Preparing data

An important step in NLP is finding and preparing the data for processing. This includes the data for training purposes and the data that needs to be processed. There are several factors that need to be considered. Here, we will focus on the support Java provides for working with characters.

We need to consider how characters are represented. Although we will deal primarily with English text, other languages present unique problems. Not only are there differences in how a character can be encoded, the order in which text is read will vary. For example, Japanese orders its text in columns going from right to left.

There are also a number of possible encodings. These include ASCII, Latin, and Unicode to mention a few. A more complete list is found in the following table. Unicode, in particular, is a complex and extensive encoding scheme:

Encoding	Description
ASCII	A character-encoding using 128 (0-127) values.
Latin	There are several Latin variations that uses 256 values. They include various combination of the umlaut, and other characters. Different versions of Latin have been introduced to address various Indo-European languages, such as Turkish and Esperanto.
Big5	A two-byte encoding to address the Chinese character set.
Unicode	There are three encodings for Unicode: UTF-8, UTF-16, and UTF-32. These use 1, 2, and 4 bytes, respectively. This encoding is able to represent all known languages in existence today, including newer languages, such as Klingon and Elvish.

 

Java is capable of handling these encoding schemes. The javac executable's -encoding command-line option is used to specify the encoding scheme to use. In the following command line, the Big5 encoding scheme is specified:

javac -encoding Big5

Character-processing is supported using the primitive char data type, the Character class, and several other classes and interfaces, as summarized in the following table:

Character type	Description
char	Primitive data type.
Character	Wrapper class for char.
CharBuffer	This class supports a buffer of char, providing methods for get/put characters or a sequence of characters operations.
CharSequence	An interface implemented by CharBuffer, Segment, String, StringBuffer, and StringBuilder. It supports read-only access to a sequence of chars.

 

Java also provides a number of classes and interfaces to support strings. These are summarized in the following table. We will use these in many of our examples. The String, StringBuffer, and StringBuilder classes provide similar string-processing capabilities but differ in whether they can be modified and whether they are thread-safe. The CharacterIterator interface and the StringCharacterIterator class provide techniques to traverse character sequences.

The Segment class represents a fragment of text:

Class/interface	Description
String	An immutable string.
StringBuffer	Represents a modifiable string. It is thread-safe.
StringBuilder	Compatible with the StringBuffer class but is not thread-safe.
Segment	Represents a fragment of text in a character array. It provides rapid access to character data in an array.
CharacterIterator	Defines an iterator for text. It supports a bidirectional traversal of text.
StringCharacterIterator	A class that implements the CharacterIterator interface for a String.

 

We also need to consider the file format if we are reading from a file. Often, data is obtained from sources where the words are annotated. For example, if we use a web page as the source of text, we will find that it is marked up with HTML tags. These are not necessarily relevant to the analysis process and may need to be removed.

The Multipurpose Internet Mail Extensions (MIME) type is used to characterize the format used by a file. Common file types are listed in the following table. Either we need to explicitly remove or alter the markup found in a file, or use specialized software to deal with it. Some of the NLP APIs provide tools to deal with specialized file formats:

File format	MIME type	Description
Text	Plain/text	Simple text file
Office type Document	Application/MS Word application/vnd.oasis.opendocument.text	Microsoft Office Open Office
PDF	Application/PDF	Adobe Portable Document Format
HTML	Text/HTML	Web pages
XML	Text/XML	eXtensible Markup Language
Database	Not applicable	Data can be in a number of different formats

 

Many of the NLP APIs assume that the data is clean. When it is not, it needs to be cleaned, lest we get unreliable and misleading results.