HunTag3 - A sequential tagger for NLP combining the Scikit-learn/LinearRegressionClassifier linear classifier and Hidden Markov Models
Based on training data, HunTag3 can perform any kind of sequential sentence
tagging and has been used for NP chunking and Named Entity Recognition for English and Hungarian.
HungTag3 is the official successor of HunTag project. A previous version of the code is stored at https://github.com/ppke-nlpg/HunTag3 . (See git tags for past major milestones.)
- Using NumPy/SciPy.sparse arrays
- Runabble YAML configuration
- Independent unigram model from SciKit-learn (LinearRegressionClassifier)
- Selectable bi- or trigram transition model (Can be trained separately)
- Features written in native Python code and lexicons are enabled
- Can work with featurized input and can generate featurized output for other taggers
- Consumes minimal memory possible
- NLTK.classify.naivebayes-like Most Informative Features function for examining features quality
- Able to write the unigram feature weights
- Uses the xtsv framework for input processing
- REST API (through xtsv)
- See requirements.txt (pip install -r requirements.txt)
- Optional: CRFsuite
- Minimum 8 GB RAM recomended for training
The authors recommend using HunTag3 in emtsv the new version of e-magyar language processing system. This module is called emChunk/emNER.
Download and install the wheel package or use this repository with the following commands:
git clone --recurse-submodules https://github.com/dlt-rilmta/HunTag3.gitcd HunTag3pip3 install -r requirements.txt- Run the tests in Makefile for examples (e.g.
make test)
- Input data must be a tab-separated file (TSV) with one word per line
- The first line must be the header which contain the names of the fields reqired by the used config
- An empty line to mark sentence boundaries
- Each line must contain the same number of fields
- The column name containing correct/desired label for each word must be set from the CLI or by argument
- The output label is placed to the last column with the specified column name
- The label may be in the BI format used at CoNLL shared tasks (e. g. B-NP to mark the first word of a noun phrase, I-NP to mark the rest and O to mark words outside an NP)
- Or in the so-called BIE1 format which has a seperate symbol for words constituting a chunk themselves (1-NP) and one for the last words of multi-word phrases (E-NP)
- The first two characters of labels should always conform to one of these two conventions, the rest may be any string describing the category
- If one wants to convert between these formats or want to check the data for incorrect label order it is recommended to use emIOBUtils
The flexibility of Huntag comes from the fact that it will generate any kind of features from the input data given the appropriate python functions (please refer to features.py and the config files).
Several dozens of features used regularly in NLP tasks are already implemented in the file features.py, however the user is encouraged to add any number of her own.
Once the desired features are implemented, a data set and a configuration file containing the list of feature functions to be used are all HunTag needs (not counting the data) to perform training and tagging.
The configuration file lists the features that are to be used for a given task. The feature file may start with a command specifying the default radius for features. This is non-mandatory. Example:
default: cutoff: 1 # 1 if not set radius: 5 # -1 if not set
There are three type of features:
- Sentence: The input is aggregated sentence-wise into a list, and this list is then passed to the feature function. This function should return a list consisting of one feature string for each of the tokens of the sentence
- Token: Get a token, and returns a feature list or bool for each given token independently
- Lexicon: The specified token field is matched against this lexicon file and ubstitutes each token with its features from the lexicon
For each feature mandatory fields are the following:
- name: name of the feature as appears in the output (featurized input, most-informative-features, etc.)
- type: sentence/token/lexicon
- action_name: Refers to the features.py function name or the lexicon file
- fields: Refers to the field names of the input (must present in the first line of the input), that the feature use (only sentence type features allowed to have more values here separated by comma. Lexicon features must supply the field of the token here)
- radius: (Only for sentence type features) add the features of each corresponding token to the feature list of all the token its given length radius (independent from the feature)
- options: (Only for sentence type features) Here one can enumerate all options that the corresponding feature need (see feature documentation in features.py)
See configs folder for examples on the format.
