perm filename CHAP4[4,KMC]24 blob sn#064008 filedate 1973-09-25 generic text, type T, neo UTF8
00100	LANGUAGE-RECOGNITION PROCESSES FOR UNDERSTANDING DIALOGUES 
00200		IN TELETYPED PSYCHIATRIC INTERVIEWS
00300	
00400		Since  the behavior being simulated by this paranoid model is
00500	the  sequential  language-behavior  of  a  paranoid  patient   in   a
00600	psychiatric  interview,  the  model  (PARRY)  must have an ability to
00700	interpret  and  respond  to  natural  language  input  to  a   degree
00800	sufficient  to  demonstrate  conduct  characteristic  of the paranoid
00900	mode.      By "natural  language"  I  shall  mean  ordinary  American
01000	English  such  as  is  used  in  everyday conversations.  It is still
01100	difficult to be explicit about the processes which enable  humans  to
01200	interpret  and  respond to natural language.  ("A mighty maze !   but
01300	not without a plan."  -  A.   Pope).    Philosophers,  linguists  and
01400	psychologists   have   investigated  natural  language  with  various
01500	purposes. Few  of  the  results  have  been  useful  to  builders  of
01600	interactive simulation models.  Attempts have been made in artificial
01700	intelligence  to  write  algorithims  which  "understand"   teletyped
01800	natural  language  expressions.      (Colby  and  Enea,1967; Enea and
01900	Colby,1973;   Schank,    Goldman,    Rieger,    and    Riesbeck,1973;
02000	Winograd,1973;  Woods,  1970).   Computer  understanding  of  natural
02100	language is actively being attempted today but it is not something to
02200	be  completly  achieved  today  or  even  tomorrow. For our model the
02300	problem at the moment was not to find immediately  the  best  way  of
02400	doing it but to find any way at all.
02500		During the 1960's when machine processing of natural language
02600	was  dominated  by  syntactic  considerations,  it  became clear that
02700	syntactical information alone  was  insufficient  to  comprehend  the
02800	expressions  of  ordinary  conversations.  A  current view is that to
02900	understand what information is contained in  linguistic  expressions,
03000	knowledge  of syntax and semantics must be combined with beliefs from
03100	a conceptual structure capable of making inferences.  How to  achieve
03200	this  combination  efficiently  with  a  large data-base represents a
03300	monumental task for both theory and implementation.
03400		For performance reasons we did not  attempt  to  construct  a
03500	conventional  linguistic parser to analyze conversational language of
03600	interviews. Parsers to date have had great difficulty  in  performing
03700	well  enough to assign a meaningful interpretation to the expressions
03800	of everyday conversational language in unrestricted English.   Purely
03900	syntactic parsers offer a cancerous proliferation of interpretations.
04000	A conventional parser, lacking neglecting  and  ignoring  mechanisms,
04100	may  simply  halt  when it comes across a word not in its dictionary.
04200	Parsers represent  tight  conjunctions  of  tests  instead  of  loose
04300	disjunctions needed for gleaning some degree of meaning from everyday
04400	language  communication.   It  is   easily   observed   that   people
04500	misunderstand  and  ununderstand  at  times and thus remain partially
04600	opaque to one another, a truth which lies at the core of  human  life
04700	and communication.
04800		How language is understood depends on  how  people  interpret
04900	the meanings of situations they find themselves in.  In  a  dialogue,
05000	language is understood in accordance with a participant's view of the
05100	situation. The participants are interested in both what an  utterance
05200	means  (what  it  refers  to)  and  what  the  utterer  means  (  his
05300	intentions). In a first psychiatric interview the doctor's  intention
05400	is to gather certain kinds of information; the patient's intention is
05500	to give information in order to receive help. Such  an  interview  is
05600	not  small  talk;  a  job is to be done. Our purpose was to develop a
05700	method for recognizing sequences of everyday English  sufficient  for
05800	the  model  to  communicate  linguistically  in a paranoid way in the
05900	circumscribed situation of a psychiatric interview.
