Metadata-Version: 1.0
Name: segeval
Version: 2.0.9
Summary: A package providing text segmentation evaluation metrics and utilities
Home-page: http://segeval.readthedocs.org/
Author: Chris Fournier
Author-email: chris.m.fournier@gmail.com
License: Copyright (c) 2011-2013 Chris Fournier

All rights reserved.

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Description: SegEval v2.0
        ============
        
        .. image:: https://travis-ci.org/cfournie/segmentation.evaluation.png?branch=master
        	:target: https://travis-ci.org/cfournie/segmentation.evaluation
        .. image:: https://coveralls.io/repos/cfournie/segmentation.evaluation/badge.png?branch=master
        	:target: https://coveralls.io/r/cfournie/segmentation.evaluation?branch=master
        .. image:: https://pypip.in/v/segeval/badge.png
           :target: https://crate.io/packages/segeval/
        .. image:: https://pypip.in/d/segeval/badge.png
        	:target: https://crate.io/packages/segeval/
        
        |
        
        Text segmentation is the task of splitting up any amount of text into segments by placing boundaries between some atomic unit (e.g., morphemes, words, lines, sentences, paragraphs, sections, etc.).  It's a common pre-processing step in many `Natural Language Processing (NLP) <http://en.wikipedia.org/wiki/Natural_language_processing>`_ tasks.
        
        This package is a collection of metrics and for comparing text segmentations and evaluating automatic text segmenters.  Both new (**Boundary Similarity**, **Segmentation Similarity**) and traditional (**WindowDiff**, **Pk**) are included, as well as inter-coder agreement coefficients and confusion matrices based upon a boundary edit distance.
        
        To see some examples of its usage, `read the docs <http://segeval.readthedocs.org/>`_.
        
        
        Feature Support
        ---------------
        Included is a variety of segmentation comparison metrics, including:
        
        * Boundary Edit Distance (BED)
        * Boundary Similarity (B)
        * BED-based confusion matrices (and precision/recall/F1)
        * Segmentation Similarity (S)
        * WindowDiff
        * Pk
        
        Additionally, B-based inter-coder agreement coefficients for segmentation that are suitable for 2 or more coders are provided, including:
        
        * Fleiss' Pi (i.e., Siegel and Castellan's K)
        * Fleiss' Kappa
        
        
        Installation
        ------------
        
        To install SegEval, simply run:
        
        .. code-block:: bash
        
            $ pip install segeval
        
        
        Documentation
        -------------
        
        Documentation is available at http://segeval.readthedocs.org/.
        
        
        Citing SegEval
        --------------
        If you're using this software for research, please cite the `ACL paper <http://nlp.chrisfournier.ca/publications/pdf/fournier_2013a.pdf>`_ [PDF] and, if you need to go into details, the `thesis <http://nlp.chrisfournier.ca/publications/pdf/fournier_masc_thesis.pdf>`_ [PDF] describing this work:
        
        - *Chris Fournier. 2013. Evaluating Text Segmentation using Boundary Edit Distance. Proceedings of 51st Annual Meeting of the Association for Computational Linguistics. (ACL 2013), to appear. Association for Computational Linguistics, Stroudsburg, PA, USA.*
        
        - *Chris Fournier. 2013. Evaluating Text Segmentation. (Master's thesis). University of Ottawa.*
        
        BibTeX:
        
        .. code-block:: latex
        
        	@inproceedings{Fournier2013a,
        		author		= {Fournier, Chris},
        		year		= {2013},
        		title		= {{Evaluating Text Segmentation using Boundary Edit Distance}},
        		booktitle	= {Proceedings of 51st Annual Meeting of the Association for Computational Linguistics},
        		publisher	= {Association for Computational Linguistics},
        		location	= {Sophia, Bulgaria},
        		pages		= {to appear},
        		address		= {Stroudsburg, PA, USA}
        	}
        
        	@mastersthesis{Fournier2013b,
        		author		= {Fournier, Chris},
        		title		= {Evaluating Text Segmentation},
        		school		= {University of Ottawa},
        		year		= {2013}
        	}
        
Keywords: segmentation,similarity,discourse
Platform: Any
Classifier: Development Status :: 5 - Production/Stable
Classifier: Environment :: Console
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Science/Research
Classifier: Natural Language :: English
Classifier: License :: OSI Approved :: BSD License
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: Python
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Classifier: Topic :: Scientific/Engineering :: Information Analysis
Classifier: Topic :: Text Processing
Classifier: Topic :: Utilities
Classifier: Programming Language :: Python :: 2.6
Classifier: Programming Language :: Python :: 2.7
Classifier: Programming Language :: Python :: 3.2
Classifier: Programming Language :: Python :: 3.3
