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Commit ddf8a3af authored by calberti's avatar calberti Committed by GitHub
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Scripts and documentation for syntaxnet support for new languages (#308) 

* Adding scripts for tokenization and parsing in new languages.
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syntaxnet/README.md
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......@@ -3,6 +3,10 @@
*A TensorFlow implementation of the models described in [Andor et al. (2016)]
(http://arxiv.org/pdf/1603.06042v1.pdf).*
**Update**: Parsey models are now [available](universal.md) for 40 languages
trained on Universal Dependencies datasets, with support for text segmentation
and morphological analysis.
At Google, we spend a lot of time thinking about how computer systems can read
and understand human language in order to process it in intelligent ways. We are
excited to share the fruits of our research with the broader community by
......
syntaxnet/syntaxnet/models/parsey_universal/context-tokenize-zh.pbtxt 0 → 100644
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Parameter {
name: "brain_tokenizer_zh_embedding_dims"
value: "32;32"
}
Parameter {
name: "brain_tokenizer_zh_embedding_names"
value: "chars;words"
}
Parameter {
name: "brain_tokenizer_zh_features"
value: "input.char "
"input(1).char "
"input(2).char "
"input(3).char "
"input(-1).char "
"input(-2).char "
"input(-3).char "
"stack.char "
"stack.offset(1).char "
"stack.offset(-1).char "
"stack(1).char "
"stack(1).offset(1).char "
"stack(1).offset(-1).char "
"stack(2).char; "
"last-word(1,min-freq=2) "
"last-word(2,min-freq=2) "
"last-word(3,min-freq=2)"
}
Parameter {
name: "brain_tokenizer_zh_transition_system"
value: "binary-segment-transitions"
}
input {
name: "word-map"
Part {
file_pattern: "last-word-map"
}
}
input {
name: "char-map"
Part {
file_pattern: "char-map"
}
}
input {
name: "label-map"
Part {
file_pattern: "label-map"
}
}
input {
name: 'stdin-untoken'
record_format: 'untokenized-text'
Part {
file_pattern: '-'
}
}
input {
name: 'stdout-conll'
record_format: 'conll-sentence'
Part {
file_pattern: '-'
}
}
syntaxnet/syntaxnet/models/parsey_universal/context.pbtxt 0 → 100644
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Parameter {
name: "brain_tokenizer_embedding_dims"
value: "16;16;16"
}
Parameter {
name: "brain_tokenizer_embedding_names"
value: "chars;digits;puncts"
}
Parameter {
name: "brain_tokenizer_features"
value: "input.char "
"input(-1).char "
"input(1).char; "
"input.digit "
"input(-1).digit "
"input(1).digit; "
"input.punctuation-amount "
"input(-1).punctuation-amount "
"input(1).punctuation-amount "
}
Parameter {
name: "brain_tokenizer_transition_system"
value: "binary-segment-transitions"
}
Parameter {
name: "brain_morpher_embedding_dims"
value: "2;16;8;16;16;16;16;16;64"
}
Parameter {
name: "brain_morpher_embedding_names"
value: "capitalization;char_ngram;other;prefix2;prefix3;suffix2;suffix3;tags;words"
}
Parameter {
name: "brain_morpher_features"
value: "input.capitalization "
"input(1).capitalization "
"input(2).capitalization "
"input(3).capitalization "
"input(-1).capitalization "
"input(-2).capitalization "
"input(-3).capitalization "
"input(-4).capitalization; "
"input.token.char-ngram "
"input(1).token.char-ngram "
"input(2).token.char-ngram "
"input(3).token.char-ngram "
"input(-1).token.char-ngram "
"input(-2).token.char-ngram "
"input(-3).token.char-ngram "
"input(-4).token.char-ngram; "
"input.digit "
"input.hyphen "
"input.token.punctuation-amount "
"input.token.quote; "
"input.token.prefix(length=2) "
"input(1).token.prefix(length=2) "
"input(2).token.prefix(length=2) "
"input(3).token.prefix(length=2) "
"input(-1).token.prefix(length=2) "
