sequor 0.3.2 → 0.4.2
raw patch · 10 files changed
+99584/−160 lines, 10 files
Files
- Makefile +27/−1
- README +0/−117
- README.rst +133/−0
- bin/seminer +12/−3
- data/seminer/bbn-wsj-22-rate-0.001-beam-10-iter-10-hash-relationfactory-2013.model too large to diff
- data/seminer/nant.c5.500.topics.classes +99371/−0
- data/seminer/relationfactory-2013.features +18/−0
- lib/seminer/README +4/−33
- lib/seminer/seminer +12/−3
- sequor.cabal +7/−3
Makefile view
@@ -12,7 +12,8 @@ -rm -rf share train-seminer: data/seminer/deu.train.lrec-2010-raw.model \- data/seminer/deu.train.lrec-2010.model + data/seminer/deu.train.lrec-2010.model \+ data/seminer/bbn-wsj-22-rate-0.001-beam-10-iter-10-hash-relationfactory-2013.model data/seminer/deu.train.lrec-2010.model: data/seminer/lrec-2010.features \ data/seminer/deu.train.bio.brown \@@ -56,6 +57,31 @@ cat data/seminer/deu.testa.bio |\ ./bin/augment brown True data/seminer/deu.eci.unlabeled.500+1000.classes\ > data/seminer/deu.testa.bio.brown+++data/seminer/bbn-wsj-22-rate-0.001-beam-10-iter-10-hash-relationfactory-2013.model: \+ data/seminer/relationfactory-2013.features \+ data/seminer/bbn-wsj-02-21.reduced2.brown \+ data/seminer/bbn-wsj-22.reduced2.brown+ ./bin/sequor train data/seminer/relationfactory-2013.features \+ data/seminer/bbn-wsj-02-21.reduced2.brown $@ \+ --rate=0.001 --beam=10 --iter=10 --hash \+ --heldout=data/seminer/bbn-wsj-22.reduced2.brown++data/seminer/bbn-wsj-02-21.reduced2.brown: \+ data/seminer/bbn-wsj-02-21.reduced2 \+ data/seminer/nant.c5.500.topics.classes+ cat data/seminer/bbn-wsj-02-21.reduced2 |\+ ./bin/augment brown True data/seminer/nant.c5.500.topics.classes > $@++data/seminer/bbn-wsj-22.reduced2.brown: \+ data/seminer/bbn-wsj-22.reduced2 \+ data/seminer/nant.c5.500.topics.classes+ cat data/seminer/bbn-wsj-22.reduced2 |\+ ./bin/augment brown True data/seminer/nant.c5.500.topics.classes > $@+++data/seminer/bbn-wsj-22.reduced2.brown: clean: -find . -name '*.o' | xargs rm
− README
@@ -1,117 +0,0 @@-sequor 0.3.0--AUTHOR: Grzegorz Chrupała <gchrupala@lsv.uni-saarland.de>--Sequor is a sequence labeler based on Collins's sequence-perceptron. Sequor has a flexible feature template language and is-meant mainly for NLP applications such as Part of Speech tagging,-syntactic chunking or Named Entity labeling.--This version of Sequor includes SemiNER, a named-entity labeler, with-pre-trained models for German. For details see ./lib/seminer/README- -INSTALLATION--See installation instructions: http://code.google.com/p/sequor/wiki/INSTALL--USAGE--With Sequor you can learn a model from sequences manually annotated-with labels, and then apply this model to new data in order to add-labels. Sequor is meant to be used mainly with linguistic data, for-example to learn Part of Speech tagging, syntactic chunking or Named-Entity labeling.- -Usage: sequor command [OPTION...] [ARG...]-train: train model-train [OPTION...] TEMPLATE-FILE TRAIN-FILE MODEL-FILE - --rate=NUM learning rate- --beam=INT beam size- --iter=INT number of iterations- --min-count=INT minimum feature frequency for label dictionary- --heldout=FILE path to heldout data- --hash use hashing instead of feature dictionary- --hash-sample=INT sample size to estimate number of features when hashing- --hash-max-size=INT maximum size of parameter vector when hashing--predict: predict using model-predict MODEL-FILE --version: print version-version --help: print usage information-help --Data files should be in the UTF-8 encoding.