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.
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)
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
The basic usage (w/o programming) is a command line utility. Its used like
usage: java -jar tika-app.jar [option...] [file|port...]
Options:
-? 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
--config=<tika-config.xml>
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-parsers
List the available document parsers
--list-parser-details
List the available document parsers and their supported mime types
--list-parser-details-apt
List the available document parsers and their supported mime types in apt format.
--list-detectors
List the available document detectors
--list-met-models
List the available metadata models, and their supported keys
--list-supported-types
List all known media types and related information
--compare-file-magic=<dir>
Compares Tika's known media types to the File(1) tool's magic directory
Description:
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 java.io.File;
import java.io.FileInputStream;
import java.io.IOException;
import org.apache.tika.exception.TikaException;
import org.apache.tika.metadata.Metadata;
import org.apache.tika.parser.ParseContext;
import org.apache.tika.parser.microsoft.ooxml.OOXMLParser;
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:
Sheet1
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
extended-properties:Company:
dcterms:created: 2006-09-16T00:00:00Z
Last-Modified: 2014-09-28T15:18:41Z
Application-Version: 15.0300
date: 2014-09-28T15:18:41Z
publisher:
modified: 2014-09-28T15:18:41Z
Creation-Date: 2006-09-16T00:00:00Z
extended-properties:AppVersion: 15.0300
protected: false
dc:publisher:
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.