"input(-2).token.prefix(length=2) "
"input(-3).token.prefix(length=2) "
"input(-4).token.prefix(length=2); "
"input.token.prefix(length=3) "
"input(1).token.prefix(length=3) "
"input(2).token.prefix(length=3) "
"input(3).token.prefix(length=3) "
"input(-1).token.prefix(length=3) "
"input(-2).token.prefix(length=3) "
"input(-3).token.prefix(length=3) "
"input(-4).token.prefix(length=3); "
"input.token.suffix(length=2) "
"input(1).token.suffix(length=2) "
"input(2).token.suffix(length=2) "
"input(3).token.suffix(length=2) "
"input(-1).token.suffix(length=2) "
"input(-2).token.suffix(length=2) "
"input(-3).token.suffix(length=2) "
"input(-4).token.suffix(length=2); "
"input.token.suffix(length=3) "
"input(1).token.suffix(length=3) "
"input(2).token.suffix(length=3) "
"input(3).token.suffix(length=3) "
"input(-1).token.suffix(length=3) "
"input(-2).token.suffix(length=3) "
"input(-3).token.suffix(length=3) "
"input(-4).token.suffix(length=3); "
"input(-1).pred-morph-tag "
"input(-2).pred-morph-tag "
"input(-3).pred-morph-tag "
"input(-4).pred-morph-tag; "
"input.token.word "
"input(1).token.word "
"input(2).token.word "
"input(3).token.word "
"input(-1).token.word "
"input(-2).token.word "
"input(-3).token.word "
"input(-4).token.word"
}
Parameter {
name: "brain_morpher_transition_system"
value: "morpher"
}
Parameter {
name: "brain_tagger_embedding_dims"
value: "2;16;8;16;16;16;16;16;64"
}
Parameter {
name: "brain_tagger_embedding_names"
value: "capitalization;char_ngram;other;prefix2;prefix3;suffix2;suffix3;tags;words"
}
Parameter {
name: "brain_tagger_features"
value: "input.capitalization "
"input(1).capitalization "
"input(2).capitalization "
"input(3).capitalization "
"input(-1).capitalization "
"input(-2).capitalization "
"input(-3).capitalization "
"input(-4).capitalization; "
"input.token.char-ngram "
"input(1).token.char-ngram "
"input(2).token.char-ngram "
"input(3).token.char-ngram "
"input(-1).token.char-ngram "
"input(-2).token.char-ngram "
"input(-3).token.char-ngram "
"input(-4).token.char-ngram; "
"input.digit "
"input.hyphen "
"input.token.punctuation-amount "
"input.token.quote; "
"input.token.prefix(length=2) "
"input(1).token.prefix(length=2) "
"input(2).token.prefix(length=2) "
"input(3).token.prefix(length=2) "
"input(-1).token.prefix(length=2) "
"input(-2).token.prefix(length=2) "
"input(-3).token.prefix(length=2) "
"input(-4).token.prefix(length=2); "
"input.token.prefix(length=3) "
"input(1).token.prefix(length=3) "
"input(2).token.prefix(length=3) "
"input(3).token.prefix(length=3) "
"input(-1).token.prefix(length=3) "
"input(-2).token.prefix(length=3) "
"input(-3).token.prefix(length=3) "
"input(-4).token.prefix(length=3); "
"input.token.suffix(length=2) "
"input(1).token.suffix(length=2) "
"input(2).token.suffix(length=2) "
"input(3).token.suffix(length=2) "
"input(-1).token.suffix(length=2) "
"input(-2).token.suffix(length=2) "
"input(-3).token.suffix(length=2) "
"input(-4).token.suffix(length=2); "
"input.token.suffix(length=3) "
"input(1).token.suffix(length=3) "
"input(2).token.suffix(length=3) "
"input(3).token.suffix(length=3) "
"input(-1).token.suffix(length=3) "
"input(-2).token.suffix(length=3) "
"input(-3).token.suffix(length=3) "
"input(-4).token.suffix(length=3); "
"input(-1).pred-tag "
"input(-2).pred-tag "
"input(-3).pred-tag "
"input(-4).pred-tag; "
"input.token.word "
"input(1).token.word "
"input(2).token.word "
"input(3).token.word "
"input(-1).token.word "
"input(-2).token.word "
"input(-3).token.word "
"input(-4).token.word"
}
Parameter {
name: "brain_tagger_transition_system"
value: "tagger"
}
Parameter {
name: "brain_parser_embedding_dims"
value: "32;32;32;64"
}
Parameter {
name: "brain_parser_embedding_names"
value: "labels;morphology;tags;words"