--As an example we can use data annotated with syntactic chunk labels in-the data directory. For example:--./bin/sequor train data/all.features data/train.conll model\- --rate 0.1 --beam 10 --iter 5 --min-count 50 --hash\- --heldout data/devel.conll--./bin/sequor predict model < data/test.conll > data/test.labels--FEATURE TEMPLATE SYNTAX--Sequor uses a small language for specifying feature templates to use-when learing. This section gives an informal overview of this-language. Sequor uses the simple CoNLL format for the input files. In-this format sentences are separated by blank lines. Each line-represents a single token (word). Each token should have the same-fixed number of space-separated fields, where the last field is the-label, e.g.--der d ART I-NC O-Europäischen europäisch ADJA I-NC ORG-Union Union NN I-NC ORG--The template language treats the input sentence as a matrix of-features (i.e. field values) and allows you to select and apply some-transformations to those features.--The language consists of a number predefined functions. By calling the-functions with certain argument you can specify the feature set to-use. As an example consider the following template: -Cat [ Cell 0 0, Suffix 2 (Cell 0 0), Row -1, Row 1 ]. -It specifies the following features: the first field in the current-token, the two-character suffix of the first field of the current-token, all the fields of the previous token and all the fields of the-following token. --Functions:--Cell r c Selects field in row r and column c. -Rect r c r' c Selects all features in the rectangle- whose upper-left corner is in row r column c- and lower-right corner is in row r' column c'.-Row r Selects all features in row r.-MarkNull f If feature does not exist, replace it with a NULL mark- Typically used when absence of feature is significant, - e.g. to mark the beginning of the sentence.-Index f Marks the feature f to use in indexing for label - dictionary.-Cat [f1,f2,...,fn] Selects features in the list.-Cart f f' Creates Cartesian product of feature sets f and f'. - If f and f' are singletons, simply conjoins the - two features.-Lower f Maps f to lower case characters.-Suffix i f Takes suffix of i character length of feature f.-Prefix i f Takes prefix of i character langth of feature f.-WordShape f Creates a specification of which charater classes such - as lower case and upper case letters, digits or - punctuation occur in feature f.--Remarks: Rows are indexed relative to the current token (0). Columns-are indexed starting with 0. Functions which take features as-arguments can be passed either singleton features or sequences of-features. If passed a sequence they are applied to each of its-elements. For example (Suffix 3 (Row 0)) will return the sequence of-features formed by taking the suffix of length 3 of each field of row-0.--For more examples see files all.features and example.features in the-directory data.-
+ README.rst view
@@ -0,0 +1,133 @@+Sequor+======++Sequor is a sequence labeler based on Collins's (2002)+perceptron. Sequor has a flexible feature template language and is+meant mainly for NLP applications such as Named Entity labeling, Part+of Speech tagging or syntactic chunking. It includes the SemiNER named+entity recognizer, with pre-trained models for German and English (see+`Named Entity Recognition (SemiNER)`_).+++Installation+------------++The easiest way to compile and install sequor is to ++1. Install the `Haskell platform <http://www.haskell.org/platform/>`_+2. Run::++ cabal update+ cabal install sequor --prefix=`pwd`++Cabal should then download and install the necessary packages, and+install the sequor binary in ./bin, and the data files in ./share++++Usage+-----+With Sequor you can learn a model from sequences manually annotated+with labels, and then apply this model to new data in order to add+labels. Sequor is meant to be used mainly with linguistic data, for+example to learn Part of Speech tagging, syntactic chunking or Named+Entity labeling::+ + Usage: sequor command [OPTION...] [ARG...]