Welcome to PyNLPl’s documentation!¶
PyNLPl, pronounced as ‘pineapple’, is a Python library for Natural Language Processing. It contains various modules useful for common, and less common, NLP tasks. PyNLPl can be used for basic tasks such as the extraction of n-grams and frequency lists, and to build simple language model. There are also more complex data types and algorithms. Moreover, there are parsers for file formats common in NLP (e.g. FoLiA/Giza/Moses/ARPA/Timbl/CQL). There are also clients to interface with various NLP specific servers. PyNLPl most notably features a very extensive library for working with FoLiA XML (Format for Linguistic Annotatation).
The library is a divided into several packages and modules. It works on Python 2.7, as well as Python 3.
The following modules are available:
pynlpl.datatypes- Extra datatypes (priority queues, patterns, tries)
pynlpl.evaluation- Evaluation & experiment classes (parameter search, wrapped progressive sampling, class evaluation (precision/recall/f-score/auc), sampler, confusion matrix, multithreaded experiment pool)
pynlpl.formats.cgn- Module for parsing CGN (Corpus Gesproken Nederlands) part-of-speech tags
pynlpl.formats.folia- Extensive library for reading and manipulating the documents in FoLiA format (Format for Linguistic Annotation).
pynlpl.formats.fql- Extensive library for the FoLiA Query Language (FQL), built on top of
pynlpl.formats.folia. FQL is currently documented here.
pynlpl.formats.cql- Parser for the Corpus Query Language (CQL), as also used by Corpus Workbench and Sketch Engine. Contains a convertor to FQL.
pynlpl.formats.giza- Module for reading GIZA++ word alignment data
pynlpl.formats.moses- Module for reading Moses phrase-translation tables.
pynlpl.formats.sonar- Largely obsolete module for pre-releases of the SoNaR corpus, use
pynlpl.formats.timbl- Module for reading Timbl output (consider using python-timbl instead though)
pynlpl.lm.lm- Module for simple language model and reader for ARPA language model data as well (used by SRILM).
pynlpl.search- Various search algorithms (Breadth-first, depth-first, beam-search, hill climbing, A star, various variants of each)
pynlpl.statistics- Frequency lists, Levenshtein, common statistics and information theory functions
pynlpl.textprocessors- Simple tokeniser, n-gram extraction
- Common Functions
- Data Types
- Evaluation & Experiments
- FoLiA library
- Reading FoLiA
- Editing FoLiA
- Searching in a FoLiA document
- Higher-Order Annotations
- Language Models
- Search Algorithms
- Statistics and Information Theory
- Text Processors