natural language processing artificial intelligence
-
Upload
lince-sebastian -
Category
Documents
-
view
224 -
download
0
Transcript of natural language processing artificial intelligence
-
8/9/2019 natural language processing artificial intelligence
1/81
Module 5
-
8/9/2019 natural language processing artificial intelligence
2/81
Natural language Processing
-
8/9/2019 natural language processing artificial intelligence
3/81
Two of the most difficult tasks that facing AI researchers are
- developing programs that understand Natural language &
-comprehend Visual scenes
eveloping Programs that can understand Natural
!anguage is ver" difficult# $h"%
Natural languages are large
The" contain a numer of different sentences#
New sentences can alwa"s e produced#
There is amiguit" in a natural languageMan" words have several meanings and sentences can
have several meanings in different conte'ts
-
8/9/2019 natural language processing artificial intelligence
4/81
English sentences are incomplete descriptions of the information.
-some dogs are outside.
The same expression means different things in different contexts:
Where is the water ?
Advt: communicate about an infinite world using a finite number ofsymbols.
o natural language pgm can be complete because new words!
expressions and meanings can be generated "uite freely.There are lots of ways to say the same thing.
#ary was born on $ctober%%.
#ary&s birthday is on $ctober %%.
-
8/9/2019 natural language processing artificial intelligence
5/81
(verview of !inguistics
'inguistics ( study of language
'evels of )nowledge used in atural language understanding
%. *honological )nowledge
-)nowledge which relates sounds to the words
Phoneme –smallest unit of sound
+. #orphological )nowledge
- lexical )nowledge related to word constructions from basic units called morphemes.
Morphemes- smallest unit of meaning
,.yntactic )nowledge
.emantic )nowledge
/.*ragmatic )nowledge
0.World )nowledge
-
8/9/2019 natural language processing artificial intelligence
6/81
-
8/9/2019 natural language processing artificial intelligence
7/81
)rammers and languages
!anguage * can e considered as a set of strings of finite or infinite
length+tring * constructed " concatenating s"mols, alphaets
Alphaets * s"mols of the language#
+entences are constructed using a set of rules called grammer# !anguage generated " grammer ) * !,)
)rammer ) can e defined as ) . ,Vn/ Vt/ s/ p
Terminal s"mols
* s"mols which cannot e decomposed further#
eg0 ad1ectives / nouns or vers in 2nglish
NonTerminals s"mols * can e decomposed further or e'panded " rules#
eg0 Noun phrases or Ver phrases
-
8/9/2019 natural language processing artificial intelligence
8/81
Most common wa" to represent grammers is as a set of production rules
+ NP VP
NP A3T N
NP N
VP V NP
N o" 4 popsicle 4 frog
V ate 4 kissed 4flew
A3T the 4 a
$ith this )/ following sentence can generated0
The o" ate a popsicle The frog kissed a o"
A o" ate the frog
-
8/9/2019 natural language processing artificial intelligence
9/81
+ NP VP A3T N VP
the N VP
the o" VP
the o" V NP
the o" ate NP
the o" ate A3T N
the o" ate a N
the o" ate a popsicle#
A grammer does not gurantee the generation of meaningful sentences/onl" that the" are structurall" correct#
The Popsicle flew a frog6
-
8/9/2019 natural language processing artificial intelligence
10/81
+tructural 3epresentations
+entences can e represented as a tree or graph to e'pose
the structure of the constituent parts#
+
NP VP
A3T N
the o"
V NP
A3T N
a popsiclePhase marker or s"ntactic tree
ate
-
8/9/2019 natural language processing artificial intelligence
11/81
7asic Parsing Techni8ues
The process of determining the s"ntactical structure of a
sentence is known as parsing#
The process of anal"9ing a sentence " taking it apart
word-" word and determining its structure from its