+ train: train model+ train [OPTION...] TEMPLATE-FILE TRAIN-FILE MODEL-FILE + --rate=NUM learning rate+ --beam=INT beam size+ --iter=INT number of iterations+ --min-count=INT minimum feature frequency for label dictionary+ --heldout=FILE path to heldout data+ --hash use hashing instead of feature dictionary+ --hash-sample=INT sample size to estimate number of features when hashing+ --hash-max-size=INT maximum size of parameter vector when hashing++::++ predict: predict using model+ predict MODEL-FILE ++ version: print version+ version ++ help: print usage information+ help ++Data files should be in the UTF-8 encoding.++As an example we can use data annotated with syntactic chunk labels in+the data directory. For example::++ ./bin/sequor train data/all.features data/train.conll model\+ --rate 0.1 --beam 10 --iter 5 --min-count 50 --hash\+ --heldout data/devel.conll++ ./bin/sequor predict model < data/test.conll > data/test.labels++Feature template syntax+-----------------------++Sequor uses a mini language to specify which features to extract from+data. For details see https://bitbucket.org/gchrupala/sequor/wiki/Templates+++Named Entity Recognition (SemiNER)+----------------------------------++Sequor includes the SemiNER named entity recognizer, with pre-trained+models for German and English.++The German model recognizer is trained on the CoNLL 2003 data and+recognizes the following labels:++- PER - people+- ORG - organizations+- LOC - locations such as cities and countries+- MISC - miscellaneous entities such as nationalities ++The German model is described in [Chrupala_and_Klakow_2010]_.++The English model is trained on the BBN Wall Street Journal data and+recognizes the following labels:++* CARDINAL - cardinal number+* DATE - calendar date+* GPE:CITY - city+* GPE:COUNTRY - country+* GPE:STATE_PROVINCE - state or province+* MONEY - currency+* NORP:NATIONALITY - nationality +* NORP:OTHER - +* NORP:POLITICAL - political affiliation +* ORDINAL - ordinal number+* ORGANIZATION - organization+* PERCENT - percentage +* PERSON - people+* QUANTITY - numerical quantity++See https://bitbucket.org/gchrupala/sequor/wiki/SemiNER for usage+information.+++Sequence perceptron+-------------------++Compared to the commonly used Conditional Random Field model, the+Sequence Perceptron algorithm is simpler, more efficient and often has+similar performance.++The sequence perceptron was introduced in [Collins_2002]_.++.. [Collins_2002] Collins, Michael. 2002. Discriminative training+ methods for Hidden Markov Models: Theory and experiments with+ perceptron+ algorithms. EMNLP 2002. http://ir.hit.edu.cn/~car/research/dishmm.pdf+++.. [Chrupala_and_Klakow_2010] Grzegorz Chrupała and Dietrich+ Klakow. 2010. A Named Entity Labeler for German: exploiting+ Wikipedia and distributional+ clusters. LREC. http://grzegorz.chrupala.me/papers/lrec-2010.pdf
bin/seminer view
@@ -1,22 +1,31 @@ #! /bin/bash +BINDIR="$( cd "$( dirname "${BASH_SOURCE[0]}" )" && pwd )"+ if [ -z $SEMINER_ROOT ];-then SEMINER_ROOT=`pwd`+then SEMINER_ROOT=$BINDIR/.. fi case $1 in - "raw")+ "de-raw") tr "\t" ' ' | cut -d' ' -f1 |\ $SEMINER_ROOT/bin/augment brown False \ $SEMINER_ROOT/data/seminer/deu.eci.unlabeled.500+1000.classes |\ $SEMINER_ROOT/bin/sequor predict \ $SEMINER_ROOT/data/seminer/deu.train.lrec-2010-raw.model ;;- "full")+ "de-full") tr "\t" ' ' | cut -d' ' -f1,2,3,4 |\ $SEMINER_ROOT/bin/augment brown False \ $SEMINER_ROOT/data/seminer/deu.eci.unlabeled.500+1000.classes |\ $SEMINER_ROOT/bin/sequor predict \ $SEMINER_ROOT/data/seminer/deu.train.lrec-2010.model+ ;;+ "en")+ tr "\t" ' ' | cut -d' ' -f1 |\+ $SEMINER_ROOT/bin/augment brown False \+ $SEMINER_ROOT/data/seminer/nant.c5.500.topics.classes |\+ $SEMINER_ROOT/bin/sequor predict \+ $SEMINER_ROOT/data/seminer/bbn-wsj-22-rate-0.001-beam-10-iter-10-hash-relationfactory-2013.model ;; esac
+ data/seminer/bbn-wsj-22-rate-0.001-beam-10-iter-10-hash-relationfactory-2013.model view
file too large to diff
+ data/seminer/nant.c5.500.topics.classes view
