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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 language-behavior of a paranoid patient in a psychiatric
00600 interview, the model must have an ability to interpret and respond to
00700 natural language input sufficient to demonstrate conduct
00800 characteristic of the paranoid mode. By "natural language" I shall
00900 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]. Machine understanding of natural
01800 language is being attempted today but it is not something to be
01900 completly achieved today or even tomorrow. The problem is not to find
02000 immediately the best way of doing it but to find any way at all.
02100 During the 1960's when machine processing of natural language
02200 was dominated by syntactic considerations, it became clear that
02300 syntactical information alone was insufficient to comprehend the
02400 expressions of ordinary conversations. A current view is that to
02500 understand what is said in linguistic expressions, knowledge of
02600 syntax and semantics must be combined with beliefs from a conceptual
02700 structure capable of making inferences. How to achieve this
02800 combination efficiently with a large data-base represents a
02900 monumental task for both theory and implementation.
03000 We did not attempt to construct a conventional linguistic
03100 parser to deal with natural language input for several reasons.
03200 Parsers to date have great difficulty in assigning a meaningful
03300 interpretation to the expressions of everyday conversational language
03400 using unrestricted English. Purely syntactic parsers offer a
03500 cancerous proliferation of interpretations. A conventional parser may
03600 simply halt when it comes across a word not in its dictionary.
03700 Parsers represent tight conjunctions of tests instead of loose
03800 disjunctions needed for everyday language which may involve
03900 misunderstandinga and ununderstandings. People misunderstand and
04000 ununderstand at times and thus are partially opaque to one another.
04100 The language analysis utilized by the model first puts the
04200 input in the form of a list and then determines the syntactical type
04300 for the input expression- question, statement or imperative. The
04400 expression-type is scanned in an attempt to form a conceptualization,
04500 i.e. a predication of an attribute, on an object or a relation
04600 between objects. An attribute consists of something one is or does or
04700 possesses. The resultant conceptualization is then classified
04800 according to the rules of Fig. 00 in %000 as malevolent, benevolent
04900 or neutral.
05000 How language is understood depends on the intentions of the
05100 producers and interpreters in the dialogue. Thus language is
05200 understood in accordance with the participant's view of the
05300 situation. Our purpose was to develop a method for understanding
05400 everyday English sufficient for the model to communicate
05500 linguistically in a paranoid way in the circumscribed situation of a
05600 psychiatric interview. Such an interview is not small talk; a job is
05700 to be done.
05800 We did not try to construct a general-purpose algorithm which
05900 could understand anything said in English by anybody to anybody in
06000 any dialogue situation. (Does anyone believe it possible?) We sought
06100 only to extract or distill an idiolectic meaning or gist of a meaning
06200 from the input. We took as pragmatic measures of "understanding" the
06210 Natural language is not an agree-on universe of discourse
06220 such as arithmetic wherein symbols have the same meaning for everyone
06221 who uses them. What we loosely call "natural language" is actually a set
06222 of idiolects, each being unique to the individual with a unique history.
06223 To be unique does not mean that no property is shared with other
06224 individuals, only that not every property is shared. An overlap of
06225 idiolects allows the communication of shared meanings.
06227 We took as pragmatic measures of "understanding" the
06300 ability (1) to form a conceptualization so that questions can be
06400 answered and commands carried out, (2) to determine the intention of
06500 the interviewer, (3) to determine the references for pronouns and
06600 other anticipated topics. This straightforward approach to a complex
06700 problem has its drawbacks, as will be shown, but we strove for a
06800 highly individualized idiolect sufficient to demonstrate paranoid
06900 processes of an individual in a particular situation rather than for
07000 a general supra-individual or ideal comprehension of English. If the
07100 language-understanding system interfered with demonstrating the
07200 paranoid processes, we would consider it defective and insufficient
07300 for our purposes.
07400 Some special problems a dialogue algorithm must handle in a
07500 psychiatric interview will now be outlined along with a brief
07600 description of how the model deals with them.
07700
07800 .F
07900 QUESTIONS
08000
08100 The principal expression-type used by an interviewer consists
08200 of a question. A question is recognized by its beginning with a wh-
08300 or how form and/or the expression ending with a question-mark. In
08400 teletyped interviews a question may sometimes be put in declarative
08500 form followed by a question mark as in:
08600 .V
08700 (1) PT.- I LIKE TO GAMBLE ON THE HORSES.
08800 (2) DR.- YOU GAMBLE?
