The build in algorithms for the openNLP categorizer

Maximum Entropy

The build in algorithms for the openNLP categorizer, which can be used to create and train the categorizer right out the box are

maxent – maximum entropy


naive Bayes (nb)

Starting page of the articles on Apache openNLP for the Content Server

maxent – maximum entropy

maximum entropy (Image KI generated)

The principle of maximum entropy states that the probability distribution which best represents the current state of knowledge about a system is the one with largest entropy, in the context of precisely stated prior data (such as a proposition that expresses testable information).

In ordinary language, the principle of maximum entropy can be said to express a claim of epistemic modesty, or of maximum ignorance (In the US its well known as GOP-principle). The selected distribution is the one that makes the least claim to being informed beyond the stated prior data, that is to say the one that admits the most ignorance beyond the stated prior data.

See here an example of the maxent categorizer


n-gram  (Image KI generated)

An n-gram is a collection of n successive items in a text document that may include words, numbers, symbols, and punctuation. N-gram models are useful in many text analytics applications where sequences of words are relevant, such as in sentiment analysis, text classification, and text generation. N-gram modeling is one of the many techniques used to convert text from an unstructured format to a structured format. An alternative to n-gram is word embedding techniques, such as word2vec. (See the article of SPACY for word vectors)

See here an example of the n-gram categorizer

Naive Bayes

naive bayes  (Image KI generated)

Naive Bayes is a simple technique for constructing classifiers: models that assign class labels to problem instances, represented as vectors of feature values, where the class labels are drawn from some finite set. There is not a single algorithm for training such classifiers, but a family of algorithms based on a common principle: all naive Bayes classifiers assume that the value of a particular feature is independent of the value of any other feature, given the class variable. For example, a fruit may be considered to be an apple if it is red, round, and about 10 cm in diameter. A naive Bayes classifier considers each of these features to contribute independently to the probability that this fruit is an apple, regardless of any possible correlations between the color, roundness, and diameter features.

See here an example of the naive Bayes categorizer

Apache Tika

Apache Tika

Apache Tika – Open and extract text from virtually all formats. Its called a “content analysis toolkit”

Its free like openNLP and its used to extract text from virtually all file formats possible.


Using Apache Tika as a command line utility

Apache Tika Example: Extracting MS Office

Goto the start opennlp series of acticles: Starting Page

The complete configuration of our AI Content Server extension is like this:

Apache Tika and openNLP

You can get TIKA at . How to install is explained on this URL

A nice overview of TIKA is here

A very nice book on TIKA at Manning

Apache Tika

Using Apache Tika as a command line utility

The basic usage (w/o programming) is a command line utility. Its used like

usage: java -jar tika-app.jar [option...] [file|port...]

    -?  or --help          Print this usage message
    -v  or --verbose       Print debug level messages
    -V  or --version       Print the Apache Tika version number

    -g  or --gui           Start the Apache Tika GUI
    -s  or --server        Start the Apache Tika server
    -f  or --fork          Use Fork Mode for out-of-process extraction

        TikaConfig file. Must be specified before -g, -s, -f or the dump-x-config !
    --dump-minimal-config  Print minimal TikaConfig
    --dump-current-config  Print current TikaConfig
    --dump-static-config   Print static config
    --dump-static-full-config  Print static explicit config

    -x  or --xml           Output XHTML content (default)
    -h  or --html          Output HTML content
    -t  or --text          Output plain text content
    -T  or --text-main     Output plain text content (main content only)
    -m  or --metadata      Output only metadata
    -j  or --json          Output metadata in JSON
    -y  or --xmp           Output metadata in XMP
    -J  or --jsonRecursive Output metadata and content from all
                           embedded files (choose content type
                           with -x, -h, -t or -m; default is -x)
    -l  or --language      Output only language
    -d  or --detect        Detect document type
           --digest=X      Include digest X (md2, md5, sha1,
                               sha256, sha384, sha512
    -eX or --encoding=X    Use output encoding X
    -pX or --password=X    Use document password X
    -z  or --extract       Extract all attachements into current directory
    --extract-dir=<dir>    Specify target directory for -z
    -r  or --pretty-print  For JSON, XML and XHTML outputs, adds newlines and
                           whitespace, for better readability

         List the available document parsers
         List the available document parsers and their supported mime types
         List the available document parsers and their supported mime types in apt format.
         List the available document detectors
         List the available metadata models, and their supported keys
         List all known media types and related information

         Compares Tika's known media types to the File(1) tool's magic directory

    Apache Tika will parse the file(s) specified on the
    command line and output the extracted text content
    or metadata to standard output.

