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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 .F
11100 QUESTIONS
11200
11300 The principal expression-type used by an interviewer consists
11400 of a question. A question is recognized by its first term being a
11500 "wh-" or "how" form and/or an expression ending with a question-mark.
11600 In teletyped interviews a question may sometimes be put in
11700 declarative form followed by a question mark as in:
11800 .V
11900 (1) PT.- I LIKE TO GAMBLE ON THE HORSES.
12000 (2) DR.- YOU GAMBLE?
12100 .END
12200 Although a question-word or auxiliary verb is missing in (2), the
12300 model recognizes that a question is being asked about its gambling
12400 simply by the question mark.
12500 Particularly difficult are those `when' questions which
12600 require a memory which can assign each event a beginning, an end and
12700 a duration. An improved version of the model should have this
12800 capacity. Also troublesome are questions such as `how often', `how
12900 many', i.e. a `how' followed by a quantifier. If the model has "how
13000 often" on its expectancy list while a topic is under discussion, the
13100 appropriate reply can be made. Otherwise the model fails to
13200 understand.
13300 In constructing a simulation of symbolic processes it is
13400 arbitrary how much information to represent in the data-base, Should
13500 PARRY know what is the capital of Alabama? It is trivial to store
13600 tomes of facts and there always will be boundary conditions. We took
13700 the position that the model should know only what we believed it
13800 reasonable to know relative to a few hundred topics expectable in a
13900 psychiatric interview. Thus PARRY performs poorly when subjected to
14000 baiting `exam' questions designed to test its informational
14100 limitations rather than to seek useful psychiatric information.
14200
14300 .F
14400 IMPERATIVES
14500
14600 Typical imperatives in a psychiatric interview consist of
14700 expressions like:
14800 .V
14900 (3) DR.- TELL ME ABOUT YOURSELF.
15000 (4) DR.- LETS DISCUSS YOUR FAMILY.
15100 .END
15200 Such imperatives are actually interrogatives to the
15300 interviewee about the topics they refer to. Since the only physical
15400 action the model can perform is to `talk' , imperatives are treated
15500 as requests for information. They are identified by the common
15600 introductory phrases: "tell me", "lets talk about", etc.
15700 .F
15800 DECLARATIVES
15900
16000 In this category is lumped everything else. It includes
16100 greetings, farewells, yes-no type answers, existence assertions and
16200 the usual predications.
16300
16400 .F
16500 AMBIGUITIES
16600
16700 Words have more than one sense, a convenience for human
16800 memories but a struggle for language-understanding algorithms.
16900 Consider the word "bug" in the following expressions:
17000 .V
17100 (5) AM I BUGGING YOU?
17200 (6) AFTER A PERIOD OF HEAVY DRINKING HAVE YOU FELT BUGS ON
17300 YOUR SKIN?
17400 (7) DO YOU THINK THEY PUT A BUG IN YOUR ROOM?
17500 .END
17600 In expression (5) the term "bug" means to annoy, in (6) it
17700 refers to an insect and in (7) it refers to a microphone used for
17800 hidden surveillence. PARRY uses context to carry out
17900 disambiguation. For example, when the Mafia is under discussion and
18000 the affect-variable of fear is high, the model interprets "bug" to
18100 mean microphone. In constructing this hypothetical individual we
18200 took advantage of the selective nature of idiolects which can have an
18300 arbitrary restriction on word senses. One characteristic of the
18400 paranoid mode is that regardless of what sense of a word the the
18500 interviewer intends, the patient may idiosyncratically interpret it
18600 as some sense of his own. This property is obviously of great help
18700 for an interactive simulation with limited language-understanding
18800 abilities.
18900 .F
19000 ANAPHORIC REFERENCES
19100 The common anaphoric references consist of the pronouns "it",
19200 "he", "him", "she", "her", "they", "them" as in:
19300 .V
19400 (8) PT.-HORSERACING IS MY HOBBY.
19500 (9) DR.-WHAT DO YOU ENJOY ABOUT IT?
