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00100 .SEC PROBLEMS FOR COMPUTER UNDERSTANDING OF NATURAL LANGUAGE
00200 COMMUNICATION 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 must have an ability to interpret
00700 and respond to natural language input sufficient to demonstrate
00800 conduct characteristic of the paranoid mode. By "natural language"
00900 I shall mean ordinary American English such as is used in everyday
01000 conversations. It is still difficult to be explicit about the
01100 processes which enable humans to interpret and respond to natural
01200 language. (A mighty maze ! but not without a plan - A. Pope).
01300 Philosophers, linguists and psychologists have investigated natural
01400 language with various purposes and yielding few results useful to
01500 model builders. Attempts currently are being made in artificial
01600 intelligence to write algorithims which "understand" natural language
01700 expressions. [Enea and Colby,1973; Winograd,1973]. Machine
01800 understanding of natural language is being attempted today but it is
01900 not something to be completly achieved today or even tomorrow. The
02000 problem at the moment is not to find immediately the best way of
02100 doing it but to find any way at all.
02200 During the 1960's when machine processing of natural language
02300 was dominated by syntactic considerations, it became clear that
02400 syntactical information alone was insufficient to comprehend the
02500 expressions of ordinary conversations. A current view is that to
02600 understand what is said in linguistic expressions, knowledge of
02700 syntax and semantics must be combined with beliefs from a conceptual
02800 structure capable of making inferences. How to achieve this
02900 combination efficiently with a large data-base represents a
03000 monumental task for both theory and implementation.
03100 We did not attempt to construct a conventional linguistic
03200 parser to analyze interview natural language input for practical
03300 reasons. Parsers to date have great difficulty in assigning a
03400 meaningful interpretation to the expressions of everyday
03500 conversational language using unrestricted English. Purely syntactic
03600 parsers offer a cancerous proliferation of interpretations. A
03700 conventional parser lacking ignoring mechanisms,may simply halt when
03800 it comes across a word not in its dictionary. Parsers represent tight
03900 conjunctions of tests instead of loose disjunctions needed for
04000 gleaning a meaninng from everyday language communication which may
04100 involve misunderstandinga and ununderstandings. People misunderstand
04200 and ununderstand at times and thus are partially opaque to one
04300 another.
04400 The language recognition process utilized by the model first
04500 puts the input in the form of a list and then determines the
04600 syntactical type for the input expression- question, statement or
04700 imperative. The expression-type is scanned in an attempt to form a
04800 conceptualization, i.e. a predication of an attribute, on an object
04900 or a relation between objects. An attribute consists of something one
05000 is or does or possesses. The resultant conceptualization is then
05100 classified according to the rules of Fig. 00 in %000 as malevolent,
05200 benevolent or neutral.
05300 How language is understood depends on the intentions of the
05400 producers and interpreters in the dialogue. Thus language is
05500 understood in accordance with the participant's view of the
05600 situation. Our purpose was to develop a method for understanding
05700 sequences of everyday English sufficient for the model to communicate
05800 linguistically in a paranoid way in the circumscribed situation of a
05900 psychiatric interview. Such an interview is not small talk; a job is
06000 to be done.
06100 We did not try to construct a general-purpose algorithm which
06200 could understand anything said in English by anybody to anybody in
06300 any dialogue situation. (Does anyone believe it possible?) We sought
06400 only to extract, distill or cull an idiolectic meaning or gist of a
06500 meaning from the input.
06600 Natural language is not an agree-on universe of discourse
06700 such as arithmetic wherein symbols have the same meaning for everyone
06800 who uses them. What we loosely call "natural language" is actually a set
06900 of idiolects, each being unique to the individual with a unique history.
07000 To be unique does not mean that no property is shared with other
07100 individuals, only that not every property is shared. An overlap of
07200 idiolects allows the communication of shared meanings.
07300 We took as pragmatic measures of "understanding" the
07400 ability (1) to form a conceptualization so that questions can be
07500 answered and commands carried out, (2) to determine the intention of
07600 the interviewer, (3) to determine the references for pronouns and
07700 other anticipated topics. This straightforward approach to a complex
07800 problem has its drawbacks, as will be shown, but we strove for a
07900 highly individualized idiolect sufficient to demonstrate paranoid
08000 processes of an individual in a particular situation rather than for
08100 a general supra-individual or ideal comprehension of English. If the
08200 language-understanding system interfered with demonstrating the
08300 paranoid processes, we would consider it defective and insufficient
08400 for our purposes.
08500 Some special problems a dialogue algorithm must handle in a
08600 psychiatric interview will now be outlined along with a brief
08700 description of how the model deals with them.