constituent parts and su parts#
The structure of a sentence can e represented with a
s"ntactic tree or a list
-
8/9/2019 natural language processing artificial intelligence
12/81
To parse a sentence/ it is necessar" to find a wa" in which that
sentence could have een generated from the start s"mol# This
can e done in two wa"s0
Top-own Parsing
* 7egin with the start s"mol and appl" the grammar rules forwarduntil the s"mols at the terminals of the tree correspond to the
components of the sentence eing parsed
7ottom-up Parsing
- 7egin with the sentence to e parsed and appl" the grammar rules
ackward until a single tree whose terminals are the words of the
sentence and whose top node is the start s"mol has een
produced#
-
8/9/2019 natural language processing artificial intelligence
13/81
Parsing an input to create an output
structure
Input string Parser (utput
representation
structure
!e'icon
-
8/9/2019 natural language processing artificial intelligence
14/81
:ath" 1umped the horse
* 8*
8*
9athy 8*
9athy 8 *
9athy umped *
9athy umped A;T
9athy umped the 9athy umped the house
9athy umped the horse
umped the horse
8 the horse
8 A;T horse
* 8 A;T
* 8 * * 8*
Top down Parsing 7ottom up Parsing
-
8/9/2019 natural language processing artificial intelligence
15/81
The !e'icon
A dictionary of words! where each word contains some syntactic!
semantic and possibly some pragmatic information
-
8/9/2019 natural language processing artificial intelligence
16/81
T"pical entries in a le'icon
$ord T"pe ;eatures
a eterminer
eVer Trans0intransitive
7o" Noun
?an Noun
?arried ver form0 past/ past participle
0
0
(range ad1ective
Noun
To preposition
-
8/9/2019 natural language processing artificial intelligence
17/81
Bnderstanding written te't is easier than understanding
speech#
)eneral approaches to natural language Bnderstanding
The use of ke"word and pattern matching#
+"ntactic and semantic directed anal"sis#?omparing and matching the input to real world situations#
(f these second approach is the most popular one#
-
8/9/2019 natural language processing artificial intelligence
18/81
Transformational )rammars
Provide a mechanism to produce single representations for
sentences having the same meanings through a series
of transformations
)enerative )rammers
-produce different structures for sentences having
different s"ntactical forms even though the" ma" havethe same semantic content#
?onsider the following sentences
-
8/9/2019 natural language processing artificial intelligence
19/81
printed
NP VP
V NP
A3T
+
+usan N
the file
+
NP VP
N A3T
fileThe
printedwas
V PP
by susa
-
8/9/2019 natural language processing artificial intelligence
20/81
?ase )rammars
)rammer rules are written to descrie s"ntactic rather thansemantic regularities#
,printed ,agent +usan
,o1ect ;ile
Mother aked for three hours
,aked ,agent Mother
,timeperiod =-hours
,aked ,(1ect Pie
,timeperiod =-hours
-
8/9/2019 natural language processing artificial intelligence
21/81
6ifferent 3ases are used by 3ase grammer are
=A> Agent ( 5nstigator of the action=animate>
5nstrument - 3ause of the event or obect used in causing theevent=inanimate>
=6> 6ative- Entity affected by the action.=animate>
='> 'ocative- *lace of the event
=> ource ( *lace from which something moves
=> oal ( *lace to which something moves
=T> Time ( Time at which the event occurred.
=$> $bect ( Entity that is acted upon or that changes!
6escribe relationships between verbs and their arguments.
-
8/9/2019 natural language processing artificial intelligence
22/81
The process of parsing into a case representation is heavily directed
by the lexical entries associated with each verb
open @ $ =5> =A>B
The door opened
7ohn opened the door
7ohn open the door with a chisel.6ie @ 6B
7ohn died
9ill @ 6 =5> AB
1ill )illed 7ohn
1ill )illed 7ohn with a )nife.
