A Vietnamese text processing library developed in the Scala programming language.
This is a repository of a Scala project which implements some basic tasks of Vietnamese text processing. Each basic task is implemented in a module. Some modules can be tested on a demo site here.
tok: tokenizer, which implements a rule-based word segmentation approach;tag: tagger, which implements a conditional Markov model for sequence tagging;ner: named entity recognizer, which implements a bidirectional conditional Markov model, and a bidirectional neural network model;tdp: dependency parser, which implements a transition-based dependency parsing approach;tpm: topic modeling, which implements a Latent Dirichlet Allocation (LDA) model;tcl: text classifier, which implements a feed-forward neural network model for text classification;vdr: diacritics restorer, which implements a conditional Markov model to recover diacritics for non-accented Vietnamese text;vdg: diacritics generator, which implements 3 RNN-based models to recover diacritics for non-accented Vietnamese text;zoo: text classifier, which implements deep learning based models for text classification, including CNN, LSTM and GRU architectures;nli: natural language inference, which implements a number of methods, including transformers-based (BERT) models.nlm: language modeling using recurrent neural networks and transformers
The tokenizer module is bundled in the file tok.jar. See section Compile and Package below to know how to create this jar file from source.
The main class of the tokenizer module is vlp.tok.Tokenizer. It segments a given text into tokens. Each token is represented by a triple (position, shape, content). This class can take two arguments of an input text file and an output file. The input file must exist and contain plain text, arranged in lines. The output file will be created by the program. For example:
$java -jar tok.jar path/to/inp.txt path/to/out.txt
If the output file is not provided, the result will be shown to the console. If both the input and output files are not provided, a sample sentence will be processed and its result is shown to the console.
The tokenizer makes use of parallel processing in Scala, which effectively exploits all CPU cores of a single machine. For this reason, on a large file it is still fast. On my laptop, the tokenizer can process an input file of more than 532,000 sentences (about 1,000,000 syllables) in about 100 seconds.
For really large input files in a big data setting, it is more convenient to use the tokenizer together with the Apache Spark library so that it is easy to port to a cluster of multiple nodes. We provide a transformer-based implementation of the Vietnamese tokenizer, in the class vlp.tok.TokenizerTransformer. This can be integrated into the machine learning pipeline of the Apache Spark Machine Learning library, in the same way as the standard org.apache.spark.ml.feature.Tokenizer. Note that the wrapper transformer depends on Apache Spark but not the tokenizer. If you do not want to use this module with Apache Spark, you can simply copy the self-contained tokenizer and import it to your project, delete TokenizerTransformer and ignore all Apache Spark dependencies.
The tagger module implements a simple first-order conditional Markov model (CMM) for sequence tagging. The basic features include current word, previous word, next word, current word shape, next word shape, previous previous word, and next next word. Each local transition probability is specified by a multinomial logistic regression model.
On the standard VLSP 2010 part-of-speech tagged treebank, this simple model gives a training accuracy of 0.9638 when all the corpus is used for training. A pre-trained model is provided in the directory dat/tag/cmm.
Since the machine learning pipeline in use is that of Apache Spark, this module depends on Apache Spark. Suppose that you have alreadly a version of Apache Spark installed.
To tag an input text file containing sentences, each on a line, invoke the command
$spark-submit tag.jar -m tag -i dat/tag/sample.txt
The tagging result will be shown to the output, each line contains pairs of (token, part-of-spech).
Option -m specifies the running mode. If the pre-trained model is not on the default path, you must specify it explicitly with option -p, as follows:
$spark-submit tag.jar -m tag -i dat/sample.txt -p path/to/model
Note that the input text file does not need to be tokenized in advance, the tagger will call the tokenizer module to segment the text into words before tagging.
To train a model, you will need the VLSP 2010 part-of-speech tagged corpus (which has about 10,000 manually tagged sentences). Suppose that the corpus is provided at the default path dat/tag/vtb-tagged.txt:
$spark-submit tag.jar -m train
If the data is at another location, specify it with option -d:
$spark-submit tag.jar -m train -d path/to/tagged/corpus
The resulting model will be saved to its default directory dat/tag. This can be changed with option -p as above. After training, the evaluation mode is called automatically to print out performance scores (accuracy and f-score) on the training set.
There are some other options for fine-tuning the training, such as -f (for min feature frequency cutoff, default value is 3) or -u (for domain dimension, default value is 16,384). See the code for detail.
