Natural Language Processing (NLP): Understanding Ambiguity and Processing Language, Summaries of Artificial Intelligence

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• NLP UNIT-

Natural Language Processing

(NLP)

• (^) Natural Language Processing (NLP) is the sub-field of Computer

Science especially Artificial Intelligence (AI) that is concerned about

enabling computers to understand and process human language.

Technically, the main task of NLP would be to program computers for

analyzing and processing huge amount of natural language data.

• (^) Natural language processing comprises of understanding and

generation.

3. Semantic Ambiguity

• (^) This kind of ambiguity occurs when the meaning of the words themselves can be misinterpreted. In

other words, semantic ambiguity happens when a sentence contains an ambiguous word or phrase.

Ex:“The car hit the pole while it was moving” is having semantic ambiguity because the

interpretations can be “The car, while moving, hit the pole” and “The car hit the pole while the pole

was moving”.

4. Anaphoric Ambiguity

• (^) This kind of ambiguity arises due to the use of anaphora entities in discourse.

EX: The horse ran up the hill. It was very steep. It soon got tired. Here, the anaphoric reference of “it”

in two situations cause ambiguity.

5. Pragmatic ambiguity

• (^) Such kind of ambiguity refers to the situation where the context of a phrase gives it multiple

interpretations. In simple words, we can say that pragmatic ambiguity arises when the statement is

not specific.

Ex: “I like you too” can have multiple interpretations like I like you (just like you like me), I like you

(just like someone else dose).

NLP Phases

1. Morphological Processing

• (^) It is the first phase of NLP. The purpose of this phase is to break chunks of language input into sets of tokens corresponding to

paragraphs, sentences and words. For example, a word like “uneasy” can be broken into two sub-word tokens as “un-easy”.

2. Syntax Analysis

• (^) It is the second phase of NLP. The purpose of this phase is two folds: to check that a sentence is well formed or not and to

break it up into a structure that shows the syntactic relationships between the different words. For example, the sentence like

“The school goes to the boy” would be rejected by syntax analyzer or parser.

3. Semantic Analysis

• (^) It is the third phase of NLP. The purpose of this phase is to draw exact meaning, or you can say dictionary meaning from the

text. The text is checked for meaningfulness. For example, semantic analyzer would reject a sentence like “Hot ice-cream”.

4. Pragmatic Analysis

• (^) It is the fourth phase of NLP. Pragmatic analysis simply fits the actual objects/events, which exist in a given context with object

references obtained during the last phase (semantic analysis). For example, the sentence “Put the banana in the basket on the

shelf” can have two semantic interpretations and pragmatic analyzer will choose between these two possibilities.

5. Discourse Analysis

• (^) While processing a language there can arise one major ambiguity known as referential ambiguity. Referential ambiguity is the

ambiguity that can arise when a reference to a word cannot be determined. In discourse analysis we try to remove this

ambiguity.

Types of Morphemes

Morphemes, the smallest meaning-bearing units, can be divided into two types −

1. Stems 2. Word Order

1. Stems

It is the core meaningful unit of a word. We can also say that it is the root of the word. For

example, in the word foxes, the stem is fox.

• (^) Affixes − As the name suggests, they add some additional meaning and grammatical

functions to the words. For example, in the word foxes, the affix is − es.

Further, affixes can also be divided into following four types −

• (^) Prefixes − As the name suggests, prefixes precede the stem. For example, in the word

unbuckle, un is the prefix.

• (^) Suffixes − As the name suggests, suffixes follow the stem. For example, in the word cats,

-s is the suffix.

• (^) Infixes − As the name suggests, infixes are inserted inside the stem. For example, the

word cupful, can be pluralized as cupsful by using -s as the infix.

Types of Morphemes

2. Word Order

The order of the words would be decided by morphological parsing. Let us now see the

requirements for building a morphological parser −

• (^) Lexicon

The very first requirement for building a morphological parser is lexicon, which includes the

list of stems and affixes along with the basic information about them. For example, the

information like whether the stem is Noun stem or Verb stem, etc.

• (^) Morphotactics

It is basically the model of morpheme ordering. In other sense, the model explaining which

classes of morphemes can follow other classes of morphemes inside a word. For example, the

morphotactic fact is that the English plural morpheme always follows the noun rather than

preceding it.

