This sample is a Silverlight™ application that analyzes an text input.
Following processings wille be done:

• Language Detection (see: Babel)
• Text is split into sentences
• POS Taggin (de, en only)
• Annotations
• Parsing (de only)

 The demo will be pretty slow since the demo server is lacking enough memory to make active use of any caching. So please be a little patient....

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• Download demo project - 516 Kb
• Download source - 531 Kb

Introduction

Quite some time ago I published an article on how to detect the encoding of an givent text. In this article I describe the next step on the long way to text classification: the dectecion of language.

The given solution is based on n-gram and word occurence comparison.

It is sutable for any languages that uses words (This is actually not true for all languages).

Depending on the model and the length of the input text the accuracy is between 70% (only short Norwegian, Swedisch and Danisch classified by the "all"model) and 99.8%  using the "default" model.

Background

The language detection of a written text is probably one of the most basic tasks in natural language processing (NLP). For any language depending processing of an unknown text the first thing to know it which language the text is written in. Luckily it is one of the easier challenges that NLP has to offer. The approach I have chosen to implement I widely known and pretty straight forward. The idea is that any language has unique set of character (co-)occurrences.

The first step is to collect those statistics for all languages that should be detectable. This is not as easy as it may sound in the first place. The problem is to collect a large set of test data (plain text) that contains only one language and that is not domain specific (Only newspaper articles my lack the use of the word “I” and direct speech. Using Shakespeare plays will not be the best approach to detect contemporary texts. Medical articles tend to contain too many domain specific terms which are not even language specific (major, minor, arteria etc…)). And if that would not be hard enough, the texts should not be copyrighted (I am not sure if this is a true requirement. Are the results of statistical analytics of copyrighted texts also copyrighted?). I have chosen to use Wikipedia as my primary source. I had to do some filtering to "clean" the sources from the ever present English phrases that occur in almost any article – no matter what language they are written in (I actually used Babel itself to detect the English phrases). The clean up was in no way perfect. Wikipedia contains a lot of proper names (i.e. band names) that often contain a “the” or a “and”. This is why those words occur in many languages even if they are not part of the language. This must not necessarily be a disadvantage, because Anglicisms are widely spread across many languages. I created three statistics for each language:

• Character set

  Some languages have a very specific Character set (i.e. Chinese, Japanese and Russian) for others some characters give a good hint what languages come in question (i.e. the German Umlauts).

• N-Grams

  After tokenizing the text into words (where applicable) the occurrences of each 1,2 and 3-gram were counted. Some n-grams are very language specific (i.e. the "TH" in English).

• Word list

  The last source of disambiguation are the actually used words. Some languages (like Portuguese and Spanish) are almost identical in used characters and also the occurrences of the specific n-grams. Still different words are used in different frequencies.

A set of statistics is called a model. I have created some subsets of the "all" model that meet my needs the best (see table below). The "common" model contains the 10 most spoken languages in the world. The “small” and “default” are based on my usage scenarios. If you are from another part of the world your preferences might be different. So please take no offence in my choice of what languages are contained in which model.

All Statistics are ordered and ranked by their occurrences. Within the Demo Application all models can be studied in detail. Classification of an unknown text is straight forward. The text is tokenized and the three tables for the statistics are generated. The result table is compared to all tables in the model and a distance is calculated. The comparison table from the model that had the smallest distance to the unknown text is most likely the language of the text.

sample model

Using the Code

Quick word about the code

Babel is a part of a larger project. I wanted the Babel assembly to work stand alone. Since some of the used classes originally were scattered accross many assemblies I used the define "_DIALOGUEMASTER" to indicate whether to use the DialogueMaster™ assemblies or implement (a probably simpler) version in place.

Any impartand DialogueMaster™ class is remotable. The clients need only one assembly containing all the interface definitions. This is why Babel uses so many interfaces where they might seem to bloat the code in the first place. Additionally DialogueMaster™ offers lots of PerformanceCounters. I chose to omit them for an easier usage of the Assembly (no installation and no admin rights needed).

