MORphological Sparsity Embiggens Learning: A simple unsupervised morphological learning model.
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README.md

MORSEL

MORphological Sparsity Embiggens Learning: A simple, unsupervised morphological learning model

MORSEL is an unsupervised morphology learner created for Morpho Challenge 2010. Its input is a wordlist with frequency information for each word, and its output is a morphological analysis for each word. Crucially, it isn't a segmenter; it's a morphological analyzer. Its goal is to learn the morphological grammar of the language and report what morphemes are present in each word.

Using MORSEL

MORSEL is distributed under the GPLv3. You are welcome to use it for performing evaluations, for bootstrapping a supervised learner, or for any other purpose you can think of as long as you comply with the license. Please get in touch with me (http://lignos.org/) if you're using it; while the documentation should be adequate it's always good to hear what applications other people have in mind for this system. I ask that if you use it you cite my Morpho Challenge 2010 paper (BibTeX information below).

Dependencies

  • Java JDK 1.6 or newer (tested with 1.6, 1.7, and 1.8)
  • ant (tested with 1.8.2 and 1.9.4)

Building

Once you've got the dependencies installed, you can simply run ant in the root of the repository and it will compile all source and create morsel.jar. The jar depends on the other jars in /lib (Apache CLI, JUnit, and GNU Trove), so if you move the jar you'll need to move /lib along with it.

Taking it for a spin

You run MORSEL by running the jar and providing:

  • A wordlist formatted with a word and its frequency on each line separated by whitespace, i.e., 69971 the. See data/test/brown_wordlist.txt for an example.
  • An output file where the morphological analyses will be stored. The output will look like this:
accelerate      ACCELERATE
accelerated     ACCELERATE +(ed)
accelerating    ACCELERATE +(ing)
acceleration    ACCELERATE +(ion)
accelerations   ACCELERATE +(ion) +(s)
  • A log file to record what the learner is doing while it learns. If you'd like it to write to standard out, just enter - for the log file.
  • A parameter file to configure the segmenter. There are two files in the params folder corresponding to the parameters used for the Morpho Challenge 2010 evaluation. Both settings (aggressive and conservative) produced state of the art results for English in Morpho Challenge 2010, and aggressive produced state of the art results for Finnish.
  • The rest of the command-line parameters are documented by running java -jar morsel.jar --help. With the exception of encoding, unless you are interested in the algorithm's internals and want more debug output there isn't much to see here. Almost everything you want to set is set via the parameter file, not command-line arguments.

For example, if you want to run on the Brown corpus wordlist, write the analysis to out.txt, write the log to log.txt, and use the conservative parameter set do the following:

java -jar morsel.jar data/test/brown_wordlist.txt out.txt log.txt params/conservative.txt

If you're using a data set of any significant size, you'll want to increase Java's maximum heap size, see Java's Xmx flag.

Evaluating MORSEL

MORSEL was designed for Morpho Challenge, so it's best evaluated using the Morpho Challenge metrics (which you can get from their website) or the EMMA metric. I have three requests for people performing evaluations:

  • MORSEL is a rule-based system for affixal morphology. It is not designed for templatic morphology (e.g., Arabic, Hebrew), so please don't bother evaluating it in those languages. It can be adapted to work in these languages in theory, but it's a fair amount of work.
  • MORSEL is a morphological analyzer. If you are evaluating it in a segmentation task, you'll have to adapt its output as a segmentation, which will distort its performance. If you evaluate in this manner, please make it clear how you adapted it and that the segmentation is derived from MORSEL's output.
  • Please cite the following paper:
 @InProceedings{lignos:2010:MC,
  title={Learning from Unseen Data},
  author={Lignos, Constantine},
  booktitle = {Proceedings of the Morpho Challenge 2010 Workshop},
  year = {2010},
  address = {Helsinki, Finland},
  month = {September 2--3},
  organization = {Aalto University School of Science and Technology},
  pages={35--38},
  editor={Kurimo, Mikko and Virpioja, Sami and Turunen, Ville T.}
}

Design

Overview

Some relevant facts about the design of MORSEL:

  • MORSEL is single-threaded. It would be trivial to parallelize the transform scoring process that happens each iteration of learning, but as MORSEL already runs quite quickly I stuck with the simplicity of a serial implementation.
  • MORSEL uses GNU Trove for high performance hash maps and sets. Because it does not create an additional object for every entry in the hash table, using Trove can result in almost 50% lower memory usage on large data sets in addition to some speed improvements.
  • MORSEL uses a fairly large amount of memory in order to speed learning. It keeps all words and a large set of hypothesized transforms in memory at once. You may need up to 16GB of memory to run MORSEL on the Morpho Challenge 2010 Finnish data set. Small data sets take much less memory; running on the Brown Corpus only uses about 256MB of RAM.