08800
08900 .F
09000 QUESTIONS
09100
09200 The principal expression-type used by an interviewer consists
09300 of a question. A question is recognized by its beginning with a wh-
09400 or how form and/or the expression ending with a question-mark. In
09500 teletyped interviews a question may sometimes be put in declarative
09600 form followed by a question mark as in:
09700 .V
09800 (1) PT.- I LIKE TO GAMBLE ON THE HORSES.
09900 (2) DR.- YOU GAMBLE?
10000 .END
10100 Although the verb is missing in (2), the model recognizes that a
10200 question is being asked about its gambling.
10300
10400 Particularly difficult are `when' questions which require a memory
10500 which can assign each event a beginning, end and a duration. An
10600 improved version of the model will have this capacity. Also
10700 troublesome are questions such as `how often', `how many', i.e. a
10800 `how' followed by a quantifier. If the model has "how often" on its
10900 expectancy list while a topic is under discussion, the appropriate
11000 reply can be made. Otherwise the model ununderstands.
11100 In constructing a simulation of a thought process it is
11200 arbitrary how much information to represent in memory. Should the
11300 model know what is the capital of Alabama? It is trivial to store a
11400 lot of facts. We took the position that the model should know only
11500 what we believed it reasonable to know relevant to a few hundred
11600 topics expectable in a psychiatric interview. Thus the model performs
11700 badly when subjected to baiting `exam' questions designed to test its
11800 informational limitations rather than to seek useful psychiatric
11900 information.
12000
12100 .F
12200 IMPERATIVES
12300
12400 Typical imperatives in a psychiatric interview consist of
12500 expressions like:
12600 .V
12700 (3) DR.- TELL ME ABOUT YOURSELF.
12800 (4) DR.- LETS DISCUSS YOUR FAMILY.
12900 .END
13000 Such imperatives are actually interrogatives to the
13100 interviewee about the topics they refer to. Since the only physical
13200 action the model can perform is to `talk' , imperatives are treated
13300 as requests for information.
13400
13500 .F
13600 DECLARATIVES
13700
13800 In this category is lumped everything else. It includes
13900 greetings, farewells, yes-no type answers, existence assertions and
14000 the usual predications.
14100
14200 .F
14300 AMBIGUITIES
14400
14500 Words have more than one sense, a convenience for human
14600 memories but a struggle for language-analysing algorithms. Consider
14700 the word "bug" in the following expressions:
14800 .V
14900 (5) AM I BUGGING YOU?
15000 (6) AFTER A PERIOD OF HEAVY DRINKING HAVE YOU FELT BUGS ON
15100 YOUR SKIN?
15200 (7) DO YOU THINK THEY PUT A BUG IN YOUR ROOM?
15300 .END
15400 In expression (5) the term "bug" means to annoy, in (6) it
15500 refers to an insect and in (7) it refers to a microphone used for
15600 hidden survellience. Some words like "run" have fifty or more common
15700 senses. Context is used to carry out disambiguation. For example,
15800 when the Mafia is under discussion the model interprets "bug" to mean
15900 microphone. Thus we have the advantage of an idiolect where we can
16000 arbitrarily restrict the word senses. One characteristic of the
16100 paranoid mode is that no matter in what sense the interviewer uses a
16200 word, the patient may idiosyncratically interpret it in some sense
16300
16400 .F
16500 ANAPHORIC REFERENCES
16600 The common anaphoric references consist of the pronouns "it",
16700 "he", "him", "she", "her", "they", "them" as in:
16800 .V
16900 (8) PT.-HORSERACING IS MY HOBBY.
17000 (9) DR.-WHAT DO YOU ENJOY ABOUT IT?
17100 .END
17200 The algorithm recognizes "it" as referring to "horseracing"
17300 because "it" has been placed on the expectancy list when horseracing
17400 was introduced in (8). A more difficult problem arises when the
17500 anaphoric reference points more than one I/O pair back in the
17600 dialogue as in:
17700 .V
17800 (10) PT.-THE MAFIA IS OUT TO GET ME.
17900 (11) DR.- ARE YOU AFRAID OF THEM?
18000 (12) PT.- MAYBE.
18100 (13) DR.- WHY IS THAT?
18200 .END
18300 The "that" of expression (13) does not refer to (12) but to
18400 the topic of being afraid which the interviewer introduced in (11).
18500 Another pronominal confusion occurs when the interviewer uses `we' in
18600 two senses as in:
18700 .V
18800 (14) DR.- WE WANT YOU TO STAY IN THE HOSPITAL.
18900 (15) PT.- I WANT TO BE DISCHARGED NOW.