08900 .END
09000 Although the verb is missing in (2), the model recognizes that a
09100 question is being asked about its gambling.
09200
09300 Particularly difficult are `when' questions which require a memory
09400 which can assign each event a beginning, end and a duration. An
09500 improved version of the model will have this capacity. Also
09600 troublesome are questions such as `how often', `how many', i.e. a
09700 `how' followed by a quantifier. If the model has "how often" on its
09800 expectancy list while a topic is under discussion, the appropriate
09900 reply can be made. Otherwise the model ununderstands.
10000 In constructing a simulation of a thought process it is
10100 arbitrary how much information to represent in memory. Should the
10200 model know what is the capital of Alabama? It is trivial to store a
10300 lot of facts. We took the position that the model should know only
10400 what we believed it reasonable to know relevant to a few hundred
10500 topics expectable in a psychiatric interview. Thus the model performs
10600 badly when subjected to baiting `exam' questions designed to test its
10700 informational limitations rather than to seek useful psychiatric
10800 information.
10900
11000 .F
11100 IMPERATIVES
11200
11300 Typical imperatives in a psychiatric interview consist of
11400 expressions like:
11500 .V
11600 (3) DR.- TELL ME ABOUT YOURSELF.
11700 (4) DR.- LETS DISCUSS YOUR FAMILY.
11800 .END
11900 Such imperatives are actually interrogatives to the
12000 interviewee about the topics they refer to. Since the only physical
12100 action the model can perform is to `talk' , imperatives are treated
12200 as requests for information.
12300
12400 .F
12500 DECLARATIVES
12600
12700 In this category is lumped everything else. It includes
12800 greetings, farewells, yes-no type answers, existence assertions and
12900 the usual predications.
13000
13100 .F
13200 AMBIGUITIES
13300
13400 Words have more than one sense, a convenience for human
13500 memories but a struggle for language-analysing algorithms. Consider
13600 the word "bug" in the following expressions:
13700 .V
13800 (5) AM I BUGGING YOU?
13900 (6) AFTER A PERIOD OF HEAVY DRINKING HAVE YOU FELT BUGS ON
14000 YOUR SKIN?
14100 (7) DO YOU THINK THEY PUT A BUG IN YOUR ROOM?
14200 .END
14300 In expression (5) the term "bug" means to annoy, in (6) it
14400 refers to an insect and in (7) it refers to a microphone used for
14500 hidden survellience. Some words like "run" have fifty or more common
14600 senses. Context is used to carry out disambiguation. For example,
14700 when the Mafia is under discussion the model interprets "bug" to mean
14800 microphone. Thus we have the advantage of an idiolect where we can
14900 arbitrarily restrict the word senses. One characteristic of the
15000 paranoid mode is that no matter in what sense the interviewer uses a
15100 word, the patient may idiosyncratically interpret it in some sense
15200
15300 .F
15400 ANAPHORIC REFERENCES
15500 The common anaphoric references consist of the pronouns "it",
15600 "he", "him", "she", "her", "they", "them" as in:
15700 .V
15800 (8) PT.-HORSERACING IS MY HOBBY.
15900 (9) DR.-WHAT DO YOU ENJOY ABOUT IT?
16000 .END
16100 The algorithm recognizes "it" as referring to "horseracing"
16200 because "it" has been placed on the expectancy list when horseracing
16300 was introduced in (8). A more difficult problem arises when the
16400 anaphoric reference points more than one I/O pair back in the
16500 dialogue as in:
16600 .V
16700 (10) PT.-THE MAFIA IS OUT TO GET ME.
16800 (11) DR.- ARE YOU AFRAID OF THEM?
16900 (12) PT.- MAYBE.
17000 (13) DR.- WHY IS THAT?
17100 .END
17200 The "that" of expression (13) does not refer to (12) but to
17300 the topic of being afraid which the interviewer introduced in (11).
17400 Another pronominal confusion occurs when the interviewer uses `we' in
17500 two senses as in:
17600 .V
17700 (14) DR.- WE WANT YOU TO STAY IN THE HOSPITAL.
17800 (15) PT.- I WANT TO BE DISCHARGED NOW.
17900 (16) DR.- WE ARE NOT COMMUNICATING.
18000 .END
18100 In expression (14) the interviewer is using "we" to refer to
18200 psychiatrists or the hospital staff while in (16) the term refers to
18300 the interviewer and patient. Identifying the correct referent would
18400 require beliefs about the dialogue which the new version of the model
18500 will have.