By default, the master URL is set to local[*], which means that all CPU cores of the current machine are used by Apache Spark. You can specify a custom master URL with option -M. See more about this as in the ner module below.
The named entity recognition module implements two models. The first one is a bidirectional conditional Markov model for sequence tagging. This tagging model combines a forward CMM and a backward CMM which are trained independently and then combined in decoding. This method has achieved the best F1 score of the VLSP 2016 shared task on Vietnamese Named Entity Recognition. On the standard test set of VLSP 2016 NER, its F1 score is about 88.8%. The second one is a neural named entity tagger which makes use of a bidirectional recurrent neural network models.
The detailed approach is described in the following paper:
- Vietnamese Named Entity Recognition using Token Regular Expressions and Bidirectional Inference, Phuong Le-Hong, Proceedings of Vietnamese Speech and Language Processing (VLSP), Hanoi, Vietnam, 2016.
As the tag module, the ner module is also an Apache Spark application, you run it by submitting the main JAR file ner.jar to Apache Spark. The main class of the toolkit is vlp.ner.Tagger which selects the desired tool by following arguments provided by the user.
The arguments are as follows:
-M <master>: the master URL for Apache Spark, default islocal[*]which uses all CPU cores of the current machine. If run on a cluster, you should provide the Spark master URL here, for example-M spark://192.168.1.1:7077.-l <language>: the language to process, wherelanguageis an abbreviation of language name which is eithervie(Vietnamese) oreng(English). If this argument is not specified, the default language is Vietnamese.-v: this parameter does not require argument. If it is used, the module runs in verbose mode, in which some intermediate information will be printed out during the processing. This is useful for debugging.-m <mode>: the running mode, eithertag,train,eval, ortest; the default mode istag.-i <input-file>: the name of an input file to be used. If running in theevalortrainmode, this should be a file in the CoNLL format for NER. If running intagmode, it should be a raw text file in UTF-8 encoding, each sentence is on a line.-u <dimension>: this argument is only required in thetrainmode to specify the number of features (or the domain dimension) of the resulting CMM. The dimension is a positive integer and depends on the size of the data. Normally, the larger the training data is, the greater the dimension that should be used. As an example, we set this argument as 32,768 when training a CMM on about 16,000 tagged sentences of the VLSP NER corpus. The default dimension is 32,768.-r: this parameter does not require argument. If it is used, the tagger will train or test using reversed sentences to produce a backward sequence model instead of the default forward sequence model.
To tag an input file and write the result to an output file of the same name (with generated suffix .out), using the default pre-trained model:
$spark-submit ner.jar -m tag -i <input-file>
The input file is a raw text file, each sentence on a line. A part-of-speech tagging model will be called before the name tagger is called to tag the sentences.
To evaluate the accuracy on a gold corpus vie.test:
$spark-submit ner.jar -m eval -i path/to/vie.test
This will produces an output file vie.test.out in the same directory as vie.test. This is a two-column text file in the format ready for being evaluated with the conlleval script. Running with conlleval vie.test.out should gives a result similar to:
processed 66097 tokens with 2996 phrases; found: 3038 phrases; correct: 2675.
accuracy: 99.02%; precision: 88.05%; recall: 89.29%; FB1: 88.66
LOC: precision: 87.48%; recall: 93.76%; FB1: 90.51 1478
MISC: precision: 80.95%; recall: 69.39%; FB1: 74.73 42
ORG: precision: 71.93%; recall: 44.89%; FB1: 55.28 171
PER: precision: 90.94%; recall: 94.67%; FB1: 92.77 1347
To train a forward model tagger on a gold corpus at the path dat/ner/vie/vie.train:
$spark-submit ner.jar -m train -u 4096
The resulting model will be saved in the default directory dat/ner/vie/cmm-f.
To train a backward model tagger on a gold corpus at the path dat/ner/vie/vie.train:
$spark-submit ner.jar -m train -u 4096 -r
The resulting model will be saved in the default directory dat/ner/vie/cmm-b.
The default forward and backward CMM models are provided in the directory dat/ner/vie, which use 32,768 dimensions (that is, they are trained with the option -u 32768).
On a large dataset, in order to avoid the out-of-memory error, you should consider to use the option --driver-memory of Apache Spark when submitting the job, as follows:
$spark-submit --driver-memory 16g ner.jar -m train -l eng
- TODO
The dependency parser module implements a transition-based dependency parsing algorithm. The vlp.tdp.Classifier learns a mapping from a parsing context to a labeled transition. Training samples in the form of (parsing context, labeled transition) pairs are extracted automatically from an available treebank in the CoNLLU format, as defined by the Universal Dependency project. This classifier implements both multinomial logistic regression (MLR) model and multi-layer perceptron (MLP) model for classification.