19000 (16) DR.- WE ARE NOT COMMUNICATING.
19100 .END
19200 In expression (14) the interviewer is using "we" to refer to
19300 psychiatrists or the hospital staff while in (16) the term refers to
19400 the interviewer and patient. Identifying the correct referent would
19500 require beliefs about the dialogue which the new version of the model
19600 will have.
19700
19800 .F
19900 TOPIC SHIFTS
20000
20100 In the main a psychiatric interviewer is in control of the
20200 interview. When he has gained sufficient information about a topic,
20300 he shifts to a new topic. Naturally the algorithm must detect this
20400 change of topic as in the following:
20500 .V
20600 (17) DR.- HOW DO YOU LIKE THE HOSPITAL?
20700 (18) PT.- ITS NOT HELPING ME TO BE HERE.
20800 (19) DR.- WHAT BROUGHT YOU TO THE HOSPITAL?
20900 (20) PT.- I AM VERY UPSET AND NERVOUS.
21000 (21) DR.- WHAT TENDS TO MAKE YOU NERVOUS?
21100 (23) PT.- JUST BEING AROUND PEOPLE.
21200 (24) DR.- ANYONE IN PARTICULAR?
21300 .END
21400 In (17) and (19) the topic is the hospital. In (21) the topic
21500 changes to causes of the patient's nervous state.
21600 When a topic is introduced by the patient as in (20), a
21700 number of things can be expected to be asked about it. Thus the
21800 algorithm has ready an updated expectancy-anaphora list which allows
21900 it to determine whether the topic introduced by the model is being
22000 responded to or whether the interviewer is continuing with the
22100 previous topic.
22200 Topics touched upon previously can be re-introduced at any
22300 point in the interview. The model knows that a topic has been
22400 discussed previously because a topic-flag is set when a topic comes
22500 up.
22600
22700 .F
22800 META-REFERENCES
22900
23000 These are references, not about a topic directly, but about
23100 what has been said about the topic as in:
23200 .V
23300 (25) DR.- WHY ARE YOU IN THE HOSPITAL?
23400 (26) PT.- I SHOULDNT BE HERE.
23500 (27) DR.- WHY DO YOU SAY THAT?
23600 .END
23700 The expression (27 ) is about and meta to expression (26 ).
23800 Sometimes when the patient makes a statement, the doctor
23900 replies, not with a question, but with another statement which
24000 constitutes a rejoinder as in:
24100 .V
24200 (28 ) PT.- I HAVE LOST A LOT OF MONEY GAMBLING.
24300 (29 ) DR.- I GAMBLE QUITE A BIT ALSO.
24400 .END
24500 Here the algorithm interprets (29 ) as a directive to
24600 continue discussing gambling, not as an indication to question the
24700 doctor about gambling. The one exception to this principle occurs
24800 when the algorithm recognizes a chance to add to its model or
24900 representation of the interviewer.
25000
25100 .F
25200 ELLIPSES
25300
25400
25500 In dialogues one finds many ellipses, expressions from which
25600 one or more words are omitted as in:
25700 .V
25800 (30 ) PT.- I SHOULDNT BE HERE.
25900 (31) DR.- WHY NOT?
26000 .END
26100 Here the complete construction must be understood as:
26200 .V
26300 (32) DR.- WHY SHOULD YOU NOT BE HERE?
26400 .END
26500 Again this is handled by the expectancy list which anticipates a "why
26600 not".
26700 The opposite of ellipsis is redundancy which usually provides
26800 no problem since the same thing is being said more than once as in:
26900 .V
27000 (33 ) DR.- LET ME ASK YOU A QUESTION.
27100 .END
27200 If an analysis were required of this expression (it is not
27300 required here since the expression is a sterotype), it would be
27400 recognized that the verb "ask" takes the noun "question" as direct
27500 object and also a question is something that is asked.
27600
27700 .F
27800 SIGNALS
27900
28000 Some fragmentary expressions serve only as directive signals
28100 to proceed as in:
28200 .V
28300 (34) PT.- I WENT TO THE TRACK LAST WEEK.
28400 (35) DR.- AND?
28500 .END
28600 The fragment of (35) requests a continuation of the story introduced
28700 in (34). The common expressions found in interviews are "and", "so",
28800 "go on", "go ahead", "really", etc. If an input expression cannot be
28900 recognized at all, the lowest level default condition is to assume it
29000 is a signal and either proceed with the next line in a story under
29100 discussion or if the latter is not the case, begin a new story with a
29200 prompting question or statement.
