Stanford.NLP.NET

Stanford.NLP.Segmenter

This package is deprecated and should not be used. Read more.

A CRF-based word segmenter (tokenizer). Supports Arabic and Chinese (can be used for English, French, and Spanish.)

Tokenization of raw text is a standard pre-processing step for many NLP tasks. For English, tokenization usually involves punctuation splitting and separation of some affixes like possessives. Other languages require more extensive token pre-processing, which is usually called segmentation.

The Stanford Word Segmenter currently supports Arabic and Chinese. The provided segmentation schemes have been found to work well for a variety of applications.

Stanford NLP group recommend at least 1Gb of memory for documents that contain long sentences.

The segmenter is available for download, licensed under the GNU General Public License (v2 or later). Source is included. The package includes components for command-line invocation and a Java API. The segmenter code is dual licensed (in a similar manner to MySQL, etc.). Open source licensing is under the full GPL, which allows many free uses. For distributors of proprietary software, commercial licensing is available.

Getting started

• Install NuGet package
• Download models
• Try samples from this page
• Read official Segmenter docs

Sample

using edu.stanford.nlp.ie.crf;
using java.util;

class Program
{
    static void Main()
    {
        // Path to the folder with models
        var segmenterData = @"nlp.stanford.edu\stanford-segmenter-4.2.0\data";
        var sampleData = @"nlp.stanford.edu\stanford-segmenter-2020-11-17\test.simp.utf8";

        // `test.simple.utf8` contains following text:
        // 面对新世纪，世界各国人民的共同愿望是：继续发展人类以往创造的一切文明成果，克服20世纪困扰着人类的战争和贫
        // 困问题，推进和平与发展的崇高事业，创造一个美好的世界。

        // This is a very simple demo of calling the Chinese Word Segmenter programmatically.
        // It assumes an input file in UTF8. This will run correctly in the distribution home
        // directory. To run in general, the properties for where to find dictionaries or
        // normalizations have to be set.
        // @author Christopher Manning

        // Setup Segmenter loading properties
        var props = new Properties();
        props.setProperty("sighanCorporaDict", segmenterData);
        // Lines below are needed because CTBSegDocumentIteratorFactory accesses it
        props.setProperty("serDictionary", segmenterData + @"\dict-chris6.ser.gz");
        props.setProperty("testFile", sampleData);
        props.setProperty("inputEncoding", "UTF-8");
        props.setProperty("sighanPostProcessing", "true");

        // Load Word Segmenter
        var segmenter = new CRFClassifier(props);
        segmenter.loadClassifierNoExceptions(segmenterData + @"\ctb.gz", props);
        segmenter.classifyAndWriteAnswers(sampleData);
    }
}

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