19600 .END
19700 When a topic is introduced by the patient as in (8), a
19800 number of things can be expected to be asked about it. Thus the
19900 algorithm has ready an updated expectancy-anaphora list which allows
20000 it to determine whether the topic introduced by the model is being
20100 responded to or whether the interviewer is continuing with the
20200 previous topic.
20300 The algorithm recognizes "it" in (9) as referring to
20400 "horseracing" because a flag for horseracing was set when horseracing
20500 was introduced in (8), "it" was placed on the expected anaphora list,
20600 and no new topic has been introduced. A more difficult problem arises
20700 when the anaphoric reference points more than one I-O pair back in
20800 the dialogue as in:
20900 .V
21000 (10) PT.-THE MAFIA IS OUT TO GET ME.
21100 (11) DR.- ARE YOU AFRAID OF THEM?
21200 (12) PT.- MAYBE.
21300 (13) DR.- WHY IS THAT?
21400 .END
21500 The "that" of expression (13) does not refer to (12) but to
21600 the topic of being afraid which the interviewer introduced in (11).
21700 Another pronominal confusion occurs when the interviewer uses
21800 `we' in two senses as in:
21900 .V
22000 (14) DR.- WE WANT YOU TO STAY IN THE HOSPITAL.
22100 (15) PT.- I WANT TO BE DISCHARGED NOW.
22200 (16) DR.- WE ARE NOT COMMUNICATING.
22300 .END
22400 In expression (14) the interviewer is using "we" to refer to
22500 psychiatrists or the hospital staff while in (16) the term refers to
22600 the interviewer and patient. Identifying the correct referent would
22700 require beliefs about the dialogue itself.
22800
22900 .F
23000 TOPIC SHIFTS
23100
23200 In the main, a psychiatric interviewer is in control of the
23300 interview. When he has gained sufficient information about a topic,
23400 he shifts to a new topic. Naturally the algorithm must detect this
23500 change of topic as in the following:
23600 .V
23700 (17) DR.- HOW DO YOU LIKE THE HOSPITAL?
23800 (18) PT.- ITS NOT HELPING ME TO BE HERE.
23900 (19) DR.- WHAT BROUGHT YOU TO THE HOSPITAL?
24000 (20) PT.- I AM VERY UPSET AND NERVOUS.
24100 (21) DR.- WHAT TENDS TO MAKE YOU NERVOUS?
24200 (23) PT.- JUST BEING AROUND PEOPLE.
24300 (24) DR.- ANYONE IN PARTICULAR?
24400 .END
24500 In (17) and (19) the topic is the hospital. In (21) the topic
24600 changes to causes of the patient's nervous state.
24700 Topics touched upon previously can be re-introduced at any
24800 point in the interview. PARRY knows that a topic has been discussed
24900 previously because a topic-flag is set when a topic comes up.
25000
25100 .F
25200 META-REFERENCES
25300
25400 These are references, not about a topic directly, but about
25500 what has been said about the topic as in:
25600 .V
25700 (25) DR.- WHY ARE YOU IN THE HOSPITAL?
25800 (26) PT.- I SHOULDNT BE HERE.
25900 (27) DR.- WHY DO YOU SAY THAT?
26000 .END
26100 The expression (27 ) is about and meta to expression (26 ). The model
26200 does not respond with a reason why it said something but with a
26300 reason for the content of what it said, i.e. it interprets (27) as
26400 "why shouldn't you be here?"
26500 Sometimes when the patient makes a statement, the doctor
26600 replies, not with a question, but with another statement which
26700 constitutes a rejoinder as in:
26800 .V
26900 (28 ) PT.- I HAVE LOST A LOT OF MONEY GAMBLING.
27000 (29 ) DR.- I GAMBLE QUITE A BIT ALSO.
27100 .END
27200 Here the algorithm interprets (29 ) as a directive to
27300 continue discussing gambling, not as an indication to question the
27400 doctor about gambling.
27500
27600 .F
27700 ELLIPSES
27800
27900
28000 In dialogues one finds many ellipses, expressions from which
28100 one or more words are omitted as in:
28200 .V
28300 (30 ) PT.- I SHOULDNT BE HERE.