18600
18700 .F
18800 TOPIC SHIFTS
18900
19000 In the main a psychiatric interviewer is in control of the
19100 interview. When he has gained sufficient information about a topic,
19200 he shifts to a new topic. Naturally the algorithm must detect this
19300 change of topic as in the following:
19400 .V
19500 (17) DR.- HOW DO YOU LIKE THE HOSPITAL?
19600 (18) PT.- ITS NOT HELPING ME TO BE HERE.
19700 (19) DR.- WHAT BROUGHT YOU TO THE HOSPITAL?
19800 (20) PT.- I AM VERY UPSET AND NERVOUS.
19900 (21) DR.- WHAT TENDS TO MAKE YOU NERVOUS?
20000 (23) PT.- JUST BEING AROUND PEOPLE.
20100 (24) DR.- ANYONE IN PARTICULAR?
20200 .END
20300 In (17) and (19) the topic is the hospital. In (21) the topic
20400 changes to causes of the patient's nervous state.
20500 When a topic is introduced by the patient as in (20), a
20600 number of things can be expected to be asked about it. Thus the
20700 algorithm has ready an updated expectancy-anaphora list which allows
20800 it to determine whether the topic introduced by the model is being
20900 responded to or whether the interviewer is continuing with the
21000 previous topic.
21100 Topics touched upon previously can be re-introduced at any
21200 point in the interview. The model knows that a topic has been
21300 discussed previously because a topic-flag is set when a topic comes
21400 up.
21500
21600 .F
21700 META-REFERENCES
21800
21900 These are references, not about a topic directly, but about
22000 what has been said about the topic as in:
22100 .V
22200 (25) DR.- WHY ARE YOU IN THE HOSPITAL?
22300 (26) PT.- I SHOULDNT BE HERE.
22400 (27) DR.- WHY DO YOU SAY THAT?
22500 .END
22600 The expression (27 ) is about and meta to expression (26 ).
22700 Sometimes when the patient makes a statement, the doctor
22800 replies, not with a question, but with another statement which
22900 constitutes a rejoinder as in:
23000 .V
23100 (28 ) PT.- I HAVE LOST A LOT OF MONEY GAMBLING.
23200 (29 ) DR.- I GAMBLE QUITE A BIT ALSO.
23300 .END
23400 Here the algorithm interprets (29 ) as a directive to
23500 continue discussing gambling, not as an indication to question the
23600 doctor about gambling. The one exception to this principle occurs
23700 when the algorithm recognizes a chance to add to its model or
23800 representation of the interviewer.
23900
24000 .F
24100 ELLIPSES
24200
24300
24400 In dialogues one finds many ellipses, expressions from which
24500 one or more words are omitted as in:
24600 .V
24700 (30 ) PT.- I SHOULDNT BE HERE.
24800 (31) DR.- WHY NOT?
24900 .END
25000 Here the complete construction must be understood as:
25100 .V
25200 (32) DR.- WHY SHOULD YOU NOT BE HERE?
25300 .END
25400 Again this is handled by the expectancy list which anticipates a "why
25500 not".
25600 The opposite of ellipsis is redundancy which usually provides
25700 no problem since the same thing is being said more than once as in:
25800 .V
25900 (33 ) DR.- LET ME ASK YOU A QUESTION.
26000 .END
26100 If an analysis were required of this expression (it is not
26200 required here since the expression is a sterotype), it would be
26300 recognized that the verb "ask" takes the noun "question" as direct
26400 object and also a question is something that is asked.
26500
26600 .F
26700 SIGNALS
26800
26900 Some fragmentary expressions serve only as directive signals
27000 to proceed as in:
27100 .V
27200 (34) PT.- I WENT TO THE TRACK LAST WEEK.
27300 (35) DR.- AND?
27400 .END
27500 The fragment of (35) requests a continuation of the story introduced
27600 in (34). The common expressions found in interviews are "and", "so",
27700 "go on", "go ahead", "really", etc. If an input expression cannot be
27800 recognized at all, the lowest level default condition is to assume it
27900 is a signal and either proceed with the next line in a story under
28000 discussion or if the latter is not the case, begin a new story with a
28100 prompting question or statement.
28200
28300 .F
28400 IDIOMS
28500
28600 Since so much of conversational language is stereotyped, the
28700 task of recognition is much easier than that of analysis. This is
28800 particularly true of idioms. Either one knows what an idiom means or
28900 one does not. It is usually hopeless to try to decipher what an idiom
29000 means from an analysis of its constituent parts. If the reader doubts
29100 this, let him ponder the following expressions taken from actual
29200 teletyped interviews.
