forked from kristjan/cjvt-srl-tagging
98 lines
4.1 KiB
Markdown
98 lines
4.1 KiB
Markdown
# cjvt-srl-tagging
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We'll be using mate-tools to perform SRL on Kres.
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## workspace
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The tools require Java.
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See `./dockerfiles/mate-tool-env/README.md` for environment preparation.
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## mate-tools
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Using **Full srl pipeline (including anna-3.3)** from the Downloads section.
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Benchmarking the tool for slo and hr: [2] (submodule of this repo).
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Mate-tool for srl tagging can be found in `./tools/srl-20131216/`.
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### train
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Create the `model-file`:
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`--help` output:
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```bash
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java -cp srl.jar se.lth.cs.srl.Learn --help
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Not enough arguments, aborting.
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Usage:
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java -cp <classpath> se.lth.cs.srl.Learn <lang> <input-corpus> <model-file> [options]
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Example:
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java -cp srl.jar:lib/liblinear-1.51-with-deps.jar se.lth.cs.srl.Learn eng ~/corpora/eng/CoNLL2009-ST-English-train.txt eng-srl.mdl -reranker -fdir ~/features/eng -llbinary ~/liblinear-1.6/train
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trains a complete pipeline and reranker based on the corpus and saves it to eng-srl.mdl
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<lang> corresponds to the language and is one of
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chi, eng, ger
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Options:
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-aibeam <int> the size of the ai-beam for the reranker
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-acbeam <int> the size of the ac-beam for the reranker
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-help prints this message
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Learning-specific options:
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-fdir <dir> the directory with feature files (see below)
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-reranker trains a reranker also (not done by default)
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-llbinary <file> a reference to a precompiled version of liblinear,
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makes training much faster than the java version.
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-partitions <int> number of partitions used for the reranker
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-dontInsertGold don't insert the gold standard proposition during
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training of the reranker.
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-skipUnknownPredicates skips predicates not matching any POS-tags from
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the feature files.
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-dontDeleteTrainData doesn't delete the temporary files from training
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on exit. (For debug purposes)
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-ndPipeline Causes the training data and feature mappings to be
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derived in a non-deterministic way. I.e. training the pipeline
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on the same corpus twice does not yield the exact same models.
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This is however slightly faster.
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The feature file dir needs to contain four files with feature sets. See
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the website for further documentation. The files are called
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pi.feats, pd.feats, ai.feats, and ac.feats
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All need to be in the feature file dir, otherwise you will get an error.
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```
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Input: `lang`, `input-corpus`.
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### parse
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`--help` output:
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```bash
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$ java -cp srl.jar se.lth.cs.srl.Parse --help
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Not enough arguments, aborting.
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Usage:
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java -cp <classpath> se.lth.cs.srl.Parse <lang> <input-corpus> <model-file> [options] <output>
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Example:
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java -cp srl.jar:lib/liblinear-1.51-with-deps.jarse.lth.cs.srl.Parse eng ~/corpora/eng/CoNLL2009-ST-English-evaluation-SRLonly.txt eng-srl.mdl -reranker -nopi -alfa 1.0 eng-eval.out
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parses in the input corpus using the model eng-srl.mdl and saves it to eng-eval.out, using a reranker and skipping the predicate identification step
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<lang> corresponds to the language and is one of
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chi, eng, ger
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Options:
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-aibeam <int> the size of the ai-beam for the reranker
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-acbeam <int> the size of the ac-beam for the reranker
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-help prints this message
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Parsing-specific options:
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-nopi skips the predicate identification. This is equivalent to the
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setting in the CoNLL 2009 ST.
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-reranker uses a reranker (assumed to be included in the model)
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-alfa <double> the alfa used by the reranker. (default 1.0)
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```
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We need to provide `lang` (`ger` for German feature functions?), `input-corpus` and `model` (see train).
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### input data:
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* `ssj500k` data found in `./bilateral-srl/data/sl/sl.{test,train}`;
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formatted for mate-tools usage in `./bilaterla-srl/tools/mate-tools/sl.{test,train}.mate` (line counts match);
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## Sources
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* [1] (mate-tools) https://code.google.com/archive/p/mate-tools/
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* [2] (benchmarking) https://github.com/clarinsi/bilateral-srl
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* [3] (conll 2008 paper) http://www.aclweb.org/anthology/W08-2121.pdf
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* [4] (format CoNLL 2009) https://wiki.ufal.ms.mff.cuni.cz/format-conll |