-
8/9/2019 natural language processing artificial intelligence
23/81
*arsing using a case grammer is expectation-driven
-
8/9/2019 natural language processing artificial intelligence
24/81
Transition networks
Another popular method used to represent formal and
natural language structures
7ased on the application of directed graphs,digraphs and
finite state automata#
?onsists of a numer of nodes and laeled arcs#
-
8/9/2019 natural language processing artificial intelligence
25/81
+emantic Anal"sis and
3epresentation structures+emantic interpretation is the most difficult stage in the transformation
process#
The domain refers to the knowledge that is part of the world model the
s"stem knows aout#
-includes o1ect descriptions/ relationships and other relevant
concepts#
The conte't relates to previous e'pressions/ the setting and time of the
utterances / and the eliefs/ desires and intentions of the speakers#
The task is part of the service the s"stem offers/ such as retrieving
information from a data ase/ providing e'pert advice/ or performing
a language translation#
-
8/9/2019 natural language processing artificial intelligence
26/81
'exical semantics Approaches
%. based on emantic grammars
+.uses conceptual dependency theory.
emantic rammar
- a context free grammar in which the choice of nonterm inals and
production rules is governed by semantics as well as syntactic
function.
- there is usually a semantic action associated with each grammar rule.
Eg: *rimitive action 5ET with unfilled slots A3T$;!$17E3T and
TEE
=5ET =A3T$; nil>
=$17E3T nil>
=TEE past>
-
8/9/2019 natural language processing artificial intelligence
27/81
,IN)2+T ,A?T(3 nil
,(7C2?T nil
,T2N+2 past
The o" drank a soda
,IN)2+T ,A?T(3 ,PP NAM2 o",?!A++ PDE-(7C
,TEP2 ANIMAT2,32; 2;
,(7C2?T ,PP,NAM2 soda,?!A++ PDE-(7C
,TEP2 INANIMAT2,32; IN2;
,T2N+2 past
-
8/9/2019 natural language processing artificial intelligence
28/81
?ompositional semantics Approaches
The meaning of an e'pression is derived from the meanings of the parts
of the e'pression#
- The target knowledge structures constructed in this approach are
t"picall" logic e'pressions such as the formulas of ;(P!#
2g0 N! statement - Sample24 contains silicon
3esult of parsing
(S DCL
(NP (N Sample 24)))
(AU (!"NS"(P#"S"N!)))
(VP (V contain))
(NP (N (silicon))))Bsing this structure/ the semantic interpreter would produce the following
predicate clause
(C$N!A%N sample24 silicon)
-
8/9/2019 natural language processing artificial intelligence
29/81
Natural language )eneration
2'act inverse of language undestanding#
More difficult than understanding/ecause the s"stem must
decide
- what to sa"/ andhow the utterances should e stated
which form is etter,active or passive
which words and structures est e'press the intent
when to sa" what#
-
8/9/2019 natural language processing artificial intelligence
30/81
The stud" of language generation falls naturall" into three areas0
@ the determination of content
formulating and developing a te't utterance plan/ and
= achieving a reali9ation of desired utterances#
?ontent determination
?oncerned with what details to include in an e'planation/ a re8uest/ a
8uestion or argument in order to conve" the meanings set forth "
the goals of the speaker#
Te't planning
Process of organi9ing the content to e communicated so as to
achieve the goals of the speaker#
3eali9ation
* the process of mapping the organi9ed content to actual te't#
-
8/9/2019 natural language processing artificial intelligence
31/81
-
8/9/2019 natural language processing artificial intelligence
32/81
Pattern 3ecognition
3omputer pattern recognition
- a process whereby computer programs are used to recogniCe
various forms of input stimuli such as visual or acoustic=speech>
patterns.
*attern recognition ystems are used to identify or classify
obects on the basis of their attribute and attribute-relation
values.
;ecognition is the process of establishing a close match between
some new stimulus and previously stored stimulus patterns.