HunTag may be run in any of the following modes (see the Makefile for overview and python3 -m huntag --help for details):
Used to train a model or just featurize given a training corpus with a set of feature functions. When run in TRAIN mode, HunTag creates three files, one containing the model and two listing features and labels and the integers they are mapped to when passed to the learner. With the --model option set to NAME, the three files will be stored under NAME.model, NAME.featureNumbers.gz and NAME.labelNumbers.gz respectively.
cat TRAINING_DATA | python3 -m huntag train OPTION
or
python3 -m huntag train -i TRAINING_DATA OPTIONS
Mandatory options:
- -c FILE, --config-file=FILE
- read feature configuration from FILE
- -m NAME, --model=NAME
- name of model and lists
Non-mandatory options:
- -i INPUT, --input=INPUT
- input is taken from INPUT file instead of STDIN
- -g NAME, --gold-tag-field=NAME
- specifies the name of the column containing the gold labels
- --input-featurized
- if set the input is handled as it is already featurized (first column is the label, the other columns are features, no need for header)
Used to train a transition model (from a bigram or trigram language model) using a given field of the training data
cat TRAINING_DATA | python3 -m huntag transmodel-train OPTIONS
or
python3 -m huntag transmodel-train -i TRAINING_DATA OPTIONS
Mandatory options:
- -m NAME, --model=NAME
- name of model file and lists
- --trans-model-order [2 or 3, default: 3]
- order of the transition model (bigram or trigram)
Non-mandatory options:
- -i INPUT, --input=INPUT
- input is taken from INPUT file instead of STDIN
- -g NAME, --gold-tag-field=NAME
- specifies the name of the column containing the gold labels
- --input-featurized
- if set the input is handled as it is already featurized (first column is the label, the other columns are features, no need for header)
Used to tag or just featurize the input. Given a maxent model providing the value P(l|w) for all labels l and words (set of feature values) w, and a transition model supplying P(l|l0) for all pairs of labels, HunTag will assign to each sentence the most likely label sequence.
cat INPUT | python3 -m huntag tag OPTIONS
or
python3 -m huntag tag -i INPUT OPTIONS
Mandatory options:
- -m NAME, --model=NAME
- name of model file and lists
- -c FILE, --config-file=FILE
- read feature configuration from FILE
Non-mandatory options:
- -l L, --language-model-weight=L
- set weight of the language model to L (default is 1)
- -i INPUT, --input=INPUT
- input is taken from INPUT file instead of STDIN
- -o OUTPUT, --output=OUTPUT
- output is written to OUTPUT file instead of STDOUT
- -t NAME, --label-tag-field=NAME
- specifies the name of the column containing the gold labels
- --input-featurized
- if set the input is handled as it is already featurized (first column is the label, the other columns are features, no need for header)
Generates a feature ranking by counting label probabilities (for each label) and frequency per feature (correlations with labels) and sort them in decreasing order of confidence and frequency. This output is usefull for inspecting features quality.
cat TRAINING_DATA | python3 -m huntag most-informative-features OPTIONS > modelName.most_informative_features
or
python3 -m huntag most-informative-features -i TRAINING_DATA OPTIONS
Mandatory options:
- -m NAME, --model=NAME
- name of model file and lists
- -c FILE, --config-file=FILE
- read feature configuration from FILE
Non-mandatory options:
- -i INPUT, --input=INPUT
- input is taken from INPUT file instead of STDIN
- -o OUTPUT, --output=OUTPUT
- output is written to OUTPUT file instead of STDOUT
- -g NAME, --gold-tag-field=NAME
- specifies the name of the column containing the gold labels
- --input-featurized
- if set the input is handled as it is already featurized (first column is the label, the other columns are features, no need for header)
Usefull for inspecting feature weights (per label) assigned by the MaxEnt learner. (As name suggests, training must happen before tagging.)