06000		We did not try to construct a general-purpose algorithm which
06100	could  understand anything said in English by anybody to anybody else
06200	in any dialogue situation. (Does anyone believe it  to  be  currently
06300	possible?     The  seductive  myth  of  generalization  can  lead  to
06400	trivialization).   We sought simply to extract  some  degree  of,  or
06500	partial   idiosyncratic,   idiolectic  meaning  (not  the  "complete"
06600	meaning,  whatever  that  means)  from  the  input.   We  utilized  a
06700	pattern-directed, rather than a parsing-directed, approach because of
06800	the former's power to ignore irrelevant and unintelligible details.
06900		Natural language is not an agreed-upon universe of  discourse
07000	such as arithmetic, wherein symbols have a fixed meaning for everyone
07100	who uses them. What we loosely call "natural language" is actually  a
07200	set of history-dependent, selective, and interest-oriented idiolects,
07300	each being unique to the individual with a  unique  history.  (To  be
07400	unique   does  not  mean  that  no  property  is  shared  with  other
07500	individuals, only that not every property is shared). It is the broad
07600	overlap  of  idiolects  which  allows  the  communication  of  shared
07700	meanings in everyday conversation.
07800		We  took as pragmatic measures of "understanding" the ability
07900	(1) to form a conceptualization so that questions can be answered and
08000	commands   carried  out,  (2)  to  determine  the  intention  of  the
08100	interviewer, (3) to determine the references for pronouns  and  other
08200	anticipated  topics.   This  straightforward  approach  to  a complex
08300	problem has its drawbacks, as will be shown. We strove for  a  highly
08400	individualized  idiolect sufficient to demonstrate paranoid processes
08500	of an individual in a particular situation rather than for a  general
08600	supra-individual   or   ideal  comprehension  of  English.    If  the
08700	language-recognition  processes  of  PARRY  were  to  interfere  with
08800	demonstrating   the   paranoid  processes,  we  would  consider  them
08900	defective and insufficient for our purposes.
09000		The language-recognition process utilized by PARRY first puts
09100	the teletyped input in the form of a list  and  then  determines  the
09200	syntactic  type  of  the  input  expression  - question, statement or
09300	imperative by looking at introductory terms and at punctuation.   The
09400	expression-type is then scanned for conceptualizations, i.e. patterns
09500	of contentives consisting of words or  word-groups,  stress-forms  of
09600	speech  having  conceptual meaning relevant to the model's interests.
09700	The search for conceptualizations  ignores  (as  irrelevant  details)
09800	function  or closed-class terms (articles, auxiliaries, conjunctions,
09900	prepositions, etc.) except as they might represent a component  in  a
10000	contentive  word-group. For example, the word-group (for a living) is
10100	defined to mean `work' as in "Wat  do  you  do  for  a  living?"  The
10200	conceptualization  is  classified according to the rules of Fig. 1 as
10300	malevolent, benevolent or neutral.  Thus PARRY attempts to judge  the
10400	intention of the utterer from the content of the utterance.
10500		(INSERT FIG.1 HERE)
10600		Some  special  problems a dialogue algorithm must handle in a
10700	psychiatric interview  will  now  be  outlined  along  with  a  brief
10800	description of how the model deals with them.
10900	
11000	QUESTIONS
11100	
11200		The principal expression-type used by an interviewer consists
11300	of a question. A question is recognized by its  first  term  being  a
11400	"wh-" or "how" form and/or an expression ending with a question-mark.
11500	In  teletyped  interviews  a  question  may  sometimes  be   put   in
11600	declarative form followed by a question mark as in:
11700		(1) PT.- I LIKE TO GAMBLE ON THE HORSES.             	
11800		(2) DR.- YOU GAMBLE?
11900	Although  a  question-word  or  auxiliary verb is missing in (2), the
12000	model recognizes that a question is being asked about its gambling 
12100	simply by the question mark.