What I actually want to say is: the code is not as readable and clean as it could (and should) be.

Classify Text

Usage of the code is straight forward. First you must chose (or create your own) model. The ClassifyText method returns a ICategoryList which is a list of ICateogry (name-score Pairs) items sorted descending by their score.

 
using System;

//
// Most simple samlple
//

class Program
{
    static void Main(string[] args)
    {
        DialogueMaster.Babel.BabelModel model = DialogueMaster.Babel.BabelModel._AllModel;
        String s = System.Console.ReadLine();
        while (s.Length > 0)
        {
            DialogueMaster.Classification.ICategoryList result = model.ClassifyText(s, 10);
            foreach (DialogueMaster.Classification.ICategory category in result)
            {
                System.Console.Out.WriteLine(" {0} : {1}", category.Name, category.Score);
            }

            s = System.Console.ReadLine();
        }
    }
}

Define your own Model

From existing set

To define your own modle from the existing set of languages simply create a new BabelModel and add the required languages from the _AllModel

class Program2
{
    static void Main(string[] args)
    {
        // Create a custom model 
        DialogueMaster.Babel.BabelModel model = new DialogueMaster.Babel.BabelModel();
        model.Add("de", DialogueMaster.Babel.BabelModel._AllModel["de"]);
        model.Add("en", DialogueMaster.Babel.BabelModel._AllModel["en"]);
        model.Add("sv", DialogueMaster.Babel.BabelModel._AllModel["sv"]);

        // ask the user for some input
        String s = System.Console.ReadLine();
        while (s.Length > 0)
        {
            // classify it 
            DialogueMaster.Classification.ICategoryList result = model.ClassifyText(s, 10);
            // and dump the result
            foreach (DialogueMaster.Classification.ICategory category in result)
            {
                System.Console.Out.WriteLine(" {0} : {1}", category.Name, category.Score);
            }

            s = System.Console.ReadLine();
        }
    }
}

Add new language

To add a new language is pretty straight forward. All you need is some learn data text.

class Program3
{
    static void Main(string[] args)
    {
        // Create a custom model 
        DialogueMaster.Babel.BabelModel model = new DialogueMaster.Babel.BabelModel();
        TokenTable klingonTable = new TokenTable(new FileInfo("LearnData\\Klingon.txt"));
        TokenTable vulcanTable = new TokenTable(new FileInfo("LearnData\\Vulcan.txt"));

        model.Add("kling", klingonTable);
        model.Add("vulcan", klingonTable);
        model.Add("en", DialogueMaster.Babel.BabelModel._AllModel["en"]);


        // ask the user for some input
        String s = System.Console.ReadLine();
        while (s.Length > 0)
        {
            // classify it 
            DialogueMaster.Classification.ICategoryList result = model.ClassifyText(s, 10);
            // and dump the result
            foreach (DialogueMaster.Classification.ICategory category in result)
            {
                System.Console.Out.WriteLine(" {0} : {1}", category.Name, category.Score);
            }

            s = System.Console.ReadLine();
        }
    }
}

Points of Interest

Supported Languages

References

• NGram
• Language Identification from Text Using N-gram Based Cumulative Frequency Addition

History

10/10/2009 Initial Version Released

During the last few years we developed a software called DialogueMaster™ that was originally designed to classify e-mails. Over the years the software became more an more sophisticated and lots of very specialized components have  been developed.

Since some of our code is based on public research or freely available samples we thought it would be a good idea to give something back to the community. The first approach was to to publish some of the basic components so I wrote two articles on CodeProject (also to be found on this site). 
Even though I really enjoyed writing the articles there were two major drawbacks: It took days to write one and the audience is very limited (who needs to detect an encoding anyway?).

After some pondering we came to the conclusion that it might be a good idea to publish some of the DialogueMaster™ functions using web services.

So from now on I will start to publish some of the basic functionalities of DialogueMaster™ on this blog. I will start with probably the most basic linguistic component we have: the language detection.