29300 .V
29400 (36) DR.- WHATS EATING YOU?
29500 (37) DR.- YOU SOUND KIND OF PISSED OFF.
29600 (38) DR.- WHAT ARE YOU DRIVING AT?
29700 (39) DR.- ARE YOU PUTTING ME ON?
29800 (40) DR.- WHY ARE THEY AFTER YOU?
29900 (41) DR.- HOW DO YOU GET ALONG WITH THE OTHER PATIENTS?
30000 (42) DR.- HOW DO YOU LIKE YOUR WORK?
30100 (43) DR.- HAVE THEY TRIED TO GET EVEN WITH YOU?
30200 (44) DR.- I CANT KEEP UP WITH YOU.
30300 .END
30400 Understanding idioms is a matter of rote memory. Hence an
30500 algorithm with a large idiom table is required. As each new idiom
30600 appears in teletyped interviews, it should be added to the idiom
30700 table because what happens once can happen again.
30800 One advantage in constructing an idiolect for a model is that
30900 it understands its own idiomatic expressions which tend to be used by
31000 the interviewer if he understands them as in:
31100 .V
31200 (45) PT.- THEY ARE OUT TO GET ME.
31300 (46) DR.- WHAT MAKES YOU THINK THEY ARE OUT TO GET YOU.
31400 .END
31500 The expression (45 ) is really a double idiom in which "out"
31600 means `intend' and "get" means `harm' in this context. Needless to
31700 say. an algorithm which tried to pair off the various meanings of
31800 "out" with the various meanings of "get" would have a hard time of
31900 it. But an algorithm which understands what it itself is capable of
32000 saying, can easily recognize echoed idioms.
32100
32200 .F
32300 FUZZ TERMS
32400
32500 In this category fall a large number of expressions which
32600 have little or no meaning and therefore can be ignored by the
32700 algorithm. The lower-case expressions in the following are examples
32800 of fuzz:
32900 .V
33000 (47) DR.- well now perhaps YOU CAN TELL ME something ABOUT YOUR FAMILY.
33100 (48) DR.- on the other hand I AM INTERESTED IN YOU.
33200 (49) DR.- hey I ASKED YOU A QUESTION.
33300 .END
33400 It is not the case that in order to ignore something one must
33500 recognize explicitly what is ignorable. Since pattern-matching allows
33600 for an `anything' slot in many of its patterns, fuzz is thus easily
33700 ignored.
33800
33900 .F
34000 SUBORDINATE CLAUSES
34100
34200 A subordinate clause is a complete statement inside another
34300 statement. It is most frequently introduced by a relative pronoun,
34400 indicated in the following expressions by lower case:
34500 .V
34600 (50) DR.- WAS IT THE UNDERWORLD that PUT YOU HERE?
34700 (51) DR.- WHO ARE THE PEOPLE who UPSET YOU?
34800 (52) DR.- HAS ANYTHING HAPPENED which YOU DONT UNDERSTAND?
34900 .END
35000 The words "whether" and "because" serving as conjunctions are
35100 less frequent. A language-analysis also must recognize that
35200 subordinate clauses can function as nouns, adjectives, adverbs, and
35300 objects of prepositions.
35400
35500 .F
35600 VOCABULARY
35700
35800 How many words should there be in the algorithm's vocabulary?
35900 It is a rare human speaker of English who can recognize 40% of the
36000 415,000 words in the Oxford English Dictionary. In his everyday
36100 conversation an educated person uses perhaps 10,000 words and has a
36200 recognition vocabulary of about 50,000 words. A study of phone
36300 conversations showed that 96 % of the talk employed only 737 words.
36310 (French, Carter, and Koening, 1930).
36400 Of course the remaining 4% , if not recognized, may be ruinous to the
36500 continuity of a conversation.
36600 In counting the words in 53 teletyped psychiatric interviews,
36700 we found psychiatrists used only 721 words. Since we are familiar
36800 with psychiatric vocabularies and styles of expression, we believed
36900 this language-algorithm could function adequately with a vocabulary
37000 of at most a few thousand words. There will always be unrecognized
37100 words. The algorithm must be able to continue even if it does not
37200 have a particular word in its vocabulary. This provision represents
37300 one great advantage of pattern-matching over conventional linguistic
37400 parsing.
