2 why python for nlp

2 why python for nlp

• 2.
  Python is asimple yet powerful programming language with excellent functionality for processing linguistic data. We chose Python as the implementation language for NLTK because it has shallow learning curve, its syntax and semantics are transparent and it has good string- handling functionality.
• 3.
  As a scriptinglanguage, Python facilitates interactive exploration. As a dynamic language, Python permits attributes to be added to objects on the fly, and permits variables to be typed dynamically facilitating rapid development. As an object-oriented language, Python permits data and methods to be encapsulated and re-used easily.
• 4.
  Python comes withan extensive standard library, including components for graphical programming, numerical processing, and web data processing. Python is heavily used in industry, scientific research and education around the word. Python is often praised for the way it facilities productivity, quality, and maintainability of software.
• 5.
  NLTK defines abasic infrastructure can be used to build NLP programs in Python. NLTK was designed with six requirements in mind: i. Simplicity ii. Consistency iii. Extensibility iv. Modularity v. Well-documented vi. NLTK organization It provides: basic classes for representing data relevant to natural language processing: standard interfaces for performing tasks such tokenization, tagging and parsing; standard implementations for each task which can be combined to solve complex problems; and extensive documentation including tutorial and reference documentation.
• 6.
  Simplicity: We havetried to provide an intuitive and appealing framework along with substantial building blocks, for students to gain a practical knowledge of NLP without getting bogged down in the tedious house-keeping usually associated with processing annotated language data. We have provided software distributions for several platforms, along with platform-specific instructions, to make the toolkit easy to install.
• 7.
  We have madea significant effort to ensure that all the data structures and interfaces are consistent, making it easy to carry out a variety of tasks using a uniform framework.
• 8.
  The toolkit easilyaccommodates new components, whether those components replicate or extend existing functionality. Moreover, the toolkit is organized so that it is usually obvious where extensions would fit into the toolkit’s infrastructure.
• 9.
  The interaction betweendifferent components of the toolkit uses simple, well-defined interfaces. It is possible to complete individual projects using small parts of the toolkit. This allows students to learn how to use the toolkit incrementally throughout a course. Modularity also makes it easier to change and extend the toolkit.
• 10.
  The toolkit comeswith substantial documentation, including nomenclature, data structures, and implementations.
• 11.
  NLTK is organizedinto a collection of task-specific packages. Each package is a combination of data structures for representing a particular kind of information such as trees, and implementations of standard algorithms involving those structures such as parsers. This approach is a standard feature of object-oriented design, in which components encapsulate both the resources and methods needed to accomplish particular task.
• 12.
  A tokeniser isa program which takes input text and divides it into “tokens”. This is often a useful first step, because it means one can then count the number of words in a text, or count the number of different words in a text, or extract all the words that occur exactly 3 times in a Text, etc. Unix has some facilities which allow you to do this tokenisation. We start with tr. This “translates” characters. Typical usage is as follows:
• 13.
  tr 'a-z' 'A-Z'< inputfile > outputfile tr ’aiou’ e < inputfile > outputfile tr -c 'A-Za-z' '012' <inputfile> outputfile
• 14.
  See data inten-ling Typingin Malayalam
• 15.
  Any question?