12200		Particularly  difficult  are  those  `when'  questions  which
12300	require a memory which can assign each event a beginning, an end  and
12400	a  duration.     An  improved  version  of the model should have this
12500	capacity.  Also troublesome are questions such as `how  often',  `how
12600	many', i.e.   a `how' followed by a quantifier. If the model has "how
12700	often" on its expectancy list while a topic is under discussion,  the
12800	appropriate   reply  can  be  made.  Otherwise  the  model  fails  to
12900	understand.
13000		In constructing a simulation  of  symbolic  processes  it  is
13100	arbitrary  how much information to represent in the data-base, Should
13200	PARRY know what is the capital of Alabama? It  is  trivial  to  store
13300	tomes of facts and there always will be boundary conditions.  We took
13400	the position that the model should know  only  what  we  believed  it
13500	reasonable  to  know relative to a few hundred topics expectable in a
13600	psychiatric interview. Thus PARRY performs poorly when  subjected  to
13700	baiting   `exam'   questions   designed  to  test  its  informational
13800	limitations rather than to seek useful psychiatric information.
13900	
14000	IMPERATIVES
14100	
14200		Typical imperatives in a  psychiatric  interview  consist  of
14300	expressions like:
14400		(3) DR.- TELL ME ABOUT YOURSELF.
14500		(4)  DR.-  LETS  DISCUSS  YOUR  FAMILY. 
14600		Such  imperatives  are   actually   interrogatives   to   the
14700	interviewee  about the topics they refer to.  Since the only physical
14800	action the model can perform is to `talk' , imperatives  are  treated
14900	as  requests  for  information.  They  are  identified  by the common
15000	introductory phrases: "tell me", "lets talk about", etc.
15100	DECLARATIVES
15200	
15300		In  this  category  is  lumped  everything  else. It includes
15400	greetings, farewells, yes-no type answers, existence  assertions  and
15500	the usual predications.
15600	
15700	AMBIGUITIES
15800	
15900		Words  have  more  than  one  sense,  a convenience for human
16000	memories  but  a  struggle  for  language-understanding   algorithms.
16100	Consider the word "bug" in the following expressions:
16200		(5) AM I BUGGING YOU?
16300		(6) AFTER A PERIOD OF HEAVY DRINKING HAVE YOU  FELT  BUGS  ON
16400	            YOUR SKIN?
16500		(7) DO YOU THINK THEY PUT A BUG IN YOUR ROOM? 
16600		In  expression  (5)  the term "bug" means to annoy, in (6) it
16700	refers to an insect and in (7) it refers to  a  microphone  used  for
16800	hidden   surveillence.       PARRY   uses   context   to   carry  out
16900	disambiguation.   For example, when the Mafia is under discussion and
17000	the  affect-variable  of  fear is high, the model interprets "bug" to
17100	mean microphone.     In constructing this hypothetical individual  we
17200	took advantage of the selective nature of idiolects which can have an
17300	arbitrary restriction on word senses.    One  characteristic  of  the
17400	paranoid  mode  is  that  regardless  of what sense of a word the the
17500	interviewer intends, the patient may idiosyncratically  interpret  it
17600	as  some  sense  of his own. This property is obviously of great help
17700	for an interactive  simulation  with  limited  language-understanding
17800	abilities.
17900	ANAPHORIC REFERENCES
18000		The common anaphoric references consist of the pronouns "it",
18100	"he", "him", "she", "her", "they", "them" as in:
18200		(8) PT.-HORSERACING IS MY HOBBY.
18300		(9) DR.-WHAT DO  YOU  ENJOY  ABOUT  IT?  
18400		When a topic is introduced by  the  patient  as  in  (8),  a
18500	number  of  things  can  be  expected  to be asked about it. Thus the
18600	algorithm has ready an updated expectancy-anaphora list which  allows
18700	it  to  determine  whether the topic introduced by the model is being
18800	responded to or  whether  the  interviewer  is  continuing  with  the
18900	previous topic.
