hase

Hase provides Haskell bindings for the Senna NLP toolkit, supporting

• Part of Speech tagging (POS)
• Chunking (CHK)
• Name Entity Recognition (NER)
• Semantic Role Labeling (SRL)

Getting Started

At first, please download Senna and extract the Senna source files into foreign/senna/. The tar command below does just that.

tar -xzf senna-v3.0.tgz -C foreign/

Then run

stack setup
stack build

to build hase. You can run the example program by executing

stack exec example

Usage

Here's a simple example to tokenize and process a sentence read from stdin.

module Main where
import NLP.Senna

main =
  withContext $ \ctx -> do
    tokenize ctx =<< getLine
    print =<< (process ctx :: IO [Token])
    print =<< (process ctx :: IO [Maybe POS])
    print =<< (process ctx :: IO [(CHK, Phrase)])
    print =<< (process ctx :: IO [(NER, Phrase)])
    print =<< (process ctx :: IO [[(SRL, Phrase)]])

The Context type holds the internal C library state. You can either manage Context manually by calling createContext and freeContext, or you can use withContext, which automatically creates and frees the Context for you.

Once the Context is created, you pass a sentence to the tokenize function. The tokenize function sets up Context to work with your sentence. Then you can call process to perform various NLP tasks on the previously tokenized sentence.

The Context is memory hungry (200 MB) and should only be setup once. Processing multiple sentences works by alternately calling tokenize and process on the same Context.

License

hase is released under MIT license. You can find a copy of the MIT License in the LICENSE file.