * W * r(A->B, C)`,
where s is a feed forward neural network.
The rules are extracted from the treebank by applying "head outward"
binarization using a slightly modified variant of Collins' head rules (for
English). All introduced intermediate states are collapsed into a single
state.
The feature templates are as described in the paper, though the lexicon is
maybe underspecified. Feature hashing (as described) is important. Don't
ignore.
HTH,
…-- David
On Sun, Sep 24, 2017 at 4:31 PM, yiak ***@***.***> wrote:
Hi, recently I want to reimplement the CRF parser in another language like
(C++/flex, python/boost and so on). I have read the paper "Less grammar,
more features" which proposes more features for CRF model. I am trying to
figure out how the parser is implemented using CRF model.
1. syntactic parser, implement transition functions to build up nodes
of a syntactic tree. (part 1)
2. CRF based model training with proposed features to provide baseline
for syntactic transition function. (part 2)
I am trying to read the source code to understand how this works. I wish
some the author can help me to figure out how these two parts are
implemented.
For the first part, since I am going to use Neural CRF, more details about
data preprocessing are appreciated.
Have a nice weekend!
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