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Sshanu / Relation-Classification-using-Bidirectional-LSTM-Tree

TensorFlow Implementation of the paper "End-to-End Relation Extraction using LSTMs on Sequences and Tree Structures" and "Classifying Relations via Long Short Term Memory Networks along Shortest Dependency Paths" for classifying relations
MIT License
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stanford_parser.jar #2

Closed Eddie1224 closed 7 years ago

Eddie1224 commented 7 years ago

hello. I have a question that which type of the stanford_parser ? java or python ? shall I download the full parser.zip and unzip it , then use the .jar and ..model.jar inside directory?

Sshanu commented 7 years ago

I have used NLTK in python. Go through this link, this helps to using NLTK in python. https://stackoverflow.com/questions/13883277/stanford-parser-and-nltk/34112695

  1. Download and install NLTK v3, same as above.

  2. Download the latest version from (current version filename is stanford-parser-full-2015-01-29.zip): http://nlp.stanford.edu/software/lex-parser.shtml#Download

  3. Extract the standford-parser-full-20xx-xx-xx.zip.

  4. Create a new folder ('jars' in my example). Place the extracted files into this jar folder: stanford-parser-3.x.x-models.jar and stanford-parser.jar.

  5. As shown above you can use the environment variables (STANFORD_PARSER & STANFORD_MODELS) to point to this 'jars' folder. I'm using Linux, so if you use Windows please use something like: C://folder//jars.

  6. Open the stanford-parser-3.x.x-models.jar using an Archive manager (7zip).

  7. Browse inside the jar file; edu/stanford/nlp/models/lexparser. Again, extract the file called 'englishPCFG.ser.gz'. Remember the location where you extract this ser.gz file.

  8. When creating a StanfordParser instance, you can provide the model path as parameter. This is the complete path to the model, in our case /location/of/englishPCFG.ser.gz.