19000		The  algorithm  recognizes  "it"  in  (9)  as  referring   to
19100	"horseracing" because a flag for horseracing was set when horseracing
19200	was introduced in (8), "it" was placed on the expected anaphora list,
19300	and no new topic has been introduced. A more difficult problem arises
19400	when the anaphoric reference points more than one I-O  pair  back  in
19500	the dialogue as in:
19600		(10) PT.-THE MAFIA IS OUT TO GET ME.
19700		(11) DR.- ARE YOU AFRAID OF THEM?
19800		(12) PT.- MAYBE.
19900		(13) DR.- WHY IS THAT? 
20000		The "that" of expression (13) does not refer to (12)  but  to
20100	the  topic  of being afraid which the interviewer introduced in (11).
20200		Another pronominal confusion occurs when the interviewer uses
20300	`we' in two senses as in:
20400		(14) DR.- WE WANT YOU TO STAY IN THE HOSPITAL.
20500		(15) PT.- I WANT TO BE DISCHARGED NOW.
20600		(16) DR.- WE ARE NOT COMMUNICATING. 
20700		In expression (14) the interviewer is using "we" to refer  to
20800	psychiatrists  or the hospital staff while in (16) the term refers to
20900	the interviewer and patient. Identifying the correct  referent  would
21000	require beliefs about the dialogue itself.
21100	
21200	TOPIC SHIFTS
21300	
21400		In  the  main, a psychiatric interviewer is in control of the
21500	interview. When he has gained sufficient information about  a  topic,
21600	he  shifts  to  a new topic. Naturally the algorithm must detect this
21700	change of topic as in the following:
21800		(17) DR.- HOW DO YOU LIKE THE HOSPITAL?
21900		(18) PT.- ITS NOT HELPING ME TO BE HERE.
22000		(19) DR.- WHAT BROUGHT YOU TO THE HOSPITAL?
22100		(20) PT.- I AM VERY UPSET AND NERVOUS.
22200		(21) DR.- WHAT TENDS TO MAKE YOU NERVOUS?
22300		(23) PT.- JUST BEING AROUND PEOPLE.
22400		(24) DR.- ANYONE IN PARTICULAR?
22500		In (17) and (19) the topic is the hospital. In (21) the topic
22600	changes to causes of the patient's nervous state.
22700		Topics touched upon previously can be  re-introduced  at  any
22800	point  in the interview.  PARRY knows that a topic has been discussed
22900	previously because a topic-flag is set when a topic comes up.
23000	
23100	META-REFERENCES
23200	
23300		These are references, not about a topic directly, but about
23400	what has been said about the topic as in:
23500		(25) DR.- WHY ARE YOU IN THE HOSPITAL?
23600		(26) PT.- I SHOULDNT BE HERE.
23700		(27) DR.-  WHY DO YOU SAY THAT?
23800	The expression (27 ) is about and meta to expression (26 ). The model
23900	does  not  respond  with  a  reason  why it said something but with a
24000	reason for the content of what it said, i.e. it  interprets  (27)  as
24100	"why shouldn't you be here?"
24200		Sometimes when the patient  makes  a  statement,  the  doctor
24300	replies,  not  with  a  question,  but  with  another statement which
24400	constitutes a rejoinder as in:
24500		(28 ) PT.- I HAVE LOST A LOT OF MONEY GAMBLING.
24600		(29 ) DR.- I GAMBLE QUITE A BIT ALSO.
24700		Here  the  algorithm  interprets  (29  )  as  a  directive to
24800	continue discussing gambling, not as an indication  to  question  the
24900	doctor  about  gambling.  
25000	
25100	ELLIPSES
25200	
25300	
25400		In dialogues one finds many ellipses, expressions from  which
25500	one or more words are omitted as in:
25600		(30 ) PT.- I SHOULDNT BE HERE.
25700		(31) DR.- WHY NOT?
25800		Here the complete construction must be understood as:
25900		(32) DR.- WHY SHOULD YOU NOT BE HERE?
26000	Again,  this   is  handled  by  the  expectancy-anaphora  list  which
26100	anticipates a "why not".