    Instead of a file name you can also specify the URL
    of a document to be parsed.

    If no file name or URL is specified (or the special
    name "-" is used), then the standard input stream
    is parsed. If no arguments were given and no input
    data is available, the GUI is started instead.

- GUI mode

    Use the "--gui" (or "-g") option to start the
    Apache Tika GUI. You can drag and drop files from
    a normal file explorer to the GUI window to extract
    text content and metadata from the files.

- Batch mode

    Simplest method.
    Specify two directories as args with no other args:
         java -jar tika-app.jar <inputDirectory> <outputDirectory>

Batch Options:
    -i  or --inputDir          Input directory
    -o  or --outputDir         Output directory
    -numConsumers              Number of processing threads
    -bc                        Batch config file
    -maxRestarts               Maximum number of times the
                               watchdog process will restart the child process.
    -timeoutThresholdMillis    Number of milliseconds allowed to a parse
                               before the process is killed and restarted
    -fileList                  List of files to process, with
                               paths relative to the input directory
    -includeFilePat            Regular expression to determine which
                               files to process, e.g. "(?i)\.pdf"
    -excludeFilePat            Regular expression to determine which
                               files to avoid processing, e.g. "(?i)\.pdf"
    -maxFileSizeBytes          Skip files longer than this value

    Control the type of output with -x, -h, -t and/or -J.

    To modify child process jvm args, prepend "J" as in:
    -JXmx4g or -JDlog4j.configuration=file:log4j.xml.

This stand alone jar is the easiest way to use TIKA.

Apache Tika Example: Extracting MS Office

This is an example of using TIKA with Excel (metadata)


import org.apache.tika.exception.TikaException;
import org.apache.tika.metadata.Metadata;
import org.apache.tika.parser.ParseContext;
import org.apache.tika.sax.BodyContentHandler;

import org.xml.sax.SAXException;

public class MSxcelParse {

   public static void main(final String[] args) throws IOException, TikaException {
      //detecting the file type
      BodyContentHandler handler = new BodyContentHandler();
      Metadata metadata = new Metadata();
      FileInputStream inputstream = new FileInputStream(new File("example_msExcel.xlsx"));
      ParseContext pcontext = new ParseContext();
      //OOXml parser
      OOXMLParser  msofficeparser = new OOXMLParser (); 
      msofficeparser.parse(inputstream, handler, metadata,pcontext);
      System.out.println("Contents of the document:" + handler.toString());
      System.out.println("Metadata of the document:");
      String[] metadataNames = metadata.names();
      for(String name : metadataNames) {
         System.out.println(name + ": " + metadata.get(name));

When you run this program, you’ll see this output

Contents of the document:

Name	Age	Designation		Salary
Ramu	50	Manager			50,000
Raheem	40	Assistant manager	40,000
Robert	30	Superviser		30,000
sita	25	Clerk			25,000
sameer	25	Section in-charge	20,000

Metadata of the document:

meta:creation-date:    2006-09-16T00:00:00Z
dcterms:modified:    2014-09-28T15:18:41Z
meta:save-date:    2014-09-28T15:18:41Z
Application-Name:    Microsoft Excel
dcterms:created:    2006-09-16T00:00:00Z
Last-Modified:    2014-09-28T15:18:41Z
Application-Version:    15.0300
date:    2014-09-28T15:18:41Z
modified:    2014-09-28T15:18:41Z
Creation-Date:    2006-09-16T00:00:00Z
extended-properties:AppVersion:    15.0300
protected:    false
extended-properties:Application:    Microsoft Excel
Content-Type:    application/vnd.openxmlformats-officedocument.spreadsheetml.sheet
Last-Save-Date:    2014-09-28T15:18:41Z

Please refer to the book or to the pages from Apache for more.