}
Parameter {
name: "brain_parser_features"
value: "stack.child(1).label "
"stack.child(1).sibling(-1).label "
"stack.child(-1).label "
"stack.child(-1).sibling(1).label "
"stack.child(2).label "
"stack.child(-2).label "
"stack(1).child(1).label "
"stack(1).child(1).sibling(-1).label "
"stack(1).child(-1).label "
"stack(1).child(-1).sibling(1).label "
"stack(1).child(2).label "
"stack(1).child(-2).label; "
"input.token.morphology-set "
"input(1).token.morphology-set "
"input(2).token.morphology-set "
"input(3).token.morphology-set "
"stack.token.morphology-set "
"stack.child(1).token.morphology-set "
"stack.child(1).sibling(-1).token.morphology-set "
"stack.child(-1).token.morphology-set "
"stack.child(-1).sibling(1).token.morphology-set "
"stack.child(2).token.morphology-set "
"stack.child(-2).token.morphology-set "
"stack(1).token.morphology-set "
"stack(1).child(1).token.morphology-set "
"stack(1).child(1).sibling(-1).token.morphology-set "
"stack(1).child(-1).token.morphology-set "
"stack(1).child(-1).sibling(1).token.morphology-set "
"stack(1).child(2).token.morphology-set "
"stack(1).child(-2).token.morphology-set "
"stack(2).token.morphology-set "
"stack(3).token.morphology-set; "
"input.token.tag "
"input(1).token.tag "
"input(2).token.tag "
"input(3).token.tag "
"stack.token.tag "
"stack.child(1).token.tag "
"stack.child(1).sibling(-1).token.tag "
"stack.child(-1).token.tag "
"stack.child(-1).sibling(1).token.tag "
"stack.child(2).token.tag "
"stack.child(-2).token.tag "
"stack(1).token.tag "
"stack(1).child(1).token.tag "
"stack(1).child(1).sibling(-1).token.tag "
"stack(1).child(-1).token.tag "
"stack(1).child(-1).sibling(1).token.tag "
"stack(1).child(2).token.tag "
"stack(1).child(-2).token.tag "
"stack(2).token.tag "
"stack(3).token.tag; "
"input.token.word "
"input(1).token.word "
"input(2).token.word "
"input(3).token.word "
"stack.token.word "
"stack.child(1).token.word "
"stack.child(1).sibling(-1).token.word "
"stack.child(-1).token.word "
"stack.child(-1).sibling(1).token.word "
"stack.child(2).token.word "
"stack.child(-2).token.word "
"stack(1).token.word "
"stack(1).child(1).token.word "
"stack(1).child(1).sibling(-1).token.word "
"stack(1).child(-1).token.word "
"stack(1).child(-1).sibling(1).token.word "
"stack(1).child(2).token.word "
"stack(1).child(-2).token.word "
"stack(2).token.word "
"stack(3).token.word "
}
Parameter {
name: "brain_parser_transition_system"
value: "arc-standard"
}
Parameter {
name: "join_category_to_pos"
value: "true"
}
input {
name: "word-map"
Part {
file_pattern: "word-map"
}
}
input {
name: "char-map"
Part {
file_pattern: "char-map"
}
}
input {
name: "tag-map"
Part {
file_pattern: "tag-map"
}
}
input {
name: "tag-to-category"
Part {
file_pattern: "tag-to-category"
}
}
input {
name: "label-map"
Part {
file_pattern: "label-map"
}
}
input {
name: "char-ngram-map"
Part {
file_pattern: "char-ngram-map"
}
}
input {
name: "prefix-table"
Part {
file_pattern: "prefix-table"
}
}
input {
name: "suffix-table"
Part {
file_pattern: "suffix-table"
}
}
input {
name: "morph-label-set"
Part {
file_pattern: "morph-label-set"
}
}
input {
name: "morphology-map"
Part {
file_pattern: "morphology-map"
}
}
input {
name: 'stdin'
record_format: 'tokenized-text'
Part {
file_pattern: '-'
}
}
input {
name: 'stdin-conll'
record_format: 'conll-sentence'
Part {
file_pattern: '-'
}
}
input {
name: 'stdin-untoken'
record_format: 'untokenized-text'
Part {
file_pattern: '-'
}
}
input {
name: 'stdout-conll'
record_format: 'conll-sentence'
Part {
file_pattern: '-'
}
}
syntaxnet/syntaxnet/models/parsey_universal/parse.sh 0 → 100755
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# A script that runs a morphological analyzer, a part-of-speech tagger and a