29300
29400 .F
29500 IDIOMS
29600
29700 Since so much of conversational language is stereotyped, the
29800 task of recognition is much easier than that of analysis. This is
29900 particularly true of idioms. Either one knows what an idiom means or
30000 one does not. It is usually hopeless to try to decipher what an idiom
30100 means from an analysis of its constituent parts. If the reader doubts
30200 this, let him ponder the following expressions taken from actual
30300 teletyped interviews.
30400 .V
30500 (36) DR.- WHATS EATING YOU?
30600 (37) DR.- YOU SOUND KIND OF PISSED OFF.
30700 (38) DR.- WHAT ARE YOU DRIVING AT?
30800 (39) DR.- ARE YOU PUTTING ME ON?
30900 (40) DR.- WHY ARE THEY AFTER YOU?
31000 (41) DR.- HOW DO YOU GET ALONG WITH THE OTHER PATIENTS?
31100 (42) DR.- HOW DO YOU LIKE YOUR WORK?
31200 (43) DR.- HAVE THEY TRIED TO GET EVEN WITH YOU?
31300 (44) DR.- I CANT KEEP UP WITH YOU.
31400 .END
31500 Understanding idioms is a matter of rote memory. Hence an
31600 algorithm with a large idiom table is required. As each new idiom
31700 appears in teletyped interviews, it should be added to the idiom
31800 table because what happens once can happen again.
31900 One advantage in constructing an idiolect for a model is that
32000 it understands its own idiomatic expressions which tend to be used by
32100 the interviewer if he understands them as in:
32200 .V
32300 (45) PT.- THEY ARE OUT TO GET ME.
32400 (46) DR.- WHAT MAKES YOU THINK THEY ARE OUT TO GET YOU.
32500 .END
32600 The expression (45 ) is really a double idiom in which "out"
32700 means `intend' and "get" means `harm' in this context. Needless to
32800 say. an algorithm which tried to pair off the various meanings of
32900 "out" with the various meanings of "get" would have a hard time of
33000 it. But an algorithm which understands what it itself is capable of
33100 saying, can easily recognize echoed idioms.
33200
33300 .F
33400 FUZZ TERMS
33500
33600 In this category fall a large number of expressions which
33700 have little or no meaning and therefore can be ignored by the
33800 algorithm. The lower-case expressions in the following are examples
33900 of fuzz:
34000 .V
34100 (47) DR.- well now perhaps YOU CAN TELL ME something ABOUT YOUR FAMILY.
34200 (48) DR.- on the other hand I AM INTERESTED IN YOU.
34300 (49) DR.- hey I ASKED YOU A QUESTION.
34400 .END
34500 It is not the case that in order to ignore something one must
34600 recognize explicitly what is ignorable. Since pattern-matching allows
34700 for an `anything' slot in many of its patterns, fuzz is thus easily
34800 ignored.
34900
35000 .F
35100 SUBORDINATE CLAUSES
35200
35300 A subordinate clause is a complete statement inside another
35400 statement. It is most frequently introduced by a relative pronoun,
35500 indicated in the following expressions by lower case:
35600 .V
35700 (50) DR.- WAS IT THE UNDERWORLD that PUT YOU HERE?
35800 (51) DR.- WHO ARE THE PEOPLE who UPSET YOU?
35900 (52) DR.- HAS ANYTHING HAPPENED which YOU DONT UNDERSTAND?
36000 .END
36100 The words "whether" and "because" serving as conjunctions are
36200 less frequent. A language-analysis also must recognize that
36300 subordinate clauses can function as nouns, adjectives, adverbs, and
36400 objects of prepositions.
36500
36600 .F
36700 VOCABULARY
36800
36900 How many words should there be in the algorithm's vocabulary?
37000 It is a rare human speaker of English who can recognize 40% of the
37100 415,000 words in the Oxford English Dictionary. In his everyday
37200 conversation an educated person uses perhaps 10,000 words and has a
37300 recognition vocabulary of about 50,000 words. A study of phone
37400 conversations showed that 96 % of the talk employed only 737 words.
37500 (French, Carter, and Koening, 1930).
37600 Of course the remaining 4% , if not recognized, may be ruinous to the
37700 continuity of a conversation.
37800 In counting the words in 53 teletyped psychiatric interviews,
37900 we found psychiatrists used only 721 words. Since we are familiar
38000 with psychiatric vocabularies and styles of expression, we believed
38100 this language-algorithm could function adequately with a vocabulary
38200 of at most a few thousand words. There will always be unrecognized
38300 words. The algorithm must be able to continue even if it does not
38400 have a particular word in its vocabulary. This provision represents
38500 one great advantage of pattern-matching over conventional linguistic
38600 parsing.
