A Vietnamese natural language processing toolkit
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Readme.md

VnCoreNLP: A Vietnamese natural language processing toolkit

VnCoreNLP is a Java NLP annotation pipeline for Vietnamese, providing rich linguistic annotations through key NLP components of word segmentation, POS tagging, named entity recognition (NER) and dependency parsing:

  • ACCURATE – VnCoreNLP is the most accurate toolkit for Vietnamese NLP, obtaining state-of-the-art results on standard benchmark datasets.
  • FAST – VnCoreNLP is fast, so it can be used for dealing with large-scale data.
  • Easy-To-Use – Users do not have to install external dependencies. Users can run processing pipelines from either the command-line or the Java API.

The general architecture and experimental results of VnCoreNLP can be found in the following related papers:

  1. Thanh Vu, Dat Quoc Nguyen, Dai Quoc Nguyen, Mark Dras and Mark Johnson. 2018. VnCoreNLP: A Vietnamese Natural Language Processing Toolkit. In Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Demonstrations, NAACL 2018, pages 56-60. [.bib]
  2. Dat Quoc Nguyen, Dai Quoc Nguyen, Thanh Vu, Mark Dras and Mark Johnson. 2018. A Fast and Accurate Vietnamese Word Segmenter. In Proceedings of the 11th International Conference on Language Resources and Evaluation, LREC 2018, pages 2582-2587. [.bib]
  3. Dat Quoc Nguyen, Thanh Vu, Dai Quoc Nguyen, Mark Dras and Mark Johnson. 2017. From Word Segmentation to POS Tagging for Vietnamese. In Proceedings of the 15th Annual Workshop of the Australasian Language Technology Association, ALTA 2017, pages 108-113. [.bib]

Please CITE paper [1] whenever VnCoreNLP is used to produce published results or incorporated into other software. If you are dealing in depth with either word segmentation or POS tagging, you are encouraged to also cite paper [2] or [3], respectively.

NOTE that if you are looking for light-weight versions, VnCoreNLP's word segmentation and POS tagging components have also been released as independent packages RDRsegmenter [2] and VnMarMoT [3], resepectively.

VnCoreNLP is free for non-commercial use and distributed under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA) License.

Using VnCoreNLP from the command line

Assume that Java 1.8+ is already set to run in the command line or terminal (for example: adding Java to the environment variable path in Windows OS); and file VnCoreNLP-1.1.jar (27MB) and folder models (115MB) are placed in the same working folder. You can run VnCoreNLP to annotate an input raw text corpus (e.g. a collection of news content) by using following commands:

//To perform word segmentation, POS tagging, NER and then dependency parsing
$ java -Xmx2g -jar VnCoreNLP-1.1.jar -fin input.txt -fout output.txt
// To perform word segmentation, POS tagging and then NER
$ java -Xmx2g -jar VnCoreNLP-1.1.jar -fin input.txt -fout output.txt -annotators wseg,pos,ner
// To perform word segmentation and then POS tagging
$ java -Xmx2g -jar VnCoreNLP-1.1.jar -fin input.txt -fout output.txt -annotators wseg,pos
// To perform word segmentation
$ java -Xmx2g -jar VnCoreNLP-1.1.jar -fin input.txt -fout output.txt -annotators wseg    

Using VnCoreNLP from the API

The following code is a simple and complete example:

import vn.pipeline.*;
import java.io.*;
public class VnCoreNLPExample {
    public static void main(String[] args) throws IOException {
    
        // "wseg", "pos", "ner", and "parse" refer to as word segmentation, POS tagging, NER and dependency parsing, respectively. 
        String[] annotators = {"wseg", "pos", "ner", "parse"}; 
        VnCoreNLP pipeline = new VnCoreNLP(annotators); 
    
        String str = "Ông Nguyễn Khắc Chúc  đang làm việc tại Đại học Quốc gia Hà Nội. Bà Lan, vợ ông Chúc, cũng làm việc tại đây."; 
        
        Annotation annotation = new Annotation(str); 
        pipeline.annotate(annotation); 
        
        System.out.println(annotation.toString());
        // 1    Ông                 Nc  O       4   sub 
        // 2    Nguyễn_Khắc_Chúc    Np  B-PER   1   nmod
        // 3    đang                R   O       4   adv
        // 4    làm_việc            V   O       0   root
        // ...
        