26200		The opposite of ellipsis is redundancy which usually provides
26300	no problem since the same thing is being said more than once as in:
26400		(33 ) DR.- LET ME ASK YOU A QUESTION.
26500	The model simply recognizes (33) as a stereotyped pattern.
26600	
26700	SIGNALS
26800	
26900		Some fragmentary expressions serve only as directive  signals
27000	to proceed, as in:
27100		(34) PT.- I WENT TO THE TRACK LAST WEEK.
27200		(35) DR.- AND?
27300	The  fragment of (35) requests a continuation of the story introduced
27400	in (34). The common expressions found in interviews are "and",  "so",
27500	"go  on", "go ahead", "really", etc. If an input expression cannot be
27600	recognized at all, the lowest level default condition is to assume it
27700	is  a  signal  and either proceed with the next line in a story under
27800	discussion or if a story has been exhausted, begin a new story with a
27900	prompting question or statement.
28000	
28100	IDIOMS
28200	
28300		Since so much of conversational language involves stereotypes
28400	and  special  cases, the task of recognition is much easier than that
28500	of linguistic analysis.  This is particularly true of idioms.  Either
28600	one knows what an idiom means or one does not. It is usually hopeless
28700	to try to decipher what an  idiom  means  from  an  analysis  of  its
28800	constituent  parts.   If  the  reader doubts this, let him ponder the
28900	following expressions taken from actual teletyped interviews.
29000		(36) DR.- WHATS EATING YOU?
29100		(37) DR.- YOU SOUND KIND OF PISSED OFF.
29200		(38) DR.- WHAT ARE YOU DRIVING AT?
29300		(39) DR.- ARE YOU PUTTING ME ON?
29400		(40) DR.- WHY ARE THEY AFTER YOU?
29500		(41) DR.- HOW DO YOU GET ALONG WITH THE OTHER PATIENTS?
29600	 	(42) DR.- HOW DO YOU LIKE YOUR WORK?
29700		(43) DR.- HAVE THEY TRIED TO GET EVEN WITH YOU?
29800		(44) DR.- I CANT KEEP UP WITH YOU.
29900		In  people,  the  understanding of idioms is a matter of rote
30000	memory.  In an algorithm, idioms can simply be stored as  such.    As
30100	each    new    idiom    appears    in   teletyped   interviews,   its
30200	recognition-pattern is  added  to  the  data-base  on  the  inductive
30300	grounds that what happens once can happen again.
30400		Another advantage in constructing an idiolect for a model  is
30500	that  it  recognizes  its  own idiomatic expressions which tend to be
30600	used by the interviewer (if he understands them) as in:
30700		(45) PT.- THEY ARE OUT TO GET ME.
30800		(46) DR.- WHAT MAKES YOU THINK THEY ARE OUT TO GET YOU.
30900		The expression (45 ) is really a double idiom in which  "out"
31000	means  `intend'  and  "get" means `harm' in this context. Needless to
31100	say.  an algorithm which tried to pair off the  various  meanings  of
31200	"out"  with  the  various meanings of "get" would have a hard time of
31300	it. But an algorithm which recognizes  what it itself is  capable  of
31400	saying, can easily recognize echoed idioms.
31500	
31600	FUZZ TERMS
31700	
31800		In this category fall a large number of expressions which, as
31900	non-contentives, have little or  no  meaning  and  therefore  can  be
32000	ignored by the algorithm. The lower-case expressions in the following
32100	are examples of fuzz:
32200		(47) DR.- well now perhaps YOU CAN TELL ME something ABOUT 
32300			YOUR FAMILY.
32400		(48) DR.- on the other hand I AM INTERESTED IN YOU.
32500		(49) DR.- hey I ASKED YOU A QUESTION.
32600		The  algorithm  has  "ignoring mechanisms" which allow for an
32700	`anything' slot in its  pattern  recognition.  Fuzz  terms  are  thus
32800	easily ignored and no attempt is made to analyze them.