-
8/9/2019 natural language processing artificial intelligence
33/81
D $bect classification is closely related to recognition.
D The ability to classify or group obects according to
some commonly shared features is a form of class
recognition.D 3lassification is
- essential for decision ma)ing! learning! and many
other cognitive acts.- 6epends on the ability to discover common patterns
among obects.
-
8/9/2019 natural language processing artificial intelligence
34/81
!&e reco'nition and classiication process
tep %
- stimuli produced by obects are perceived by sensory devices.The more prominent attributes= such as siCe! shape! color! andtexture> produce the strongest stimuli. The values of these attributesand their relations are used to characteriCe an obect in the form of a
pattern vector
- The range of characteristic attribute values is )nown as themeasurement space #
tep +
A subset of attributes whose values provide obect grouping or
clustering are selected. The range of the subset of attribute values is )nown as the feature
space F.
-
8/9/2019 natural language processing artificial intelligence
35/81
tep ,
-
8/9/2019 natural language processing artificial intelligence
36/81
The pattern recognition process
;eature
selection Matching
?lassification rules
+ensors
!earning
?lassification
+timuli
-
8/9/2019 natural language processing artificial intelligence
37/81
F There are two asic approaches to the
recognition prolem
@The decision theoretic approach
The s"ntactic approach
-
8/9/2019 natural language processing artificial intelligence
38/81
ecision Theoretic classification
F 7ased on the use of decision functions to classif" o1ects#
F A decision function maps pattern vectors G into decision
regions of #
+"ntactic ?lassification
-The s"ntactic recognition approach is ased on the
uni8ueness of s"ntactic structure6 among the o1ect
classes#
- a kind of grammar is defined for o1ect descriptions# - vocaula" is ased or shape primitives#
-
8/9/2019 natural language processing artificial intelligence
39/81
!earning ?lassification Patterns
F 7efore a s"stem can recogni9e o1ects/ it must posses
knowledge of the characteristics features for those
o1ects
F !earning decision functions/ grammars or other rules can
e performed in either of the two wa"s/ through
F +upervised learning
F Bnsupervised learning
-
8/9/2019 natural language processing artificial intelligence
40/81
+upervised !earning
- accomplished " presenting training e'amples to alearning unit#
The e'amples are laelled eforehand with their correct
identities or class# The attriute values and o1ect laelsare used " the learning component to e'tract and
determine pattern criteria for each class# This knowledge
is used to ad1ust parameters in decision functions or
grammer rewrite rules#
-
8/9/2019 natural language processing artificial intelligence
41/81
Bnsupervised !earning
- !aled training e'amples are not availale and little is
known eforehand regarding the o1ect population#In
such cases/ the s"stem must e ale to perceive and
e'tract relevant properties from the unknown o1ects/find common pattern among them/ and formulate
descriptions or discrimination criteria consistent with the
goals of the recognition process#
-
8/9/2019 natural language processing artificial intelligence
42/81
!earning through ?lustering
- 3lustering is the process of grouping or classifying obects on the basis of
a close association or shared characteristics.
- a discovery learning process in which similar patterns are found among a
group of obects.
The clustering problem gives rise to several subproblems
%. What set of attributes and relations are most relevant! and what weights
should be given to each?
+. What representation formalism should be used to characteriCe the obects?
,. What representation scheme should be used to describe the cluster
groupings or classifications?. What clustering criteria is most consistent with and effective in achieving
the obectives relative to the context or domain?