        //Write to file
        PrintStream outputPrinter = new PrintStream("output.txt");
        pipeline.printToFile(annotation, outputPrinter); 
    
        // You can also get a single sentence to analyze individually 
        Sentence firstSentence = annotation.getSentences().get(0);
        System.out.println(firstSentence.toString());
    }
}

vncorenlpexample

See VnCoreNLP's open-source in folder src for API details.

Experimental results

We briefly present experimental setups and obtained results in the following subsections. See details in papers [1,2,3] above.

Word segmentation

  • Training data: 75k manually word-segmented training sentences from the VLSP 2013 word segmentation shared task.
  • Test data: 2120 test sentences from the VLSP 2013 POS tagging shared task.
Model F1 (%) Speed (words/second)
VnCoreNLP (i.e. RDRsegmenter) 97.90 62k / _
UETsegmenter 97.87 48k / 33k*
vnTokenizer 97.33 _ / 5k*
JVnSegmenter-Maxent 97.00 _ / 1k*
JVnSegmenter-CRFs 97.06 _ / 1k*
DongDu 96.90 _ / 17k*
  • Speed is computed on a personal computer of Intel Core i7 2.2 GHz, except when specifically mentioned. * denotes that the speed is computed on a personal computer of Intel Core i5 1.80 GHz.
  • See paper [2] for more details.

POS tagging

  • 27,870 sentences for training and development from the VLSP 2013 POS tagging shared task:
    • 27k sentences are used for training.
    • 870 sentences are used for development.
  • Test data: 2120 test sentences from the VLSP 2013 POS tagging shared task.
Model Accuracy (%) Speed
VnCoreNLP (i.e. VnMarMoT) 95.88 25k
RDRPOSTagger 95.11 180k
BiLSTM-CRF 95.06 3k
BiLSTM-CRF + CNN-char 95.40 2.5k
BiLSTM-CRF + LSTM-char 95.31 1.5k
  • See paper [3] for more details.

Named entity recognition

  • 16,861 sentences for training and development from the VLSP 2016 NER shared task:
    • 14,861 sentences are used for training.
    • 2k sentences are used for development.
  • Test data: 2,831 test sentences from the VLSP 2016 NER shared task.
  • NOTE that in the VLSP 2016 NER data, each word representing a full personal name are separated into syllables that constitute the word. The VLSP 2016 NER data also consists of gold POS and chunking tags as reconfirmed by VLSP 2016 organizers. This scheme results in an unrealistic scenario for a pipeline evaluation:
    • The standard annotation for Vietnamese word segmentation and POS tagging forms each full name as a word token, thus all word segmenters have been trained to output a full name as a word and all POS taggers have been trained to assign a POS label to the entire full-name.
    • Gold POS and chunking tags are NOT available in a real-world application.
  • For a realistic scenario, contiguous syllables constituting a full name are merged to form a word. Then, POS tags are predicted by using our tagging component. The results are as follows:
Model F1 Speed
VnCoreNLP 88.55 18k
BiLSTM-CRF 86.48 2.8k
BiLSTM-CRF + CNN-char 88.28 1.8k
BiLSTM-CRF + LSTM-char 87.71 1.3k
BiLSTM-CRF + predicted POS 86.12 _
BiLSTM-CRF + CNN-char + predicted POS 88.06 _
BiLSTM-CRF + LSTM-char + predicted POS 87.43 _
  • Here, for VnCoreNLP, we include the time POS tagging takes in the speed.
  • See paper [1] for more details.

Dependency parsing

  • The last 1020 sentences of the benchmark Vietnamese dependency treebank VnDT are used for test, while the remaining 9k+ sentences are used for training & development. LAS and UAS scores are computed on all tokens (i.e. including punctuation).
Model LAS (%) UAS (%) Speed
Gold POS VnCoreNLP 73.39 79.02 _
BIST-bmstparser 73.17 79.39 _
BIST-barchybrid 72.53 79.33 _
MSTparser 70.29 76.47 _
MaltParser 69.10 74.91 _
Predicted POS VnCoreNLP 70.23 76.93 8k
jPTDP 69.49 77.68 700
  • See paper [1] for more details.