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README

Improved Sentence Boundary Detection

Dan Gillick
January 21, 2009

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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 (<S>) or not. In the example, only the period
at the end of U.S. should be classified as <S>:

"On Jan. 20, former Sen. Barack Obama became the 44th President of 
the U.S.<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.

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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. 

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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.

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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).

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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.

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Setup:

(1) You need Python 2.5 or later. Python 3 does not seem to work.
(2) To use SVM models, you'll need SVM Light (http://svmlight.joachims.org/)
      - once installed, you'll need to modify sbd.py slightly:
        at the top, change the paths to SVM_LEARN and SVM_CLASSIFY to point
        to the files you've installed.

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Example calls:

(show command line options)
python sbd.py -h

(split sentences in sample.txt using the provided Naive Bayes model)
python sbd.py -m model_nb sample.txt

(now using the provided SVM model)
python sbd.py -m model_svm sample.txt

(now keeping tokenized output)
python sbd.py -m model_nb -t sample.txt

(now writing output to sample.sent)
python sbd.py -m model_nb -t sample.txt -o sample.sent

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Note about SVM_LIGHT:

The provided SVM model was built with SVM_LIGHT version 6.02. It seems that
SVM_CLASSIFY requires a matching version or it will crash. So, you can either
try to use version 6.02, or you can make the following quick fix:

open model_svm/svm_model
change the first line from:
SVM-light Version V6.02
to whatever your version is.
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Dan Gillick
January 21, 2009
Berkeley, California

dgillick@cs.berkeley.edu