/. What clustering algorithms can best meet the criteria
-
8/9/2019 natural language processing artificial intelligence
43/81
-
8/9/2019 natural language processing artificial intelligence
44/81
F To covert speech to on- screen te't / a computer has to go through severalcomple' steps#
$hen we speak we create virations in the air# The analog-to-digitalconverter,A? translates this analog wave into digital data that thecomputer can understand#
To do this / it digiti9es the sound " taking precise measurements ofthe wave at fre8uent intervals# The s"stem filters the digiti9ed sound toremove unwanted noise/ and sometimes to separate it into different andsof fre8uenc"#
Ne't the signal is divided into small segments and the program thenmatches these segments to known phonemes in appropriate language# Aphoneme is the smallest element of a language- a representation of thesounds we make and put together to form meaningful e'pressions# The
program e'amines phonemes in the conte't of other phonemes aroundthem# The software language model compares the phonemes to wordsin its uilt-in dictionar"# The program then determines what the user wasproal" sa"ing and either outputs it as te't or issues a computercommand
Dow speech recognition works %
-
8/9/2019 natural language processing artificial intelligence
45/81
-
8/9/2019 natural language processing artificial intelligence
46/81
2'pert +"stem Architectures
-
8/9/2019 natural language processing artificial intelligence
47/81
"pert Systems
* a recent product o A%
* a +ind o +nowled'e based systems
* &a,e pro,en to be eecti,e in a number o problem
domains w&ic& re-uire t&e +ind o intelli'ence
possessed by a &uman epert.
Application Domains
Law aerospace
C&emistry military operations
/iolo'y inance
"n'ineerin' ban+in'
0edicine 'eolo'y
manuacturin'
-
8/9/2019 natural language processing artificial intelligence
48/81
efinition
A set o pro'rams desi'ned to act as anepert in a particular domain.
ot meant for replacing experts in that domain!but to assistthem.
?h t i ti f t f 2 t
-
8/9/2019 natural language processing artificial intelligence
49/81
?haracteristic features of 2'pert
s"stems Use +nowled'e rat&er t&an data
1nowled'e is encoded and maintained separately.
Capable o eplainin' &ow a particular conclusion
was reac&ed
Use symbolic representations or +nowled'e
Can reason wit& meta +nowled'e
%mportance o "pert Systems
-
8/9/2019 natural language processing artificial intelligence
50/81
2'pert +"stem Architectures
@ #ule based System or Production Systems
-use +nowled'e encoded in t&e orm o production rulesie . % .....t&en... rules.
"ac& rule represents a small piece o +nowled'erelatin' to t&e 'i,en domain o epertise.
? t f 2 t
-
8/9/2019 natural language processing artificial intelligence
51/81
?omponents of an 2'pert
+"stem
"planation0odule
%$%nterace
"ditor
%nerence"n'ine
1nowled'e base
Case &istory
ile
3or+in'memory
Learnin' 0odule
%NPU!
$U!PU!
-
8/9/2019 natural language processing artificial intelligence
52/81
1nowled'e /ase
Contains acts and rules about some domain.
"'
%5 !&e patient &as a c&ronic disorder6and t&e a'e o t&e patient is less t&an 786 and
t&e patient s&ows condition A6 and
test / re,eals bioc&emistry condition C
!9"N Conclude t&e patient:s dia'nosis is autoimmune*
c&ronic*&epatitis
-
8/9/2019 natural language processing artificial intelligence
53/81
-
8/9/2019 natural language processing artificial intelligence
54/81
The inferring process is carried out
recursivel" in three stages0
@ match
select
= e'ecute
The Production s"stem Inference c"cle
-
8/9/2019 natural language processing artificial intelligence
55/81
1nowled'e base 3or+in' 0emory
matc&
Conlict Set
Select
eecute
The Production s"stem Inference c"cle
-
8/9/2019 natural language processing artificial intelligence
56/81
7uilding a :nowledge ase
An editor is used by de,elopers to create new rulesor addition to t&e +nowled'e base6 to delete
outdated rules 6or to modiy eistin' rules in some
way.