28400 (31) DR.- WHY NOT?
28500 .END
28600 Here the complete construction must be understood as:
28700 .V
28800 (32) DR.- WHY SHOULD YOU NOT BE HERE?
28900 .END
29000 Again, this is handled by the expectancy-anaphora list which
29100 anticipates a "why not".
29200 The opposite of ellipsis is redundancy which usually provides
29300 no problem since the same thing is being said more than once as in:
29400 .V
29500 (33 ) DR.- LET ME ASK YOU A QUESTION.
29600 .END
29700 The model simply recognizes (33) as a stereotyped pattern.
29800
29900 .F
30000 SIGNALS
30100
30200 Some fragmentary expressions serve only as directive signals
30300 to proceed, as in:
30400 .V
30500 (34) PT.- I WENT TO THE TRACK LAST WEEK.
30600 (35) DR.- AND?
30700 .END
30800 The fragment of (35) requests a continuation of the story introduced
30900 in (34). The common expressions found in interviews are "and", "so",
31000 "go on", "go ahead", "really", etc. If an input expression cannot be
31100 recognized at all, the lowest level default condition is to assume it
31200 is a signal and either proceed with the next line in a story under
31300 discussion or if a story has been exhausted, begin a new story with a
31400 prompting question or statement.
31500
31600 .F
31700 IDIOMS
31800
31900 Since so much of conversational language involves stereotypes
32000 and special cases, the task of recognition is much easier than that
32100 of linguistic analysis. This is particularly true of idioms. Either
32200 one knows what an idiom means or one does not. It is usually hopeless
32300 to try to decipher what an idiom means from an analysis of its
32400 constituent parts. If the reader doubts this, let him ponder the
32500 following expressions taken from actual teletyped interviews.
32700 .V
32800 (36) DR.- WHATS EATING YOU?
32900 (37) DR.- YOU SOUND KIND OF PISSED OFF.
33000 (38) DR.- WHAT ARE YOU DRIVING AT?
33100 (39) DR.- ARE YOU PUTTING ME ON?
33200 (40) DR.- WHY ARE THEY AFTER YOU?
33300 (41) DR.- HOW DO YOU GET ALONG WITH THE OTHER PATIENTS?
33400 (42) DR.- HOW DO YOU LIKE YOUR WORK?
33500 (43) DR.- HAVE THEY TRIED TO GET EVEN WITH YOU?
33600 (44) DR.- I CANT KEEP UP WITH YOU.
33700 .END
33800 In people, the understanding of idioms is a matter of rote
33900 memory. In an algorithm, idioms can simply be stored as such. As
34000 each new idiom appears in teletyped interviews, its
34100 recognition-pattern is added to the data-base on the inductive
34200 grounds that what happens once can happen again.
34300 Another advantage in constructing an idiolect for a model is
34400 that it recognizes its own idiomatic expressions which tend to be
34500 used by the interviewer (if he understands them) as in:
34600 .V
34700 (45) PT.- THEY ARE OUT TO GET ME.
34800 (46) DR.- WHAT MAKES YOU THINK THEY ARE OUT TO GET YOU.
34900 .END
35000 The expression (45 ) is really a double idiom in which "out"
35100 means `intend' and "get" means `harm' in this context. Needless to
35200 say. an algorithm which tried to pair off the various meanings of
35300 "out" with the various meanings of "get" would have a hard time of
35400 it. But an algorithm which recognizes what it itself is capable of
35500 saying, can easily recognize echoed idioms.
35600
35700 .F
35800 FUZZ TERMS
35900
36000 In this category fall a large number of expressions which, as
36100 non-contentives, have little or no meaning and therefore can be
36200 ignored by the algorithm. The lower-case expressions in the following
36300 are examples of fuzz:
36400 .V
36500 (47) DR.- well now perhaps YOU CAN TELL ME something ABOUT
36600 YOUR FAMILY.
36700 (48) DR.- on the other hand I AM INTERESTED IN YOU.
36800 (49) DR.- hey I ASKED YOU A QUESTION.
