Metadata-Version: 1.1
Name: splitta
Version: 0.1.0
Summary: API for the StoryPilot platform.
Home-page: https://github.com/hinstitute/splitta
Author: Dan Gillick
Author-email: dgillick@cs.berkeley.edu
License: (c) 2009 Dan Gillick
Download-URL: https://github.com/hinstitute/splitta/tarball/0.1.0
Description: Improved Sentence Boundary Detection
        ====================================
        
        Overview
        --------
        
        Consider the following text:
        
        "On Jan. 20, former Sen. Barack Obama became the 44th President of the
        U.S. Millions attended the Inauguration."
        
        The periods are potentially ambiguous, signifying either the end of a
        sentence, an abbreviation, or both. The sentence boundary detection
        (SBD) task involves disambiguating the periods, and in particular,
        classifying each period as end-of-sentence () or not. In the example,
        only the period at the end of U.S. should be classified as :
        
        "On Jan. 20, former Sen. Barack Obama became the 44th President of the
        U.S. Millions attended the Inauguration."
        
        Chances are, if you are using some SBD system, it has an error rate of
        1%-3% on English newswire text. The system described here achieves the
        best known error rate on the Wall Street Journal corpus: 0.25% and
        comparable error rates on the Brown corpus (mixed genre) and other test
        corpora.
        
        Background
        ----------
        
        SBD is fundamental to many natural language processing problems, but
        only a few papers describe solutions. A variety of rule-based systems
        are floating around, and a few semi-statistical systems are available if
        you know where to look. The most widely cited are:
        
        -  Alembic (Aberdeen, et al. 1995): Abbreviation list and ~100
           hand-crafted regular expressions.
        -  Satz (Palmer & Hearst at Berkeley, 1997): Part of speech features and
           abbreviation lists as input to a classifier (neural nets and decision
           trees have similar performance).
        -  mxTerminator (Reynar & Ratnaparkhi, 1997): Maximum entropy
           classification with simple lexical features.
        -  Mikheev (Mikheev, 2002): Observes that perfect labels for
           abbreviations and names gives almost perfect SBD results. Creates
           heuristics for marking these, unsupervised, from held-out data.
        -  Punkt (Strunk and Kiss, 2006): Unsupervised method uses heuristics to
           identify abbreviations and sentence starters.
        
        I have not been able to find publicly available copies of any of these
        systems, with the exception of Punkt, which ships with NLTK.
        Nonetheless, here are some error rates reported on what I believe to be
        the same subset of the WSJ corpus (sections 03-16).
        
        -  Alembic: 0.9%
        -  Satz: 1.5%; 1.0% with extra hand-written lists of abbreviations and
           non-names.
        -  mxTerminator: 2.0%; 1.2% with extra abbreviation list.
        -  Mikheev: 1.4%; 0.45% with abbreviation list (assembled automatically
           but carefully tuned; test-set-dependent parameters are a concern)
        -  Punkt: 1.65% (Though if you use the model that ships with NLTK,
           you'll get over 3%)
        
        All of these systems use lists of abbreviations in some capacity, which
        I think is a mistake. Some abbreviations almost never end a sentence
        (Mr.), which makes list-building appealing. But many abbreviations are
        more ambiguous (U.S., U.N.), which complicates the decision.
        
        --------------
        
        While 1%-3% is a low error rate, this is often not good enough. In
        automatic document summarization, for example, including a sentence
        fragment usually renders the resulting summary unintelligible. With
        10-sentence summaries, 1 in 10 is ruined by an SBD system with 99%
        accuracy. Improving the accuracy to 99.75%, only 1 in 40 is ruined.
        Improved sentence boundary detection is also likely to help with
        language modeling and text alignment.
        
        --------------
        
        I built a supervised system that classifies sentence boundaries without
        any heuristics or hand-generated lists. It uses the same training data
        as mxTerminator, and allows for Naive Bayes or SVM models (SVM Light).
        
        +-------------------------------------+---------+---------------+
        | Corpus                              | SVM     | Naive Bayes   |
        +=====================================+=========+===============+
        | WSJ                                 | 0.25%   | 0.35%         |
        +-------------------------------------+---------+---------------+
        | Brown                               | 0.36%   | 0.45%         |
        +-------------------------------------+---------+---------------+
        | Complete Works of Edgar Allen Poe   | 0.52%   | 0.44          |
        +-------------------------------------+---------+---------------+
        
        I've packaged this code, written in Python, for general use. Word-level
        tokenization, which is particularly important for good sentence boundary
        detection, is included.
        
        Note that the included models use all of the labeled data listed here,
        meaning that the expected results are somewhat better than the numbers
        reported above. Including the Brown data as training improves the WSJ
        result to 0.22% and the Poe result to 0.37 (using the SVM).
        
        Performance
        -----------
        
        A few other notes on performance. The standard WSJ test corpus includes
        26977 possible sentence boundaries. About 70% are in fact sentence
        boundaries. Classification with the included SVM model will give 59
        errors. Of these, 24 (41%) involve the word "U.S.", a particularly
        interesting case. In training, "U.S." appears 2029 times, and 90 of
        these are sentence boundaries. Further complicating the situation,
        "U.S." often appears in a context like "U.S. Security Council" or "U.S.
        Government", and either "Security" or "Government" are viable sentence
        starters.
        
        Other confusing cases include "U.N.", "U.K.", and state abbreviations
        like "N.Y." which have similar characteristics as "U.S." but appear
        somewhat less frequently.
        
        Setup
        -----
        
        See SETUP.md for setup instructions and notes.
        
Keywords: splitta,setence bounadry detection,sbd
Platform: UNKNOWN
