diff --git a/Makefile b/Makefile --- a/Makefile +++ b/Makefile @@ -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 diff --git a/README b/README deleted file mode 100644 --- a/README +++ /dev/null @@ -1,117 +0,0 @@ -sequor 0.3.0 - -AUTHOR: Grzegorz Chrupała - -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. - diff --git a/README.rst b/README.rst new file mode 100644 --- /dev/null +++ b/README.rst @@ -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 `_ +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 diff --git a/bin/seminer b/bin/seminer --- a/bin/seminer +++ b/bin/seminer @@ -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 diff --git a/data/seminer/bbn-wsj-22-rate-0.001-beam-10-iter-10-hash-relationfactory-2013.model b/data/seminer/bbn-wsj-22-rate-0.001-beam-10-iter-10-hash-relationfactory-2013.model new file mode 100644 # file too large to diff: data/seminer/bbn-wsj-22-rate-0.001-beam-10-iter-10-hash-relationfactory-2013.model diff --git a/data/seminer/nant.c5.500.topics.classes b/data/seminer/nant.c5.500.topics.classes new file mode 100644 --- /dev/null +++ b/data/seminer/nant.c5.500.topics.classes @@ -0,0 +1,99371 @@ +0 0 allocation +0 0 bare +0 0 Month +0 0 color +0 0 poverty +0 0 airplane +0 0 factions +0 0 unemployed +0 0 deadline +0 0 repayment +0 0 label +0 0 overwhelming +0 0 wealthy +0 0 rape +0 0 pipeline +0 0 earth +0 0 Bulls +0 0 Honolulu +0 0 trash +0 0 entrepreneur +0 0 Comptroller +0 0 grass +0 0 Mars +0 0 deterioration +0 0 G-7 +0 0 brightest +0 0 integrity +0 0 18th +0 0 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99 value-priced +99 99 vanishing-premium diff --git a/data/seminer/relationfactory-2013.features b/data/seminer/relationfactory-2013.features new file mode 100644 --- /dev/null +++ b/data/seminer/relationfactory-2013.features @@ -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) + ]) + ] diff --git a/lib/seminer/README b/lib/seminer/README --- a/lib/seminer/README +++ b/lib/seminer/README @@ -1,36 +1,7 @@ SemiNER - Named Entity Labeler -Grzegorz Chrupala - -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. diff --git a/lib/seminer/seminer b/lib/seminer/seminer --- a/lib/seminer/seminer +++ b/lib/seminer/seminer @@ -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 diff --git a/sequor.cabal b/sequor.cabal --- a/sequor.cabal +++ b/sequor.cabal @@ -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