36900 .END
37000 The algorithm has "ignoring mechanisms" which allow for an
37100 `anything' slot in its pattern recognition. Fuzz terms are thus
37200 easily ignored and no attempt is made to analyze them.
37300
37400 .F
37500 SUBORDINATE CLAUSES
37600
37700 A subordinate clause is a complete statement inside another
37800 statement. It is most frequently introduced by a relative pronoun,
37900 indicated in the following expressions by lower case:
38000 .V
38100 (50) DR.- WAS IT THE UNDERWORLD that PUT YOU HERE?
38200 (51) DR.- WHO ARE THE PEOPLE who UPSET YOU?
38300 (52) DR.- HAS ANYTHING HAPPENED which YOU DONT UNDERSTAND?
38400 .END
38500 One of the linguistic weaknesses of the model is that it
38600 takes the entire input as a single expression. When the input is
38700 syntactically complex, containing subordinate clauses, the algorithm
38800 can become confused. To avoid this, future versions of PARRY will
38900 segment the input into shorter and more manageable patterns in which
39000 an optimal selection of emphases and neglect of irrelevant detail can
39100 be achieved while avoiding combinatorial explosions.
39200 .F
39300 VOCABULARY
39400
39500 How many words should there be in the algorithm's vocabulary?
39600 It is a rare human speaker of English who can recognize 40% of the
39700 415,000 words in the Oxford English Dictionary. In his everyday
39800 conversation an educated person uses perhaps 10,000 words and has a
39900 recognition vocabulary of about 50,000 words. A study of telephone
40000 conversations showed that 96 % of the talk employed only 737 words.
40100 (French, Carter, and Koenig, 1930). Of course if the remaining 4% are
40200 important but unrecognized contentives,the result may be ruinous to
40300 the coherence of a conversation.
40400 In counting all the words in 53 teletyped psychiatric
40500 interviews conducted by psychiatrists, we found only 721 different
40600 words. Since we are familiar with psychiatric vocabularies and
40700 styles of expression, we believed this language-algorithm could
40800 function adequately with a vocabulary of at most a few thousand
40900 contentives. There will always be unrecognized words. The algorithm
41000 must be able to continue even if it does not have a particular word
41100 in its vocabulary. This provision represents one great advantage
41200 of pattern-matching over conventional linguistic parsing. Our
41300 algorithm can guess while a traditional parser must know with
41400 certainty in order to proceed.
41500
41600 .F
41700 MISSPELLINGS AND EXTRA CHARACTERS
41800 There is really no good defense against misspellings in a
41900 teletyped interview except having a human monitor the conversation
42000 and make the necessary corrections. Spelling correcting programs are
42100 slow, inefficient, and imperfect. They experience great problems
42200 when it is the first character in a word which is incorrect.
42300 Extra characters sent over the teletype by the interviewer or
42400 by a bad phone line can be removed by a human monitor since the
42500 output from the interviewer first appears on the monitor's console
42600 and then is typed by her directly to the program.
42700
42800 .F
42900 META VERBS
43000
43100 Certain common verbs such as "think", "feel", "believe", etc.
43200 can take a clause as their ojects as in:
43300 .V
43400 (54) DR.- I THINK YOU ARE RIGHT.
43500 (55) DR.- WHY DO YOU FEEL THE GAMBLING IS CROOKED?
43600 .END
43700 The verb "believe" is peculiar since it can also take as
43800 object a noun or noun phrase as in:
43900 .V
44000 (56) DR.- I BELIEVE YOU.
44100 .END
44200 In expression (55) the conjunction "that" can follow the word
44300 "feel" signifying a subordinate clause. This is not the case after
44400 "believe" in expression (56). PARRY makes the correct
44500 identification in (56) because nothing follows the "you".
44600 .F
44700 ODD WORDS
44800 From extensive experience with teletyped interviews, we
44900 learned the model must have patterns for "odd" words. We term them
45000 such since these are words which are quite natural in the usual
45100 vis-a-vis interview in which the participants communicate through
45200 speech, but which are quite odd in the context of a teletyped
45300 interview. This should be clear from the following examples in which
45400 the odd words appear in lower case:
45500 .V
45600 (57) DR.-YOU sound CONFUSED.
