1. Natural Language Processing (NLP)
Lecture No 2
Institute of Southern Punjab Multan
Department of Computer Science

2. Language Processing Level 1 – Speech sound (Phonetics & Phonology)
Level 2 – Words & their forms (Morphology, Lexicon)
Level 3 – Structure of sentences (Syntax, Parsing)
Level 4 – Meaning of sentences (Semantics)
Level 5 – Meaning in context & for a purpose (Pragmatics)
Level 6 – Connected sentence processing in a larger body of text (Discourse)

3. Levels of Text Processing
Word Level
Words Properties
Stop-Words
Stemming
Frequent N-Grams
Thesaurus (WordNet)
Sentence Level
Document Level
Document-Collection Level
Linked-Document-Collection Level
Application Level

4. Language Processing Pipeline
speech
text
Phonetic/Phonological Analysis
OCR/Tokenization
POS tagging
Morphological and lexical analysis
WSD
Shallow parsing
Syntactic analysis
Deep Parsing
Semantic Interpretation
Anaphora resolution
Discourse Processing
Integration

5. Some Building Blocks Source Language Analysis
Target Language Generation
Text Normalization
Text Rendering
Morphological Analysis
Morphological Synthesis
POS Tagging
Phrase Generation
Parsing
Role Ordering
Semantic Analysis
Lexical Choice
Discourse Analysis
Discourse Planning

6. Stages in a Comprehensive NLP System
Tokenization
Morphological Analysis
Syntactic Analysis
Semantic Analysis (lexical and compositional)
Pragmatics and Discourse Analysis
Knowledge-Based Reasoning
Text generation

7. Tokenization-Overview
Tokenization is the process of breaking up the given text into units called tokens.
The tokens may be words or number or punctuation mark.
Tokenization does this task by locating word boundaries. Ending point of a word and beginning of the next word is called word boundaries.
Tokenization is also known as word segmentation.

8. Tokenizer
A tokenizer that divides a string into substrings by splitting on the specified string.
Whitespace Tokenizer
Whitespace And Punctuation Tokenizer
Classifier Based Tokenizer
Regex Tokenizer
Penn TreeBank Tokenizer

9. Natural Language ToolKit (NLTK)
&
Python

10. Python
Programming language is Python
Python is freely available for many platforms from the Python Software Foundation: –
– Named for the group Monty Python
Characteristics of Python
Easy-to-learn scripting language
Similar in many aspects to Perl
Object-oriented, with modules, classes, exceptions, high level dynamic data types
Similar to Java
Strongly typed, but without type declarations (dynamic typing)
Regular Expressions and other string processing features

11. Natural Language Toolkit (NLTK)
NL ToolKit provides libraries of many of the common NLP processes at various language levels
A suite of Python libraries for symbolic and statistical natural language programming
Developed at the University of Pennsylvania
NLTK provides: -
Basic classes for representing data relevant to Natural Language Processing.
Standard interfaces for performing NLP tasks such as tokenization, tagging and parsing
Standard implementation of each task which can be combined to solve complex problems

12. NLTK Modules corpora: a package containing modules of example text
tokenize: functions to separate text strings
probability: for modeling frequency distributions and probabilistic systems
stem – package of functions to stem words of text
wordnet – interface to the WordNet lexical resource
chunk – identify short non-nested phrases in text
etree: for hierarchical structure over text
tag: tagging each word with part-of-speech, sense, etc.
parse: building trees over text - recursive descent, shift-reduce, probabilistic, etc.
cluster: clustering algorithms
draw: visualize NLP structures and processes
contrib: various pieces of software from outside contributors

13. Lab Session / Pracical Activity
1- Python and NLTK Download and Installation
2- Tokenization Demo in Python (Word Level Tokenization)

14. Research Task Search and Download Papers Regarding Text Classification and read Abstract , Introduction, Literature Review to Identify Problem Statement, Proposed Solution and Pre-Processing Steps used.

15. END OF LECTURE