38700 It is not the number of words which creates difficulties but
38800 their combinations. One thousand factorial is still a very large
38900 number. Syntactic and semantic constraints in stereotypes and in
39000 analysis reduce this number to an indefinitely large one.
39100
39200 .F
39300 MISSPELLINGS AND EXTRA CHARACTERS
39400 There is really no good defense against misspellings in a
39500 teletyped interview except having a human monitor retype the correct
39600 versions. Spelling correcting programs are slow, inefficient, and
39700 imperfect. They experience great problems when it is the first
39800 character in a word which is incorrect.
39900 Extra characters sent by the interviewer or by a bad phone
40000 line can be removed by a human monitor.
40100
40200 .F
40300 META VERBS
40400
40500 Certain common verbs such as "think", "feel", "believe", etc
40600 take as their objects a clause as in:
40700 .V
40800 (54) DR.- I THINK YOU ARE RIGHT.
40900 (55) DR.- WHY DO YOU FEEL THE GAMBLING IS CROOKED?
41000 .END
41100 The verb "believe" is peculiar since it can also take as
41200 object a noun or noun phrase as in:
41300 .V
41400 (56) DR.- I BELIEVE YOU.
41500 .END
41600 In expression (55) the conjunction "that" can follow the word
41700 "feel" signifying a subordinate clause. This is not the case after
41800 "believe" in expression (56).
41900
42000 .F
42100 ODD WORDS
42200
42300 These are words which are odd in the context of a teletyped
42400 interview while they are quite natural in the usual vis-a-vis
42500 interview in which the participants communicate through speech. This
42600 should be clear from the following examples in which the odd words
42700 appear in lower case:
42800 .V
42900 (57) DR.-YOU sound CONFUSED.
43000 (58) DR.- DID YOU hear MY LAST QUESTION?
43100 (59) DR.- WOULD YOU come in AND sit down PLEASE?
43200 (60) DR.- CAN YOU say WHO?
43300 (61) DR.- I WILL see YOU AGAIN TOMORROW.
43400 .END
43500
43600
43700 .F
43800 MISUNDERSTANDING
43900
44000 It is not fully recognized bt students of language how often people
44100 misunderstand one another in conversation and yet their
44200 dialogues proceed as if understanding and being understood had taken
44300 place.
44400 The classic story involves three partially deaf men cycling
44500 through the English counrtyside:
44600 .V
44700 FIRST - "WHAT TOWN IS THIS?"
44800 SECOND - "THURSDAY"
44900 THIRD - "ME TOO, LETS STOP AND HAVE A DRINK."
45000 .END
45100 Sometimes a psychiatric interviewer realizes when misunderstanding
45200 occurs and tries to correct it. Other times he simply passes it by. It is
45300 characteristic of the paranoid mode to respond idiosyncratically to
45400 particular word-concepts regardless of what the interviewer is saying:
45500 .V
45600 (62) PT.- IT IS NOT HELPING ME TO BE HERE.
45700 (63) DR.- I BET.
45800 (64) PT.- GAMBLING HAS BEEN NOTHING BUT TROUBLE FOR ME.
45900 .END
46000
46100 .F
46200 UNUNDERSTANDING
46300
46400 A dialogue algorithm must be prepared for situations
46500 in which it simply does not understand i.e. it cannot arrive at any
46600 interpretation as to what the interviewer is saying. An algorithm should
46700 not be faulted for a lack of facts as in:
46800 .V
46900 (65) DR.- WHO IS THE PRESIDENT OF TURKEY?
47000 .END CONTINUE
47100 wherin the memory does not contain the words "president" and "Turkey".
47200 In this default condition it is simplest to reply:
47300 .V
47400 (66) PT.- I DONT KNOW.
47500 .END CONTINUE
47600 and dangerous to reply:
47700 .V
47800 (67) PT.- COULD YOU REPHRASE THE QUESTION?
47900 .END CONTINUE
48000 because of the horrible loops which can result.
48100 Since the main problem in the default condition of ununderstanding
48200 is how to continue, heuristics can be employed such as asking about the
48300 interviewer's intention as in:
48400 .V
48500 (68) PT.- WHY DO YOU WANT TO KNOW THAT?
48600 .END CONTINUE
48700 or rigidly continuing with a previous topic or introducing a new topic.
48800 These are admittedly desperate measures intended to prompt
48900 the interviewer in directions the algorithm has a better chance of
49000 understanding. Usually it is the interviewer who controls the flow
49100 from topic to topic but there are times, hopefully few, when control
49200 must be assumed by the algorithm.