Natural language processing (NLP) is a subfield of artificial intelligence that aims to allow computers to understand human language. NLP involves analyzing and representing text or speech at different linguistic levels for applications like question answering or machine translation. Challenges for NLP include ambiguities in language like lexical, syntactic, semantic, and anaphoric ambiguities. Common NLP tasks include part-of-speech tagging, parsing, named entity recognition, and sentiment analysis. Applications of NLP include text processing, machine translation, speech processing, and converting text to speech.

1. Natural Language
Processing

2. NATURAL LANGUAGE PROCESSING
• A subfield of Artificial Intelligence
•An inter disciplinary subject
•Aim
•To build intelligent computers that can interact
human being like a human being!!
•NLP is the ability of a computer to understand
what a human is saying to it .

3. Natural language Processing is
theoretically motivated range of
computational techniques for analyzing
and representing naturally occurring
text/speech at one or more levels of
linguistic analysis for the purpose of
achieving human-like language
processing for a range of tasks or
applications.
Definition

4. Why NLP ?
Applications for processing
large amounts of texts require
NLP expertise

5. Phases of linguistic
analysis
1. Higher level corresponds to speech recognition
2. Lower level corresponds to natural language
processing

6. Ambiguities in Natural
language Processing
The basic definition of ambiguity, as generally
used in natural language processing, is
“capable of being understood in more than
one way” .

7. DIFFERENT TYPES OF AMBIGUITY
Lexical Ambiguity:
Lexical Ambiguity is the ambiguity of a single word.
Example: The word silver can be used as a noun, an adjective, or a
verb.
She bagged two silver medals.
She made a silver speech.
His worries had silvered his hair.

8. How to resolve Lexical
Ambiguity
Lexical ambiguity can be resolved by Lexical
category disambiguation i.e, parts-of-speech
tagging.
As many words may belong to more than one
lexical category part-of-speech tagging is the
process of assigning a part-of-speech or lexical
category such as a noun, verb, pronoun,
preposition, adverb, adjective etc. to each word in
a sentence.

9. Lexical Semantic Ambiguity:
The type of lexical ambiguity,
which occurs when a single word is associated with multiple
senses.
Example: bank, pen, fast, bat, cricket etc.
The tank was full of water.
I saw a military tank.
The occurrence of tank in both sentences corresponds
to the syntactic category noun, but their meanings are
different.

10. Lexical Semantic ambiguity resolved
using word sense disambiguation
(WSD) techniques where WSD aims at
automatically assigning the meaning of
the word in the context in a
computational manner.

11. Syntactic Ambiguity: The
structural ambiguities were syntactic
ambiguities.
Structural ambiguity is of two kinds:
Scope Ambiguity and Attachment
Ambiguity.

12. Scope ambiguity:
Scope ambiguity involves operators and
quantifiers.
Consider the example:
Old men and women were taken to safe locations.
The scope of the adjective (i.e., the amount of text it
qualifies) is ambiguous. That is , whether the structure (old
men and women) or ((old men) and women)?
The scope of quantifiers is often not clear and creates
ambiguity.
Every man loves a woman.
The interpretations can be, For every man there is a woman
and also it can be there is one particular woman who is
loved by every man.

13. Attachment Ambiguity
Attachment ambiguity arises from uncertainty of attaching a
phrase or clause to a part of a sentence.
Consider the example:
The man saw the girl with the telescope.
It is ambiguous whether the man saw a girl carrying a telescope,
or he saw her through his telescope.
The meaning is dependent on whether the preposition ‘with ’is
attached to the girl or the man.
Consider the example:
Buy books for children
Preposition Phrase ‘for children’ can be either adverbial and attach
to the verb buy or adjectival and attach to the object
noun books.

14. Semantic Ambiguity:
This occurs when the meaning of the words themselves can be
misinterpreted . Even after the syntax and the meanings of the
individual words have been resolved, there are two ways of reading
the sentence.
Consider the example,
Seema loves her mother and Sriya does too.
The interpretations can be Sriya loves Seema’s mother or
Sriya likes her own mother.
Semantic ambiguities born from the fact that generally a
computer is not in a position to distinguishing what is logical
from what is not.

15. Consider the example:
The car hit the pole while it was moving.
The interpretations can be The car, while moving, hit the pole
and The car hit the pole while the pole was moving.
The first interpretation is preferred to the second one
because we have a model of the world that helps us to
distinguish what is logical (or possible) from what is
not. To supply to a computer a model of the world is
not so easy.
Consider the example:
We saw his duck
Duck can refer to the person’s bird or to a motion he
made.
Semantic ambiguity happens when a sentence
contains an ambiguous word or phrase

16. Anaphoric Ambiguity:
A phrase or word refers to something
previously mentioned , but there is more than one possibility.
Consider the example,
The horse ran up the hill. It was very steep. It soon got tired.
The anaphoric reference of ‘it’ in the two situations cause
ambiguity.
Steep applies to surface hence ‘it’ can be hill. Tired applies to
animate object hence ‘it’ can be horse.

17. Applications of NLP
•Text Processing
•Morph Analyzer
•POS Tagging
•Parsing
•Machine Translation
•Speech Processing
•Text to Speech (TTS)

18. Text Processing
Text processing lets you process (natural)
language texts . You can detect the text’s
language , the quality of the writing , find
entity mentions , tag part-of-speech ,
extract dates ,extract locations ,or
determine the sentiment of the text .

19. Morph Analyzer
Morphology is a part of linguistics that deals
with the study of words ,their internal structure
and partially their meanings
Morph Analyzer is a program for analyzing the
morphology of an input word. It detects the
morphemes of any text
Example :
Phrase level Morph Analyzer,Word level
Morph Analyzer.

20. POS TAGGER
A Part-Of-Speech Tagger (POS Tagger) is a
piece of software that reads text in some
language and assigns part of speech to
each word ,such as noun verb ,adjective ,
etc ., although generally computational
applications use more fine-grained POS
tags like ‘noun-plural’.

21. Parsing
In computer technology , a parser is a program , usually
part of compiler , that receives input in the form of
sequential source program instructions ,interactive online
commands ,markup tags ,or some other defined interface
and breaks them into parts (for example ,the nouns
(objects , verbs (methods), and their attributes or options)
that can then be managed by other programming (for
example other components in a compiler).
A parser may also check to see that all input has been
provided that is necessary .

22. Machine Translation
•Machine translation is a translation of a text by a
computer with no human involvement.
•Machine translation system use combination of language
and grammar rules plus dictionary for common words

23. Speech Processing
Speech processing is the study of speech signals
and the processing methods of these signals .The
signals are usually processed in digital
representation , so speech processing can be
regarded as a special case of digital signal
processing , applied to speech signal.

24. Text to Speech(TTS)
•A TTS converts normal language text into
speech .
•Synthesized speech can be created by
concatenating pieces of recorded speech that
are stored in database.