# dependency parser on a text file, with one sentence per line.
#
# Example usage:
# bazel build syntaxnet:parser_eval
# cat sentences.txt |
# syntaxnet/models/parsey_universal/parse.sh \
# $MODEL_DIRECTORY > output.conll
#
# To run on a conll formatted file, add the --conll command line argument:
# cat sentences.conll |
# syntaxnet/models/parsey_universal/parse.sh \
# --conll $MODEL_DIRECTORY > output.conll
#
# Models can be downloaded from
# http://download.tensorflow.org/models/parsey_universal/<language>.zip
# for the languages listed at
# https://github.com/tensorflow/models/blob/master/syntaxnet/universal.md
#
PARSER_EVAL=bazel-bin/syntaxnet/parser_eval
CONTEXT=syntaxnet/models/parsey_universal/context.pbtxt
if [[ "$1" == "--conll" ]]; then
INPUT_FORMAT=stdin-conll
shift
else
INPUT_FORMAT=stdin
fi
MODEL_DIR=$1
$PARSER_EVAL \
--input=$INPUT_FORMAT \
--output=stdout-conll \
--hidden_layer_sizes=64 \
--arg_prefix=brain_morpher \
--graph_builder=structured \
--task_context=$CONTEXT \
--resource_dir=$MODEL_DIR \
--model_path=$MODEL_DIR/morpher-params \
--slim_model \
--batch_size=1024 \
--alsologtostderr \
| \
$PARSER_EVAL \
--input=stdin-conll \
--output=stdout-conll \
--hidden_layer_sizes=64 \
--arg_prefix=brain_tagger \
--graph_builder=structured \
--task_context=$CONTEXT \
--resource_dir=$MODEL_DIR \
--model_path=$MODEL_DIR/tagger-params \
--slim_model \
--batch_size=1024 \
--alsologtostderr \
| \
$PARSER_EVAL \
--input=stdin-conll \
--output=stdout-conll \
--hidden_layer_sizes=512,512 \
--arg_prefix=brain_parser \
--graph_builder=structured \
--task_context=$CONTEXT \
--resource_dir=$MODEL_DIR \
--model_path=$MODEL_DIR/parser-params \
--slim_model \
--batch_size=1024 \
--alsologtostderr
syntaxnet/syntaxnet/models/parsey_universal/tokenize.sh 0 → 100755
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# A script that runs a tokenizer on a text file with one sentence per line.
#
# Example usage:
# bazel build syntaxnet:parser_eval
# cat untokenized-sentences.txt |
# syntaxnet/models/parsey_universal/tokenize.sh \
# $MODEL_DIRECTORY > output.conll
#
# Models can be downloaded from
# http://download.tensorflow.org/models/parsey_universal/<language>.zip
# for the languages listed at
# https://github.com/tensorflow/models/blob/master/syntaxnet/universal.md
#
PARSER_EVAL=bazel-bin/syntaxnet/parser_eval
CONTEXT=syntaxnet/models/parsey_universal/context.pbtxt
INPUT_FORMAT=stdin-untoken
MODEL_DIR=$1
$PARSER_EVAL \
--input=$INPUT_FORMAT \
--output=stdin-untoken \
--hidden_layer_sizes=128,128 \
--arg_prefix=brain_tokenizer \
--graph_builder=greedy \
--task_context=$CONTEXT \
--resource_dir=$MODEL_DIR \
--model_path=$MODEL_DIR/tokenizer-params \
--batch_size=32 \
--alsologtostderr \
--slim_model
syntaxnet/syntaxnet/models/parsey_universal/tokenize_zh.sh 0 → 100755
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# A script that runs a traditional Chinese tokenizer on a text file with one