• (^) Orthographic rules

These spelling rules are used to model the changes occurring in a word. For example, the rule

of converting y to ie in word like city+s = cities not citys.

Syntactic Analysis

• (^) Concept of Parser

It is used to implement the task of parsing. It may be defined as the software

component designed for taking input data (text) and giving structural

representation of the input after checking for correct syntax as per formal

grammar. It also builds a data structure generally in the form of parse tree or

abstract syntax tree or other hierarchical structure.

The main roles of the parse include −

• (^) To report any syntax error.

To recover from commonly occurring error so that the processing of the

remainder of program can be continued.

• (^) To create parse tree.
• (^) To create symbol table.
• (^) To produce intermediate representations (IR).

Syntactic Analysis

Types of Parsing

Derivation divides parsing into the followings two types −

1. Top-down Parsing
2. Bottom-up Parsing 1. Top-down Parsing

• (^) In this kind of parsing, the parser starts constructing the parse tree from the start

symbol and then tries to transform the start symbol to the input. The most

common form of topdown parsing uses recursive procedure to process the input.

The main disadvantage of recursive descent parsing is backtracking.

2. Bottom-up Parsing

• (^) In this kind of parsing, the parser starts with the input symbol and tries to

construct the parser tree up to the start symbol.

Context Free Grammar (CFG)

• (^) CFG consists of finite set of grammar rules with the following four components −

 Set of Non-terminals

• (^) It is denoted by V. The non-terminals are syntactic variables that denote the sets of strings,

which further help defining the language, generated by the grammar

 Set of Terminals

• (^) It is also called tokens and defined by Σ. Strings are formed with the basic symbols of

terminals.

 Set of Productions

• (^) It is denoted by P. The set defines how the terminals and non-terminals can be combined.

Every production(P) consists of non-terminals, an arrow, and terminals (the sequence of

terminals). Non-terminals are called the left side of the production and terminals are called

the right side of the production.

 Start Symbol

• (^) The production begins from the start symbol. It is denoted by symbol S. Non-terminal

symbol is always designated as start symbol.

Part of Speech (POS ) Tagging

• (^) Part-of-Speech (PoS) tagging, then it may be defined as the process of assigning

one of the parts of speech to the given word. It is generally called POS tagging. In

simple words, we can say that POS tagging is a task of labelling each word in a

sentence with its appropriate part of speech. We already know that parts of

speech include nouns, verb, adverbs, adjectives, pronouns, conjunction and their

sub-categories.

• (^) Most of the POS tagging falls under Rule Base POS tagging, Stochastic POS

tagging and Transformation based tagging.

Construct parse tree for sentence using CFG rules

input string ----> “The man read this book”

rules:

S→NP VP

S→AUX NP VP

S→VP

NP→DET NOM

NOM→NOUN

VP→VERB NP

NOM→NOUN NOM

DET→that | this | a | the

NOUN→book| flight | meal | man

VERB→book| include | read

AUX→does

Construct parse tree for sentence using CFG rules

• (^) Ram ate the delicious cake.

S:Sentence-->SB VP OB

SB:Subject-->PN

VP:Verb Phrase-->ADV V | V

OB:Object-->the S

s1:Subset of S--->ADJ N

PN:Proper Noun-->Ram

ADJ:Adjective-->interesting | delicious

N:Noun-->book | cake

V:Verb--->read | ate

Semantic Analysis

Elements of Semantic Analysis

1. Hyponymy & Hypernymy

It may be defined as the relationship between a generic term and instances of that

generic term. Here the generic term is called hypernym and its instances are called

hyponyms.

Ex: Dog is a hyponym of animal.

Animal is a hypernym of dog.

Ex: Purple is a hypernym of voilet and purple is a hyponym of color.

2. Homonymy

It may be defined as the words having same spelling or same form but having

different and unrelated meaning.

Ex: Give me that cricket bat.

Anu saw a bat that was sleeping on a tree.

Semantic Analysis

3. Polysemy

Polysemy is a Greek word, which means “many signs”. It is a word or phrase with

different but related sense. In other words, we can say that polysemy has the

same spelling but different and related meaning.

Ex: the word “bank” is a polysemy word having the following meanings −

A financial institution.

The building in which such an institution is located.

4. Meronymy

• (^) Being part of something

Ex: Coconut is a meronym of coconut tree.