0ost diicult tas+ in creatin' and maintainin'production systems is
*buildin' and maintainin' o a consistent but complete
set o rules. !&is s&ould be done wit&out addin'
redundant or unnecessary rules. "' o an intelli'ent editor < !"%#"S%AS (de,eloped to
wor+ wit& systems li+e 0=C%N)
-
8/9/2019 natural language processing artificial intelligence
57/81
IH( Interface
Permits the user to communicate with the s"stem in a
more natural wa"#
The s"stem must have special prompts or a speciali9ed
vocaular" which encompasses the terminolog" of the
given domain of e'pertise#
2g0 ME?IN has a vocaular" of some words#
-
8/9/2019 natural language processing artificial intelligence
58/81
!earning module and histor" file
- Not common components of e'pert
s"stems
- Bsed to assist in uilding and refining the
knowledge ase
Non Production s"stem
-
8/9/2019 natural language processing artificial intelligence
59/81
Non Production s"stem
Architectures- less common e'pert s"stem architecture#
- Instead of rules/ these s"stems emplo" more structured
representation schemes like
Associative or semantic networks
;rame structures
ecision trees
+peciali9ed networks like neural networks#
Associative or +emantic Network
-
8/9/2019 natural language processing artificial intelligence
60/81
Associative or +emantic Network
Architectures- useful in representing hierarchical knowledge structures/
where propert" inheritance is common#
- Not a popular form of representation for standard e'pert
s"stems#
- can e used in natural language s"stems or computer
vision s"stems also#
2g0 2'pert s"stem ased on the use of an associative
network representation * ?A+N2T
?A+N2T * ?ausal Associational Network
-used to diagnose and recommend treatment for
)laucoma
-
8/9/2019 natural language processing artificial intelligence
61/81
bird
ly
tweetyyellow
win's
CAN
A*1%ND*$5
C$L$#
9AS PA#!S
5ra'ment o an associati,e networ+
-
8/9/2019 natural language processing artificial intelligence
62/81
/ob Proessor
/i+e9ouseSandy
0A##%"D!$ $3NS
%SA
D#%V"S
-
8/9/2019 natural language processing artificial intelligence
63/81
5rame Arc&itectures
2g0 for a frame ased e'pert s"stem - PIP s"stem
PIP * Present Illness Program
Medical knowledge in PIP is organi9ed in frame structures#
:nowledge for e'pert s"stems ma" e stored in the form
of a decision tree when the knowledge can e structured
in a top-to-ottom manner#
:nowledge ase can e constructed with a special tree-uilding editor or with a learning module#
Decision !ree Arc&itectures
A se'ment o decision tree structure
-
8/9/2019 natural language processing artificial intelligence
64/81
A se'ment o decision tree structure
attribute>
oran'ered blue
yes no yes yesno no
/urn test
Solubility test
Compound*7? Compound*7@
J J J J J J J J J J J J J J J J J J J J J J J J J J J J J J J J J J J
-
8/9/2019 natural language processing artificial intelligence
65/81
-
8/9/2019 natural language processing artificial intelligence
66/81
7lackoard +"stem Architecture
- a special type o +nowled'e*based system w&ic& uses aorm o opportunistic reasonin'.
- Diers rom pure orward or pure bac+ward c&ainin'
- "it&er direction may be c&osen dynamically at eac&
sta'e in t&e problem solution process.
- /lac+board systems are composed o
a number o +nowled'e sources
a 'lobally accessible database structure6called ablac+board
Control %normation
-
8/9/2019 natural language processing artificial intelligence
67/81
1igsaw pu99le
A pule consistin' o a mass o irre'ularly s&aped pieceso cardboard6 plastic6 or wood t&at orm a picture w&enitted to'et&er. Also called picture puzzle.