32900	
33000	SUBORDINATE CLAUSES
33100	
33200		A subordinate clause is a complete statement  inside  another
33300	statement.  It  is  most frequently introduced by a relative pronoun,
33400	indicated in the following expressions by lower case:
33500		(50) DR.-  WAS IT THE UNDERWORLD that PUT YOU HERE?
33600		(51) DR.- WHO ARE THE PEOPLE who UPSET YOU?
33700		(52) DR.- HAS ANYTHING HAPPENED which YOU DONT UNDERSTAND?
33800		One  of  the  linguistic  weaknesses  of the model is that it
33900	takes the entire input as a single expression.   When  the  input  is
34000	syntactically  complex, containing subordinate clauses, the algorithm
34100	can become confused. To avoid this, future  versions  of  PARRY  will
34200	segment  the input into shorter and more manageable patterns in which
34300	an optimal selection of emphases and neglect of irrelevant detail can
34400	be achieved while avoiding combinatorial explosions.
34500	VOCABULARY
34600	
34700		How many words should there be in the algorithm's vocabulary?
34800	It  is  a  rare human speaker of English who can recognize 40% of the
34900	415,000 words in the Oxford  English  Dictionary.   In  his  everyday
35000	conversation  an  educated person uses perhaps 10,000 words and has a
35100	recognition vocabulary of about 50,000  words.  A study  of telephone
35200	conversations  showed  that 96 % of the talk employed only 737 words.
35300	(French, Carter, and Koenig, 1930). Of course if the remaining 4% are
35400	important  but  unrecognized contentives,the result may be ruinous to
35500	the coherence  of a conversation.
35600		In  counting  all  the  words  in  53  teletyped  psychiatric
35700	interviews conducted by psychiatrists, we found  only  721  different
35800	words.     Since  we  are  familiar with psychiatric vocabularies and
35900	styles of  expression,  we  believed  this  language-algorithm  could
36000	function  adequately  with  a  vocabulary  of  at most a few thousand
36100	contentives. There will always be unrecognized words.  The  algorithm
36200	must  be  able to continue even if it does not have a particular word
36300	in its vocabulary.    This provision represents one  great  advantage
36400	of   pattern-matching  over  conventional  linguistic  parsing.   Our
36500	algorithm can  guess  while  a  traditional  parser  must  know  with
36600	certainty in order to proceed.
36700	
36800	MISSPELLINGS AND EXTRA CHARACTERS
36900		There is really no good defense  against  misspellings  in  a
37000	teletyped  interview  except  having a human monitor the conversation
37100	and make the necessary corrections. Spelling correcting programs  are
37200	slow,  inefficient,  and  imperfect.   They experience great problems
37300	when it is the first character in a word which is incorrect.
37400		Extra characters sent over the teletype by the interviewer or
37500	by a bad phone line can be removed  by  a  human  monitor  since  the
37600	output  from  the  interviewer first appears on the monitor's console
37700	and then is typed by her directly to the program.
37800	
37900	META VERBS
38000	
38100		Certain common verbs such as "think", "feel", "believe", etc.
38200	can take a clause as their ojects as in:
38300		(54) DR.- I THINK YOU ARE RIGHT.
38400		(55) DR.- WHY DO YOU FEEL THE GAMBLING IS CROOKED?
38500		The  verb  "believe"  is  peculiar  since it can also take as
38600	object a noun or noun phrase as in:
38700		(56) DR.- I BELIEVE YOU.
38800		In expression (55) the conjunction "that" can follow the word
38900	"feel" signifying a subordinate clause. This is not  the  case  after
39000	"believe"   in   expression   (56).   PARRY    makes  the  correct
39100	identification in (56) because nothing follows the "you".
39200	ODD WORDS
39300		From  extensive  experience  with  teletyped  interviews,  we
39400	learned the model must have patterns for "odd" words.  We  term  them
39500	such  since  these  are  words  which  are quite natural in the usual
39600	vis-a-vis interview in which  the  participants  communicate  through
39700	speech, but  which  are  quite  odd  in  the  context  of a teletyped
39800	interview. This should be clear from the following examples in  which
39900	the odd words appear in lower case:
40000		(57) DR.-YOU sound CONFUSED.