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vanishing-premium
+ data/seminer/relationfactory-2013.features view
@@ -0,0 +1,18 @@+Cat [ Index (Cell 0 0)+ , Lower (Cell 0 0)+ , Suffix 3 (Cell 0 0)+ , Suffix 2 (Cell 0 0)+ , Suffix 1 (Cell 0 0)+ , WordShape (Cell 0 0)+ , MarkNull (Cell -1 0)+ , Cell -2 0+ , Cell 1 0+ , Cell 2 0+ , Cart (Cell 0 1)+ (Cat [ Lower (Cell 0 0)+ , Suffix 3 (Cell 0 0)+ , Suffix 2 (Cell 0 0)+ , Suffix 1 (Cell 0 0)+ , WordShape (Cell 0 0) + ])+ ]
lib/seminer/README view
@@ -1,36 +1,7 @@ SemiNER - Named Entity Labeler -Grzegorz Chrupala <gchrupala@lsv.uni-saarland.de>--Version 0.3--SemiNER is a tool for Named Entity labeleling. This -release includes models trained on the German CoNLL data with-features extracted from a large unlabeled German corpus.--The easiest way to start using SemiNER is to run the following command-from the top-level sequor directory:--> cabal install --prefix=`pwd`--This assumes that you have already installed the Haskell platform from-http://www.haskell.org/platform--Contact the author me if you encounter problems.--USAGE:--There are two pretrained models: full (which uses all the features-from training data, including lemmas, POS tags and chunk tags) and raw-(which only uses word features and cluster id features. You don't need-to run any additional preprocessing steps to run the raw model.--To label new data using the raw pre-trained model:-> bin/seminer raw < INPUT-FILE > OUTPUT-FILE-To label new data using the full pre-trained model:-> bin/seminer full < INPUT-FILE > OUTPUT-FILE---The CoNLL input format is one token per line, sentences separated by a-blank line.+SemiNER is a tool for Named Entity labeleling which includes+pretrained models for German and English. See+https://bitbucket.org/gchrupala/sequor/wiki/SemiNER for installation+and usage information.
lib/seminer/seminer view
@@ -1,22 +1,31 @@ #! /bin/bash +BINDIR="$( cd "$( dirname "${BASH_SOURCE[0]}" )" && pwd )"+ if [ -z $SEMINER_ROOT ];-then SEMINER_ROOT=`pwd`+then SEMINER_ROOT=$BINDIR/.. fi case $1 in - "raw")+ "de-raw") tr "\t" ' ' | cut -d' ' -f1 |\ $SEMINER_ROOT/bin/augment brown False \ $SEMINER_ROOT/data/seminer/deu.eci.unlabeled.500+1000.classes |\ $SEMINER_ROOT/bin/sequor predict \ $SEMINER_ROOT/data/seminer/deu.train.lrec-2010-raw.model ;;- "full")+ "de-full") tr "\t" ' ' | cut -d' ' -f1,2,3,4 |\ $SEMINER_ROOT/bin/augment brown False \ $SEMINER_ROOT/data/seminer/deu.eci.unlabeled.500+1000.classes |\ $SEMINER_ROOT/bin/sequor predict \ $SEMINER_ROOT/data/seminer/deu.train.lrec-2010.model+ ;;+ "en")+ tr "\t" ' ' | cut -d' ' -f1 |\+ $SEMINER_ROOT/bin/augment brown False \+ $SEMINER_ROOT/data/seminer/nant.c5.500.topics.classes |\+ $SEMINER_ROOT/bin/sequor predict \+ $SEMINER_ROOT/data/seminer/bbn-wsj-22-rate-0.001-beam-10-iter-10-hash-relationfactory-2013.model ;; esac
sequor.cabal view
@@ -1,5 +1,5 @@ Name: sequor-Version: 0.3.2+Version: 0.4.2 Description: A sequence labeler based on Collins's sequence perceptron. Synopsis: A sequence labeler based on Collins's sequence perceptron. Homepage: http://code.google.com/p/sequor/@@ -10,7 +10,7 @@ Build-Type: Simple Cabal-Version: >=1.2 Category: Natural Language Processing-Extra-source-files: README, Makefile, lib/seminer/README,+Extra-source-files: README.rst, Makefile, lib/seminer/README, lib/seminer/seminer, lib/seminer/seminer, bin/seminer Data-dir: data Data-files: all.features, example.features, mlcomp.features,@@ -20,7 +20,11 @@ seminer/lrec-2010-raw.features, seminer/deu.eci.unlabeled.500+1000.classes, seminer/deu.train.lrec-2010.model,- seminer/deu.train.lrec-2010-raw.model+ seminer/deu.train.lrec-2010-raw.model,+ seminer/relationfactory-2013.features,+ seminer/nant.c5.500.topics.classes,+ seminer/bbn-wsj-22-rate-0.001-beam-10-iter-10-hash-relationfactory-2013.model+ Executable sequor Main-is: Main.hs