45700 (58) DR.- DID YOU hear MY LAST QUESTION?
45800 (59) DR.- WOULD YOU come in AND sit down PLEASE?
45900 (60) DR.- CAN YOU say WHO?
46000 (61) DR.- I WILL see YOU AGAIN TOMORROW.
46100 .END
46200
46300
46400 .F
46500 MISUNDERSTANDING
46600
46700 It is perhaps not fully recognized by students of language
46800 how often people misunderstand one another in conversation and yet
46900 their dialogues proceed as if understanding and being understood had
47000 taken place.
47100 A classic example is the following man-on-the-street interview.
47200 .V
47300 INTERVIEWER - WHAT DO YOU THINK OF MARIHUANA?
47400 MAN - DIRTIEST TOWN IN MEXICO.
47500 INTERVIEWER - HOW ABOUT LSD?
47600 MAN - I VOTED FOR HIM.
47700 INTERVIEWER - HOW DO YOU FEEL ABOUT THE INDIANAPOLIS 500?
47800 MAN - I THINK THEY SHOULD SHOOT EVERY LAST ONE OF THEM.
47900 INTERVIEWER - AND THE VIET CONG POSITION?
48000 MAN - I'M FOR IT, BUT MY WIFE COMPLAINS ABOUT HER ELBOWS.
48100 .END
48200 Sometimes a psychiatric interviewer realizes when
48300 misunderstanding occurs and tries to correct it. Other times he
48400 simply passes it by. It is characteristic of the paranoid mode to
48500 respond idiosyncratically to particular word-concepts regardless of
48600 what the interviewer is saying:
48700 .V
48800 (62) PT.- SOME PEOPLE HERE MAKE ME NERVOUS.
48900 (63) DR.- I BET.
49000 (64) PT.- GAMBLING HAS BEEN NOTHING BUT TROUBLE FOR ME.
49100 .END
49200 Here one word sense of "bet" (to wager) is confused with the offered
49300 sense of expressing agreement. As has been mentioned, this
49400 sense-confusion property of paranoid conversation eases the task of
49500 simulation.
49600 .F
49700 UNUNDERSTANDING
49800
49900 A dialogue algorithm must be prepared for situations in which
50000 it simply does not understand. It cannot arrive at any interpretation
50100 as to what the interviewer is saying since no pattern can be matched.
50200 It may recognize the topic but not what is being said about it.
50300 The language-recognizer should not be faulted for a simple
50400 lack of information as in:
50500 .V
50600 (65) DR.- WHICH OF YOUR PARENTS DO YOU RESEMBLE MOST?
50700 .END CONTINUE
50800 when the data-base does not contain the word "resemble". In this
50900 default condition it is simplest to reply:
51000 .V
51100 (66) PT.- I DONT KNOW.
51200 .END CONTINUE
51300 and dangerous to reply:
51400 .V
51500 (67) PT.- COULD YOU REPHRASE THE QUESTION?
51600 .END CONTINUE
51700 because of the disastrous loops which can result.
51800 Since the main problem in the default condition of
51900 ununderstanding is how to continue, PARRY employs heuristics such
52000 as changing the level of the dialogue and asking about the
52100 interviewer's intention as in:
52200 .V
52300 (68) PT.- WHY DO YOU WANT TO KNOW THAT?
52400 .END CONTINUE
52500 or rigidly continuing with a previous topic or introducing a new
52600 topic.
52700 These are admittedly desperate measures intended to prompt
52800 the interviewer in directions the algorithm has a better chance of
52900 understanding. Although it is usually the interviewer who controls
53000 the flow from topic to topic, there are times when control must be
53100 assumed by the model.
53200 There are many additional problems in understanding
53300 conversational language but the description of this chapter should be
53400 sufficient to convey some of the complexities involved. Further
53500 examples will be presented in the next chapter in describing the
53600 logic of the central processes of the model.