Are you interested in processing Vietnamese language using a robust natural language processing (NLP) toolkit? Look no further! VnCoreNLP is your go-to solution, providing a fast and accurate NLP annotation pipeline designed specifically for the intricacies of the Vietnamese language. In this guide, we will walk you through the installation, usage, and offer experimental results, ensuring you get the most out of this powerful tool!

Introduction

VnCoreNLP not only delivers rich linguistic annotations through key NLP components such as word segmentation, POS tagging, named entity recognition (NER), and dependency parsing but does so without requiring additional dependencies. You can run processing pipelines through both command-line and API interfaces.

Installation

Before diving into usage, let’s get VnCoreNLP installed on your system:

• Ensure you have Java 1.8+ installed. This is a prerequisite!
• Download the VnCoreNLP-1.2.jar (27MB) file and the folder containing models (115MB) into the same working folder.
• If you wish to use a Python wrapper, ensure you have Python 3.6+. To install this wrapper, run:

$ pip3 install py_vncorenlp

• Special thanks to Linh The Nguyen for creating this wrapper!

Usage for Python Users

For Python enthusiasts, here’s how you can start using VnCoreNLP:

import py_vncorenlp

# Automatically download VnCoreNLP components from the original repository
# and save them in a local working folder
py_vncorenlp.download_model(save_dir=absolute_path_to_vncorenlp)

# Load VnCoreNLP from the local folder
model = py_vncorenlp.VnCoreNLP(save_dir=absolute_path_to_vncorenlp)

# Annotate a raw corpus
model.annotate_file(input_file=absolute_path_to_input_file, output_file=absolute_path_to_output_file)

# Annotate a raw text
model.print_out(model.annotate_text("Ông Nguyễn Khắc Chúc đang làm việc tại Đại học Quốc gia Hà Nội. Bà Lan, vợ ông Chúc, cũng làm việc tại đây."))

By default, the output is formatted with six columns denoting word index, word form, POS tag, NER label, head index, and dependency relation type.

Usage for Java Users

For those who prefer Java, VnCoreNLP also provides convenient usage from the command line or API:

Using VnCoreNLP from the Command Line

You can run VnCoreNLP commands to annotate an input text corpus.

$ java -Xmx2g -jar VnCoreNLP-1.2.jar -fin input.txt -fout output.txt 
$ java -Xmx2g -jar VnCoreNLP-1.2.jar -fin input.txt -fout output.txt -annotators wseg,pos,ner
$ java -Xmx2g -jar VnCoreNLP-1.2.jar -fin input.txt -fout output.txt -annotators wseg,pos
$ java -Xmx2g -jar VnCoreNLP-1.2.jar -fin input.txt -fout output.txt -annotators wseg

Using VnCoreNLP from the API

Here’s a simple example to get started with the API:

import vn.pipeline.*;
import java.io.*;

public class VnCoreNLPExample {
    public static void main(String[] args) throws IOException {
        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);

        PrintStream outputPrinter = new PrintStream("output.txt");
        pipeline.printToFile(annotation, outputPrinter);
    }
}

Experimental Results

For detailed insights on the capabilities of VnCoreNLP, please refer to the following papers:

• VnCoreNLP: A Vietnamese Natural Language Processing Toolkit
• A Fast and Accurate Vietnamese Word Segmenter
• From Word Segmentation to POS Tagging for Vietnamese

Troubleshooting

If you encounter issues during installation or while using VnCoreNLP, consider the following troubleshooting ideas:

• Ensure that you have the correct version of Java and Python installed.
• Check if the working directory contains both the VnCoreNLP-1.2.jar and models folder.
• For Python users, verify the correct installation of the Py wrapper.

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At fxis.ai, we believe that such advancements are crucial for the future of AI, as they enable more comprehensive and effective solutions. Our team is continually exploring new methodologies to push the envelope in artificial intelligence, ensuring that our clients benefit from the latest technological innovations.