# sentence per line.
#
# Example usage:
# bazel build syntaxnet:parser_eval
# cat untokenized-sentences.txt |
# syntaxnet/models/parsey_universal/tokenize_zh.sh \
# $MODEL_DIRECTORY > output.conll
#
# The traditional Chinese model can be downloaded from
# http://download.tensorflow.org/models/parsey_universal/Chinese.zip
#
PARSER_EVAL=bazel-bin/syntaxnet/parser_eval
CONTEXT=syntaxnet/models/parsey_universal/context-tokenize-zh.pbtxt
INPUT_FORMAT=stdin-untoken
MODEL_DIR=$1
$PARSER_EVAL \
--input=$INPUT_FORMAT \
--output=stdin-untoken \
--hidden_layer_sizes=256,256 \
--arg_prefix=brain_tokenizer_zh \
--graph_builder=structured \
--task_context=$CONTEXT \
--resource_dir=$MODEL_DIR \
--model_path=$MODEL_DIR/tokenizer-params \
--batch_size=1024 \
--alsologtostderr \
--slim_model
syntaxnet/universal.md 0 → 100644
View file @ ddf8a3af
# Parsey Universal.
A collection of pretrained syntactic models is now available for download at
`http://download.tensorflow.org/models/<language>.zip`
After downloading and unzipping a model, you can run it similarly to
Parsey McParseface with:
```shell
MODEL_DIRECTORY=/where/you/unzipped/the/model/files
cat sentences.txt | syntaxnet/models/parsey_universal/parse.sh \
$MODEL_DIRECTORY > output.conll
```
These models are trained on
[Universal Dependencies](http://universaldependencies.org/) datasets v1.3.
The following table shows their accuracy on Universal
Dependencies test sets for different types of annotations.
Language | No. tokens | POS | fPOS | Morph | UAS | LAS
-------- | :--: | :--: | :--: | :--: | :--: | :--: | :--:
Ancient_Greek-PROIEL | 18502 | 97.14% | 96.97% | 89.77% | 78.74% | 73.15%
Ancient_Greek | 25251 | 93.22% | 84.22% | 90.01% | 68.98% | 62.07%
Arabic | 28268 | 95.65% | 91.03% | 91.23% | 81.49% | 75.82%
Basque | 24374 | 94.88% | - | 87.82% | 78.00% | 73.36%
Bulgarian | 15734 | 97.71% | 95.14% | 94.61% | 89.35% | 85.01%
Catalan | 59503 | 98.06% | 98.06% | 97.56% | 90.47% | 87.64%
Chinese | 12012 | 91.32% | 90.89% | 98.76% | 76.71% | 71.24%
Croatian | 4125 | 94.67% | - | 86.69% | 80.65% | 74.06%
Czech-CAC | 10862 | 98.11% | 92.43% | 91.43% | 87.28% | 83.44%
Czech-CLTT | 4105 | 95.79% | 87.36% | 86.33% | 77.34% | 73.40%
Czech | 173920 | 98.12% | 93.76% | 93.13% | 89.47% | 85.93%
Danish | 5884 | 95.28% | - | 95.24% | 79.84% | 76.34%
Dutch-LassySmall | 4562 | 95.62% | - | 95.44% | 81.63% | 78.08%
Dutch | 5843 | 89.89% | 86.03% | 89.12% | 77.70% | 71.21%
English-LinES | 8481 | 95.34% | 93.11% | - | 81.50% | 77.37%
English | 25096 | 90.48% | 89.71% | 91.30% | 84.79% | 80.38%
Estonian | 23670 | 95.92% | 96.76% | 92.73% | 83.10% | 78.83%
Finnish-FTB | 16286 | 93.50% | 91.15% | 92.44% | 84.97% | 80.48%
Finnish | 9140 | 94.78% | 95.84% | 92.42% | 83.65% | 79.60%
French | 7018 | 96.27% | - | 96.05% | 84.68% | 81.05%
Galician | 29746 | 96.81% | 96.14% | - | 84.48% | 81.35%
German | 16268 | 91.79% | - | - | 79.73% | 74.07%
Gothic | 5158 | 95.58% | 96.03% | 87.32% | 79.33% | 71.69%
Greek | 5668 | 97.48% | 97.48% | 92.70% | 83.68% | 79.99%