40100		(58) DR.- DID YOU hear MY LAST QUESTION?
40200		(59) DR.- WOULD YOU come in AND sit down PLEASE?
40300		(60) DR.- CAN YOU say WHO?
40400		(61) DR.- I WILL see YOU AGAIN TOMORROW.
40500	
40600	
40700	MISUNDERSTANDING
40800	
40900		It is perhaps not fully recognized by  students  of  language
41000	how  often  people  misunderstand one another in conversation and yet
41100	their dialogues proceed as if understanding and being understood  had
41200	taken place.
41300		A classic example is the following man-on-the-street interview.
41400		INTERVIEWER -  WHAT DO YOU THINK OF MARIHUANA?
41500	 	MAN - DIRTIEST TOWN IN MEXICO.
41600		INTERVIEWER - HOW ABOUT LSD?
41700		MAN - I VOTED FOR HIM.
41800		INTERVIEWER - HOW DO YOU FEEL ABOUT THE INDIANAPOLIS 500?
41900		MAN - I THINK THEY SHOULD SHOOT EVERY LAST ONE OF THEM.
42000		INTERVIEWER - AND THE VIET CONG POSITION?
42100		MAN - I'M FOR IT, BUT MY WIFE COMPLAINS ABOUT HER ELBOWS.
42200		Sometimes    a    psychiatric   interviewer   realizes   when
42300	misunderstanding occurs and tries  to  correct  it.  Other  times  he
42400	simply  passes  it  by.  It is characteristic of the paranoid mode to
42500	respond idiosyncratically to particular word-concepts  regardless  of
42600	what the interviewer is saying:
42700		(62) PT.- SOME PEOPLE HERE MAKE ME NERVOUS. 	
42800	        (63) DR.- I BET.
42900	        (64) PT.- GAMBLING HAS BEEN NOTHING BUT TROUBLE FOR ME.
43000	Here one word sense of "bet" (to wager) is confused with the  offered
43100	sense   of   expressing   agreement.  As  has  been  mentioned,  this
43200	sense-confusion property of paranoid conversation eases the  task  of
43300	simulation.
43400	UNUNDERSTANDING
43500	
43600		A dialogue algorithm must be prepared for situations in which
43700	it simply does not understand. It cannot arrive at any interpretation
43800	as to what the interviewer is saying since no pattern can be matched.
43900	It may recognize the topic but not what is being said about it.
44000		The language-recognizer should not be faulted  for  a  simple
44100	lack of irrelevant information as in:
44200		(65) DR.- WHAT IS THE FIFTIETH STATE?
44300	when the data-base does not contain  the  answer.   In  this  default
44400	condition it is simplest to reply:
44500		(66) PT.- I DONT KNOW.
44600	When information is absent it is dangerous to reply:
44700		(67) PT.- COULD YOU REPHRASE THE QUESTION?
44800	because of the disastrous loops which can result.
44900		Since  the  main  problem  in  the   default   condition   of
45000	ununderstanding is how to continue, PARRY   employs heuristics such
45100	as  changing  the  level  of  the  dialogue  and  asking  about   the
45200	interviewer's intention as in:
45300		(68) PT.- WHY DO YOU WANT TO KNOW THAT?
45400	or  rigidly  continuing  with  a  previous topic or introducing a new
45500	topic.
45600		These are admittedly desperate measures  intended  to  prompt
45700	the  interviewer  in  directions the algorithm has a better chance of
45800	understanding. Although it is usually the  interviewer  who  controls
45900	the  flow  from  topic to topic, there are times when control must be
46000	assumed by the model.
46100		There  are  many   additional   problems   in   understanding
46200	conversational language but the description of this chapter should be
46300	sufficient to convey some  of  the  complexities  involved.   Further
46400	examples  will  be  presented  in  the next chapter in describing the
46500	logic of the central processes of the model.