Negative weights mean negative correlation, which is also usefull.
python3 -m huntag tag --print-weights N OPTIONS > modelName.modelWeights
or
python3 -m huntag tag --print-weights N OPTIONS -o modelName.modelWeights
Mandatory options:
- N is the number of features to print (default: 100)
- -m NAME, --model=NAME
- name of model file and lists
- -c FILE, --config-file=FILE
- read feature configuration from FILE
Non-mandatory options:
- -o OUTPUT, --output=OUTPUT
- output is written to OUTPUT file instead of STDOUT
This options generate suitable input for CRFsuite from training and tagging data. Model name is required as the features and labels are translated to numbers and back. CRFsuite use its own bigram model.
A 100 token long example can be found in the git repository for clarifying the format to be used.
# train
cat input.txt | python3 -m huntag train --model=modelName --config-file=configs/maxnp.szeged.emmorph.yaml
# transmodel-train
cat input.txt | python3 -m huntag transmodel-train --model=modelName # --trans-model-order [2 or 3, default: 3]
# tag
cat input.txt | python3 -m huntag tag --model=modelName --config-file=configs/maxnp.szeged.emmorph.yaml ## Featurizing input (eg. for CRFsuite)
# train-featurize
cat input.txt | python3 -m huntag train-featurize --model=modelName --config-file=configs/maxnp.szeged.emmorph.yaml > modelName.CRFsuite.train
# tag-featurize
cat input.txt | python3 -m huntag tag-featurize --model=modelName --config-file=configs/maxnp.szeged.emmorph.yaml > modelName.CRFsuite.tag
# most-informative-features
cat input.txt | python3 -m huntag most-informative-features --model=modelName --config-file=configs/maxnp.szeged.emmorph.yaml > modelName.most_informative_features
# tag FeatureWeights
cat input.txt | python3 -m huntag tag --print-weights 100 --model=modelName --config-file=configs/maxnp.szeged.emmorph.yaml > modelNam.modelWeights
Pretrained models for Hungarian are available in the models directory.
HunTag3 is a massive overhaul, cleanup and functional extension of the original HunTag idea and codebase. HunTag3 was created by Balázs Indig with contributions from Márton Miháltz.
HunTag was created by Gábor Recski and Dániel Varga. It is a reimplementation and generalization of a Named Entity Recognizer built by Dániel Varga and Eszter Simon.
The patch for Liblinear (to lower memory usage) was created by Attila Zséder. See link for deatils: http://www.csie.ntu.edu.tw/~cjlin/liblinear/faqfiles/python_datastructures.html
HunTag3 is made available under the GNU Lesser General Public License v3.0. If you received HunTag3 in a package that also contain the Hungarian training corpora for named-entity recognition or chunking task, then please note that these corpora are derivative works based on the Szeged Treebank, and they are made available under the same restrictions that apply to the original Szeged Treebank
This tool is also integrated into the e-magyar language processing system. It is called emChunk/emNER.
If you use the tool, please cite the following paper:
@inproceedings{HunTag3,
title = {{HunTag3:} a general-purpose, modular sequential tagger -- chunking phrases in {English and maximal NPs and NER for Hungarian}}, author = {Endr\'edy, Istv\'an and Indig, Bal\'azs}, booktitle = {7th {L}anguage \& {T}echnology {C}onference, {Human Language Technologies as a Challenge for Computer Science and Linguistics (LTC '15)}}, year = {2015}, month = {November}, publisher = {Pozna\'n: {U}niwersytet im. {Adama Mickiewicza w Poznaniu}},
isbn = {978-83-932640-8-7},
pages = {213-218},
address = {{P}ozna\'n, {P}oland}}
If you use some specialized version for Hungarian, please also cite the following paper:
Dóra Csendes, János Csirik, Tibor Gyimóthy and András Kocsor (2005): The Szeged Treebank. In: Text, Speech and Dialogue. Lecture Notes in Computer Science Volume 3658/2005, Springer: Berlin. pp. 123-131.
@inproceedings{Csendes:2005,
author={Csendes, D{\'o}ra and Csirik, J{\'a}nos and Gyim{\'o}thy, Tibor and Kocsor, Andr{\'a}s},
title={The {S}zeged {T}reebank}, booktitle={Lecture Notes in Computer Science: Text, Speech and Dialogue},
year={2005},
pages={123-131},
publisher={Springer}}