Two dependency treebanks, one for English and one for Vietnamese are available in the dat/dep/eng and dat/dep/vie. These datasets of version 2.0 are publicly available at the Universal Dependency website. In the Vietnamese treebank, there are 1,400 training sentences and 800 development sentences. Refer to the class vlp.tdp.FeatureExtractor for the list of (discrete) features used in this classifier implementation.
To train a transition classifier using MLR model with default settings:
$spark-submit --class vlp.tdp.Classifier tdp.jar -m train
The resulting model will be saved to its default directory dat/tdp/vie/mlr. After training, the evaluation mode is called automatically to print out development and training scores.
To train the classifier using MLP with default settings:
$spark-submit --class vlp.tdp.Classifier tdp.jar -m train -c mlp
The option -c stands for classifier type. To use a MLP with two hidden layers, the first layer has 64 units and the second layer has 32 units, use the -h option as follows:
$spark-submit --class vlp.tdp.Classifier tdp.jar -m train -c mlp -h "64 32"
The resulting model will be saved to its default directory dat/tdp/vie/mlp.
There are some other options for fine-tuning the training, such as -f (for min feature frequency cutoff, default value is 2) or -u (for domain dimension, default value is 65,536). See the code for detail.
To train a transition classifier for English, use the option -l eng (-l is for language). For example:
$spark-submit --class vlp.tdp.Classifier tdp.jar -m train -l eng -u 2048
The resulting model will be saved to its default directory dat/tdp/eng/mlr. There are 67 dependency labels for English.
As above, by default, the master URL is set to local[*], which means that all CPU cores of the current machine are used by Apache Spark. You can specify a custom master URL with option -M. On a large dataset such as the English treebank, in order to avoid the out-of-memory error, you should consider to use the option --driver-memory of Apache Spark when submitting the job, as follows:
$spark-submit --driver-memory 16g --class vlp.tdp.Classifier tdp.jar -m train -l eng -u 16384
The executor memory is set to the default value of 8g.
The following table shows the average F1-scores of the transition classifier trained on the Vietnamese dependency treebank when using a MLR. The classifier performance depends largely on the number of features in use.
| #(features) | F1 dev. | F1 train. |
|---|---|---|
| 1,024 | 0.7861 | 0.8757 |
| 2,048 | 0.7728 | 0.9091 |
| 4,096 | 0.7483 | 0.9470 |
| 8,192 | 0.7322 | 0.9800 |
| 16,384 | 0.7249 | 0.9946 |
| 32,768 | 0.7367 | 0.9978 |
| 65,536 | 0.7411 | 0.9990 |
On the English treebank which contains 10,008 training graphs and 1,648 dev graphs, the classifier performance are as follows:
| #(features) | F1 dev. | F1 train. |
|---|---|---|
| 2,048 | 0.8757 | 0.9034 |
| 4,096 | 0.8751 | 0.9213 |
| 8,192 | 0.8660 | 0.9420 |
| 16,384 | 0.8473 | 0.9708 |
| 32,768 | 0.8367 | 0.9885 |
| 65,536 | 0.8369 | 0.9950 |
| 100,000 | 0.8368 | 0.9967 |
| 131,072 | 0.8344 | 0.9976 |
| 150,000 | 0.8352 | 0.9978 |
| 180,000 | 0.8305 | 0.9982 |
The English parser scores are as follows:
| #(features) | UAS dev. | LAS dev. | UAS train. | LAS train. |
|---|---|---|---|---|
| 65,536 | 0.6184 | 0.5775 | 0.8186 | 0.8111 |
| 100,000 | 0.6484 | 0.6099 | 0.9253 | 0.9225 |
| 131,072 | 0.6183 | 0.5109 | 0.9517 | 0.9500 |
| 150,000 | 0.6615 | 0.6243 | 0.9664 | 0.9652 |
| 180,000 | 0.6742 | 0.6332 | 0.9812 | 0.9802 |
The parser is in vlp.tdp.Parser class. It implements the arc-eager transition parsing algorithm, where the next transition is predicted by using the current parsing configuration as input to the transition classifier. The transition set are contains labels such as SH (shift), RE (reduce), LA-dep (left arc with label dep) and RA-dep (right arc with label dep). The dependency labels are scanned from a training corpus. For the Vietnamese dependency treebank, the transition set contains 52 disctict labeled transitions. Each parse corresponds to a sequence of best transitions which are obtained by a greedy inference method.