Parameter files

MORSEL's behavior is largely specified via the parameter file specified on the command line. Here is a description of the parameters used. For sensible defaults, see params/conservative.txt. Some of these parameters are difficult to understand without understanding the learning algorithm. I recommend you look at the following papers:

A rule-based unsupervised morphology learning framework
Constantine Lignos, Erwin Chan, Mitchell P. Marcus, and Charles Yang
Working Notes of the 10th Workshop of the Cross-Language Evaluation Forum (CLEF), 2009.

Learning from unseen data
Constantine Lignos
Proceedings of the Morpho Challenge 2010 Workshop, 35-38, 2010.

Learning iteration parameters:

  • max_iter: The maximum number of transforms that will be learned from the corpus. One transform is learned per iteration. Usually, learning stops well before this is reached because other stopping criteria are met.
  • top_affixes: The number of affixes considered in each learning iteration as part of a transform.
  • window_size: The number of transforms that can be vetted in a single iteration. If this number of transforms is reached, learning stops.

Word scoring parameters:

  • frequent_type_threshold: The frequency a word needs to be above to be counted towards the number of types a transform covers. This is useful for excluding extremely rare items. For example, setting this to one excludes all hapax legomena (words only seen once) from the transform selection process.
  • frequent_prob_threshold: Similar to frequent_type_threshold, expect the cutoff is specified as a normalized frequency rather than a raw count.

Transform scoring parameters:

  • reeval: Whether words identified originally as bases can later become derived forms. For example, when set to true, after the pairs (bake, bakes) and (baker, bakers) are identified through the transform ($, s), when the transform (e, er) is learned baker will change from being a base form to a derived one, allowing it to be related to both bake and bakers.
  • score_reeval: Whether pairs that require reeval to be identified should be counted when transforms are scored.
  • doubling: Whether to allow doubling and de-doubling of characters at the point of morpheme concatenation. For example, when doubling is set to true, pin + -ing can be pinning, and bake + -ed can be baked. This allows for some flexibility regarding orthographic conventions that the learner does not explicitly identify.

Transform selection parameters:

  • type_threshold: The minimum number of word pairs a transform needs to model in order to be considered valid. This can be used to prevent the learning of transforms that only apply to a handful of words. (In the final selected parameters, this was set to two, effectively disabling any filtering.)
  • overlap_stem_length: The length of the stem to be used in the stem overlap calculation.
  • overlap_threshold: The threshold for overlap ratio above which a transform is rejected. While this parameter is set, the stem overlap filter never comes into play in any well-behaved data set.
  • precision_threshold: The minimum required segmentation precision for a transform to be accepted.

Weighting parameters:

  • weighted_transforms: Whether the score of transforms should be weighted by the number of characters they add to a word. Setting to true allows for rarer but more substantial transforms to outscore shorter but more frequent ones.
  • weighted_affixes: Whether the score of affixes should be weighted by their length. Setting to true allows for rarer but longer affixes to outscore shorter but more frequent ones.

Pre-processing parameters:

  • hyphenation: Whether to always split words on hyphens.
  • compounding: Whether to split compounds at the end of learning (basic compounding).
  • iter_compounding: Whether to split compounds at the end of every iteration (iterative compounding).
  • aggr_compounding: Whether to use the learned transforms to make transform splitting more aggressive (aggressive compounding).

Word/transform Inference parameters:

  • rule_inference_conservative: Whether to use the transforms learned to infer missing bases (base inference).

Experimental features (features that appear to be implemented correctly but do not improve performance):

  • transform_relations: Whether to filter potential analyses by whether the transforms in the analysis have appeared together in previous analyses.
  • allow_unmod_simplex_word_analysis: Whether to attempt to aggressively segment unmodeled words using the learned transforms.

Implementation-internal parameters:

  • transform_optimization: Whether to dramatically improve performance by keeping persistent data structures across iterations. You want this set to true unless you're debugging unexpected behavior.
  • iteration_analysis: Whether to output the analysis of the lexicon at every single iteration. Generates a large amount of output but is useful for examining the learning trajectory.

External libraries

MORSEL depends on:

  • Apache Commons CLI library for parsing command-line arguments
  • junit for unit testing
  • GNU trove for hash maps and sets

Because Maven was not yet widely adopted by researchers when MORSEL was first developed, JAR files for the dependencies are located in the /lib folder. The JAR built by MORSEL (morsel.jar) will look for these dependencies in the same directory.

Building

The ant buildfile build.xml specifies how MORSEL should be built. Just running ant will perform compilation (compile target) and packaging the JAR (dist target). To run unit tests, run ant test. ant clean cleans all compilation output, including the JAR.

The JAR manifest is set such that the main class edu.upenn.ircs.lignos.morsel.MorphLearner is run when the jar is executed. This is the the only main function in MORSEL.

Enjoy!

Constantine Lignos
Institute for Research in Cognitive Science
Computer and Information Science Department
University of Pennsylvania