?omponents of lackoard s"stems
-
8/9/2019 natural language processing artificial intelligence
68/81
?omponents of lackoard s"stems
1nowled'e sources
Control %normation
/lac+board
:nowledge sources
-
8/9/2019 natural language processing artificial intelligence
69/81
- separate and independent sets of coded knowledge
- ma" contain knowledge in the form of procedures/ rules/ or
other schemes#
2ach knowledge source ma" e thought of as a specialist in
some limited area needed to solve a given suset of
prolems
7lackoard
- ?ontain current prolem state and information needed " the
knowledge sources such as input data/ partial solutions/control data/ alternatives/ final solutions
- :nowledge sources make changes to the lackoard data#
- ?ommunication and interaction etween the knowledge
sources takes place solel" through the lack oard#
? t l I f ti
-
8/9/2019 natural language processing artificial intelligence
70/81
?ontrol Information
- Ma" e contained within the sources/ on the lack oard/
or possil" in a separate module#
- Monitors the changes to the lackoard and determines
what the immediate focus of attention should e in solving
the prolem#
- (ne of the application of 7lackoard +"stem Architecture
was in the D2A3+AE famil" of pro1ects,speech
understanding s"stems
Analogical 3easoning
-
8/9/2019 natural language processing artificial intelligence
71/81
Analogical 3easoning
Architectures- sol,e new problems li+e &umans6 by indin' a similar
problem solution t&at is +nown and applyin' t&e +nown
solution to t&e new problem6 possibly wit& some
modiications.
- 3ill re-uire a lar'e +nowled'e base &a,in' numeroussolutions and ot&er pre,iously encountered situations
or episodes.
- !&e inerence mec&anism must be able to etend
+nown situations or solutions to it t&e current problemand ,eriy t&at t&e etended solution is reasonable.
-
8/9/2019 natural language processing artificial intelligence
72/81
Neural Network Architectures
-
8/9/2019 natural language processing artificial intelligence
73/81
Artificial Neural networks
-
8/9/2019 natural language processing artificial intelligence
74/81
Artificial Neural networks
ANN are mathematical inventions inspired by observations
made in the study of biological system.
Loosely based on the actual Biology
Can be described as mapping an input space to an output
space.
Consists of artificial neurons composed of eights and
connections.
-
8/9/2019 natural language processing artificial intelligence
75/81
-
8/9/2019 natural language processing artificial intelligence
76/81
Modeling Neurons
A simplified model of the neuron
%
NP
U
!
S
$U!PU!
Articial neuron can e thought of as a small computing engine that takes in
input/ process them and then transmit an output#
K f L$ G7
-
8/9/2019 natural language processing artificial intelligence
77/81
K.f L$i Gi
7
2
>
8
3732
3>
38
B
i 8
-
8/9/2019 natural language processing artificial intelligence
78/81
-
8/9/2019 natural language processing artificial intelligence
79/81
Neural Network Architecture
Neural networks'arge networ)s of simple processing elements or nodes which process
information dynamically in response to external inputs
The nodes are simplified models of neurons.
The )nowledge in a neural networ) is distributed throughout the networ) inthe form of internode connections and weighted lin)s which form the
inputs to the nodes.
The lin) weights serve to enhance or inhibit the input stimuli values which are
then added together at the nodes.5f the sum of all the inputs to a node
exceeds some threshold value T! the node executes and produces an outputwhich is passed on to other nodes or is used to produce some output
response.
o output is produced if the total input is less than T
:nowledge +"stem 7uilding
-
8/9/2019 natural language processing artificial intelligence
80/81
:nowledge +"stem 7uilding
Tools- t&ese tools ran'e rom &i'& le,el pro'rammin'
lan'ua'es to intelli'ent editors.
3&en e,aluatin' buildin' tools or epert system
de,elopment6 t&e de,eloper s&ould consider t&e
ollowin' eatures and capabilities
>. 1nowled'e representation met&ods a,ailable.
2. %nerence and control met&ods a,ailable.
7. User interace c&aracteristics.
4. eneral system c&aracteristics and support a,ailable.
-
8/9/2019 natural language processing artificial intelligence
81/81
Personal ?onsultant Plus
3adian 3ule master
:22,:nowledge 2ngineering 2nvironment
(P+5 +"stem