Hebrew | 12125 | 95.04% | 95.04% | 92.05% | 84.61% | 78.71%
Hindi | 35430 | 96.45% | 95.77% | 90.98% | 93.04% | 89.32%
Hungarian | 4235 | 94.00% | - | 75.68% | 78.75% | 71.83%
Indonesian | 11780 | 92.62% | - | - | 80.03% | 72.99%
Irish | 3821 | 91.34% | 89.95% | 77.07% | 74.51% | 66.29%
Italian | 10952 | 97.31% | 97.18% | 97.27% | 89.81% | 87.13%
Kazakh | 587 | 75.47% | 75.13% | - | 58.09% | 43.95%
Latin-ITTB | 6548 | 97.98% | 92.68% | 93.52% | 84.22% | 81.17%
Latin-PROIEL | 14906 | 96.50% | 96.08% | 88.39% | 77.60% | 70.98%
Latin | 4832 | 88.04% | 74.07% | 76.03% | 56.00% | 45.80%
Latvian | 3985 | 80.95% | 66.60% | 73.60% | 58.92% | 51.47%
Norwegian | 30034 | 97.44% | - | 95.58% | 88.61% | 86.22%
Old_Church_Slavonic | 5079 | 96.50% | 96.28% | 89.43% | 84.86% | 78.85%
Persian | 16022 | 96.20% | 95.72% | 95.90% | 84.42% | 80.28%
Polish | 7185 | 95.05% | 85.83% | 86.12% | 88.30% | 82.71%
Portuguese-BR | 29438 | 97.07% | 97.07% | 99.91% | 87.91% | 85.44%
Portuguese | 6262 | 96.81% | 90.67% | 94.22% | 85.12% | 81.28%
Romanian | 18375 | 95.26% | 91.66% | 91.98% | 83.64% | 75.36%
Russian-SynTagRus | 107737 | 98.27% | - | 94.91% | 91.68% | 87.44%
Russian | 9573 | 95.27% | 95.02% | 87.75% | 81.75% | 77.71%
Slovenian-SST | 2951 | 90.00% | 84.48% | 84.38% | 65.06% | 56.96%
Slovenian | 14063 | 96.22% | 90.46% | 90.35% | 87.71% | 84.60%
Spanish-AnCora | 53594 | 98.28% | 98.28% | 97.82% | 89.26% | 86.50%
Spanish | 7953 | 95.27% | - | 95.74% | 85.06% | 81.53%
Swedish-LinES | 8228 | 96.00% | 93.77% | - | 81.38% | 77.21%
Swedish | 20377 | 96.27% | 94.13% | 94.14% | 83.84% | 80.28%
Tamil | 1989 | 79.29% | 71.79% | 75.97% | 64.45% | 55.35%
Turkish | 8616 | 93.63% | 92.62% | 86.79% | 82.00% | 71.37%
**Average** | - | 94.27% | 92.93% | 90.38% | 81.12% | 75.85%
These results are obtained using gold text segmentation. Accuracies are measured
over all tokens, including punctuation. `POS`, `fPOS` are coarse and fine
part-of-speech tagging accuracies. `Morph` is full-token accuracy of predicted
morphological attributes. `UAS` and `LAS` are unlabeled and labeled attachment
scores.
Many of these models also support text segmentation, with:
```shell
MODEL_DIRECTORY=/where/you/unzipped/the/model/files
cat sentences.txt | syntaxnet/models/parsey_universal/tokenize.sh \
$MODEL_DIRECTORY > output.conll
```
Text segmentation is currently available for:
`Bulgarian`, `Czech`, `German`, `Greek`, `English`, `Spanish`, `Estonian`,
`Basque`, `Persian`, `Finnish`, `Finnish-FTB`, `French`, `Galician`,
`Ancient_Greek`, `Ancient_Greek-PROIEL`, `Hebrew`, `Hindi`, `Croatian`,
`Hungarian`, `Indonesian`, `Italian`, `Latin`, `Latin-PROIEL`, `Dutch`,
`Norwegian`, `Polish`, `Portuguese`, `Slovenian`, `Swedish`, `Tamil`.
For `Chinese` (traditional) we use a larger text segmentation
model, which can be run with:
```shell
MODEL_DIRECTORY=/where/you/unzipped/the/model/files
cat sentences.txt | syntaxnet/models/parsey_universal/tokenize_zh.sh \
$MODEL_DIRECTORY > output.conll
```
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