37500 It is not the number of words which creates difficulties but
37600 their combinations. One thousand factorial is still a very large
37700 number. Syntactic and semantic constraints in stereotypes and in
37800 analysis reduce this number to an indefinitely large one.
37900
38000 .F
38100 MISSPELLINGS AND EXTRA CHARACTERS
38200 There is really no good defense against misspellings in a
38300 teletyped interview except having a human monitor retype the correct
38400 versions. Spelling correcting programs are slow, inefficient, and
38500 imperfect. They experience great problems when it is the first
38600 character in a word which is incorrect.
38700 Extra characters sent by the interviewer or by a bad phone
38800 line can be removed by a human monitor.
38900
39000 .F
39100 META VERBS
39200
39300 Certain common verbs such as "think", "feel", "believe", etc
39400 take as their objects a clause as in:
39500 .V
39600 (54) DR.- I THINK YOU ARE RIGHT.
39700 (55) DR.- WHY DO YOU FEEL THE GAMBLING IS CROOKED?
39800 .END
39900 The verb "believe" is peculiar since it can also take as
40000 object a noun or noun phrase as in:
40100 .V
40200 (56) DR.- I BELIEVE YOU.
40300 .END
40400 In expression (55) the conjunction "that" can follow the word
40500 "feel" signifying a subordinate clause. This is not the case after
40600 "believe" in expression (56).
40700
40800 .F
40900 ODD WORDS
41000
41100 These are words which are odd in the context of a teletyped
41200 interview while they are quite natural in the usual vis-a-vis
41300 interview in which the participants communicate through speech. This
41400 should be clear from the following examples in which the odd words
41500 appear in lower case:
41600 .V
41700 (57) DR.-YOU sound CONFUSED.
41800 (58) DR.- DID YOU hear MY LAST QUESTION?
41900 (59) DR.- WOULD YOU come in AND sit down PLEASE?
42000 (60) DR.- CAN YOU say WHO?
42100 (61) DR.- I WILL see YOU AGAIN TOMORROW.
42200 .END
42300
42400
42500 .F
42600 MISUNDERSTANDING
42700
42800 It is not fully recognized bt students of language how often people
42900 misunderstand one another in conversation and yet their
43000 dialogues proceed as if understanding and being understood had taken
43100 place.
43200 The classic story involves three partially deaf men cycling
43300 through the English counrtyside:
43400 .V
43500 FIRST - "WHAT TOWN IS THIS?"
43600 SECOND - "THURSDAY"
43700 THIRD - "ME TOO, LETS STOP AND HAVE A DRINK."
43800 .END
43900 Sometimes a psychiatric interviewer realizes when misunderstanding
44000 occurs and tries to correct it. Other times he simply passes it by. It is
44100 characteristic of the paranoid mode to respond idiosyncratically to
44200 particular word-concepts regardless of what the interviewer is saying:
44300 .V
44400 (62) PT.- IT IS NOT HELPING ME TO BE HERE.
44500 (63) DR.- I BET.
44600 (64) PT.- GAMBLING HAS BEEN NOTHING BUT TROUBLE FOR ME.
44700 .END
44800
44900 .F
45000 UNUNDERSTANDING
45100
45200 A dialogue algorithm must be prepared for situations
45300 in which it simply does not understand i.e. it cannot arrive at any
45400 interpretation as to what the interviewer is saying. An algorithm should
45500 not be faulted for a lack of facts as in:
45600 .V
45700 (65) DR.- WHO IS THE PRESIDENT OF TURKEY?
45800 .END CONTINUE
45900 wherin the memory does not contain the words "president" and "Turkey".
46000 In this default condition it is simplest to reply:
46100 .V
46200 (66) PT.- I DONT KNOW.
46300 .END CONTINUE
46400 and dangerous to reply:
46500 .V
46600 (67) PT.- COULD YOU REPHRASE THE QUESTION?
46700 .END CONTINUE
46800 because of the horrible loops which can result.
46900 Since the main problem in the default condition of ununderstanding
47000 is how to continue, heuristics can be employed such as asking about the
47100 interviewer's intention as in:
47200 .V
47300 (68) PT.- WHY DO YOU WANT TO KNOW THAT?
47400 .END CONTINUE
47500 or rigidly continuing with a previous topic or introducing a new topic.
47600 These are admittedly desperate measures intended to prompt
47700 the interviewer in directions the algorithm has a better chance of
47800 understanding. Usually it is the interviewer who controls the flow
47900 from topic to topic but there are times, hopefully few, when control
48000 must be assumed by the algorithm.