When using 65,536 features in the classifier, the labeled attachment scores (LAS) of the parser on the development and test set of the Vietnamese dependency treebank is LAS(dev.) = 0.5303 and LAS(train.) = 0.9953.
To evaluate a transition parser using the default MLR classifier:
$spark-submit tdp.jar
To use a MLP classifier:
$spark-submit tdp.jar -c mlp
The class vlp.tpm.LDA imlements a Latent Dirichlet Allocation (LDA) topic model. It can process a collection of documents in a simple JSON format, find topics and top words in each topic.
To train a topic model on the default data file using a dictionary of 2,048 words:
$spark-submit --driver-memory 8g --class vlp.tdp.LDA tdp.jar -m train -u 2048
The data file must be a JSON file, each elements is of the following structure:
class News(url: String, sentences: List[String])
See the file dat/txt/fin.json for an example.
The default number of features (words) in use is 32,768. Use the option -k to change the number of topics, default value is 50:
$spark-submit --class vlp.tdp.LDA tdp.jar -m train -k 100
The data path can be changed with option -d.
After training a model, it can be evaluated by using the (default mode) eval:
$spark-submit --class vlp.tdp.LDA tdp.jar
Some information of the topic and word distributions, as well as the log-likelihood of the model on the corpus will be printed out.
The class vlp.tcl.Classifier implements a feed-forward neural network model for text classification. A simple form of this model is multinomial logistic regression or MLR, which can be considered as a network model without hidden layers. To train a MLR model on a data set:
$spark-submit --driver-memory 8g --class vlp.tcl.Classifier tcl.jar -m train
The default MLR model will be saved into the default directory dat/tcl. This model path can be changed by using the option -p <modelPath>. The data set can be specified by -d <dataPath> option. The data path can be one or some raw text files, each line contains a sample of the form label <tab> content.
$spark-submit --driver-memory 8g --class vlp.tcl.Classifier tcl.jar -m train -d dat/*.txt
A neural network, instead of a MLR can be specified by using the option -c mlp and appropriate parameters, notably its hidden layer configuration such as -h "128 64". For example:
$spark-submit --driver-memory 8g --class vlp.tcl.Classifier tcl.jar -m train -d dat/*.txt -c mlp -h "128 64"
The command above trains a multiple layer perceptron (aka neural network) with two layers of 128 hidden units and 64 hidden units respectively. If the option -h is not specified, a defautl hidden layer of 16 units will be used. The default number of (maximum) features is 32,768; and this parameter can be controlled by the option -u. There is also -f option for feature minimum frequency cutoff.
After training, the model will be evaluated on the training set and test set which are randomly split with ratio [0.8, 0.2] respectively. The (accuracy, f-measure) scores will be printed out to the console.
In the default eval mode, the classifier will print out evaluation score of the test set, using a pre-trained model in the default model path:
$spark-submit --driver-memory 8g --class vlp.tcl.Classifier tcl.jar
There is also a common option for verbose mode (-v) and for using the classifier with a cluster instead of a single local machine (-M <masterURL>).
- TODO
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- Most of the modules depends on the Machine Learning library of Apache Spark.
- This big data technology permits to process millions of texts with a very high speed.
- The services can be used in two modes: batch processing (offline) or on-the-fly (online).
- The program is developed in the Scala programming language. It needs a Java Runtime Environment (JRE) to run, or a Java Development Kit (JDK) environment to compile and package. We use Java version 8.0.
- Since the code is developed in Scala, you need to have Scala too.
- If you want to compile and build the software from source, you need a Scala build tool to manage all dependencies and produce a binary JAR file. We use SBT.
- Go to the main directory
cd vlpon your command line console. - Invoke
sbtconsole with the commandsbt. - In the sbt, compile the entire project with the command
compile. All requried libraries are automatically downloaded, only at the first time. - In the sbt, package the project with the command
assembly. - The resulting JAR files are in sub-projects, for example
- tokenizer is in
tok/target/scala-2.12/tok.jar - part-of-speech tagger is in
tag/target/scala-2.12/tag.jar - named entity tagger is in
ner/target/scala-2.12/ner.jar - etc.
- tokenizer is in
Any bug reports, suggestions and collaborations are welcome. I am reachable at:
- LE-HONG Phuong, http://mim.hus.edu.vn/lhp/ or http://vlp.group/lhp/
- College of Science, Vietnam National University, Hanoi