Fast supervised sentence boundary detection using the averaged perceptron
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Detector Morse

Detector Morse is a program for sentence boundary detection (henceforth, SBD), also known as sentence segmentation. Consider the following sentence, from the Wall St. Journal portion of the Penn Treebank:

Rolls-Royce Motor Cars Inc. said it expects its U.S. sales to remain
steady at about 1,200 cars in 1990.

This sentence contains 4 periods, but only the last denotes a sentence boundary. The first one in U.S. is unambiguously part of an acronym, not a sentence boundary; the same is true of expressions like $12.53. But the periods at the end of Inc. and U.S. could easily denote a sentence boundary. Humans use the local context to determine that neither period denote sentence boundaries (e.g. the selectional properties of the verb expect are not met if there is a sentence bounary immediately after U.S.). Detector Morse uses artisinal, handcrafted contextual features and low-impact, leave-no-trace machine learning methods to automatically detect sentence boundaries.

SBD is one of the earliest pieces of many natural language processing pipelines. Since errors at this step are likely to propagate, SBD is an important---albeit overlooked---problem in natural language processing.

Detector Morse has been tested on CPython 3.4 and PyPy3 (2.3.1, corresponding to Python 3.2); the latter is much faster. Detector Morse depends on the Python module jsonpickle to (de)serialize models. For the versions used, see requirements.txt.


    sudo pip install -r requirements.txt
    sudo python setup build install


 Detector Morse, by Kyle Gorman
 usage: python -m detectormorse [-h] [-v] [-V] (-t TRAIN | -r READ)
                                (-s SEGMENT | -w WRITE | -e EVALUATE)
                                [-E EPOCHS] [-C]

 optional arguments:
   -h, --help            show this help message and exit
   -v, --verbose         enable verbose output
   -V, --really-verbose  enable even more verbose output
   -t TRAIN, --train TRAIN
                         training data
   -r READ, --read READ  read in serialized model
   -s SEGMENT, --segment SEGMENT
                         segment sentences
   -w WRITE, --write WRITE
                         write out serialized model
   -e EVALUATE, --evaluate EVALUATE
                         evaluate on segmented data
   -E EPOCHS, --epochs EPOCHS
                         # of epochs (default: 20)
   -C, --nocase          disable case features

Files used for training (-t/--train) and evaluation (-e/--evaluate) should contain one sentence per line; newline characters are ignored otherwise.

When segmenting a file (-s/--segment), DetectorMorse simply inserts a newline after predicted sentence boundaries that aren't already marked by one. All other newline characters are passed through, unmolested.

The included DM-wsj.json.gz is a segmenter model trained on the Wall St. Journal portion of the Penn Treebank.


See this blog post.


DetectorMorse processes text by reading the entire file into memory. This means it will not work with files that won't fit into the available RAM. The easiest way to get around this is to import the Detector instance in your own Python script.

Exciting extras!

I've included a Perl script which attempts to invert the Penn Treebank tokenization process. Tokenization is an inherently "lossy" procedure, so there is no guarantee that the output is exactly how it appeared in the WSJ. But, the rules appear to be correct and produce sane text, and I have used it for all experiments. Update (2015-02-10): I've removed this script; I just use the Stanford tokenizer for this purpose, now.