Difference between revisions of "Parsing/home"

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= Background =
 
= Background =
  
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An experimentation environment for data-driven dependency parsing
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An experimentation environment for data-driven dependency parsing is maintained for NLPL under the coordination of Uppsala University (UU).
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is maintained for NLPL under the coordination of Uppsala University (UU).
 
 
Initially, the software and data are commissioned on the Norwegian Abel supercluster.
 
Initially, the software and data are commissioned on the Norwegian Abel supercluster.
  
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= Using the Uppsala Parser =
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= Preprocessing Tools =
  
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* Log into Abel
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* [http://wiki.nlpl.eu/index.php/Parsing/repp REPP Tokenizer (English and Norwegian)]
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* Activate the NLPL module repository:
 
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module use -a /projects/nlpl/software/modulefiles/
 
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* Load the most recent version of the uuparser module:
 
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module load uuparser
 
  
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'''Train a parsing model'''
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Additionally, a variety of tools for sentence splitting, tokenization, lemmatization, et al.
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are available through the NLPL installations of the
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[http://nltk.org Natural Language Processing Toolkit (NLTK)] and the
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[https://spacy.io spaCy: Natural Language Processing in Python] tools.
  
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To train a set of parsing models on treebanks from Universal Dependencies (v2.1):
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= Parsing Systems =
  
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uuparser --include [languages to include denoted by their ISO id] --outdir my-output-directory
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* [http://wiki.nlpl.eu/index.php/Parsing/uuparser The Uppsala Parser]
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* [http://wiki.nlpl.eu/index.php/Parsing/udpipe UDPipe]
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* [http://wiki.nlpl.eu/index.php/Parsing/dozat Stanford Graph-Based Parser by Tim Dozat]
  
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for example
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= Training and Evaluation Data =
  
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uuparser --include "sv en ru" --outdir ~/experiments
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* [http://wiki.nlpl.eu/index.php/Parsing/ud Universal Dependencies v2.0–2.3]
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* [http://wiki.nlpl.eu/index.php/Parsing/sdp Semantic Dependency Parsing]
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will train separate models for UD Swedish, English and Russian and store the results in the ''experiments'' folder in your home directory.
 
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Model selection is included in the training process by default; that is, at each epoch the current model is evaluated on the UD dev data, and at the end of training the best performing model for each language is selected.  
 
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'''Predicting with a pre-trained parsing model'''
 
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To predict on UD test data with the models trained above:
 
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uuparser --include "sv en ru" --outdir ~/experiments --predict
 
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'''Contact:'''
 
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Aaron Smith, Uppsala University, firstname.lastname@lingfil.uu.se
 

Revision as of 17:42, 30 January 2019

Background

An experimentation environment for data-driven dependency parsing is maintained for NLPL under the coordination of Uppsala University (UU). Initially, the software and data are commissioned on the Norwegian Abel supercluster.

Preprocessing Tools

Additionally, a variety of tools for sentence splitting, tokenization, lemmatization, et al. are available through the NLPL installations of the Natural Language Processing Toolkit (NLTK) and the spaCy: Natural Language Processing in Python tools.

Parsing Systems

Training and Evaluation Data

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