Bayan Aref Abu Shawar
Submitted in accordance with the requirements for the degree of Doctor of Philosophy
University of Leeds School of Computing
April 2005
The candidate confirms that the work submitted is his/her own and that appropriate credit has been given where reference has been made to the work
of others.
This copy has been supplied on the understanding that it is copyright material and that no quotation from the thesis may be published without proper
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1. Abu Shawar B. and Atwell E. 2002. A comparison between ALICE and Elizabeth chatbot systems. School of Computing research report 2002.19, University of Leeds.
2. Abu Shawar B. and Atwell E. 2003. Machine Learning from dialogue corpora to generate chatbots. Expert Update journal, Vol. 6, No 3, pp 25-30
3. Abu Shawar B. and Atwell E. 2003. Using dialogue corpora to retrain a chatbot system. In Archer, D., Rayson, P., Wilson, A. and McEnery, T. (eds.) Proceedings of the Corpus Linguistics 2003 conference. Lancaster University, UK, pp. 681-690.
4. Abu Shawar B. and Atwell E. 2003. Using the Corpus of Spoken Afrikaans to generate an Afrikaans chatbot. SALALS Journal: Southern African Linguistics and Applied Language Studies. Vol. 21, pp. 283-294. 5. Abu Shawar B. 2003. Review of CL2003, the International Conference on
Corpus Linguistics”, ELRA newsletter. Vol. 8, No. 2, pp. 4-6.
6. Abu Shawar B. and Atwell E. 2004. An Arabic chatbot giving answers from the Qur'an. In Bel, B & Marlien, I (eds.) Proceedings of TALN04: XI Conference sur le Traitement Automatique des Langues Naturelles. Vol. 2, pp. 197-202.
7. Atwell E.; Al-Sulaiti L.; Al-Osaimi S.; Abu Shawar B. 2004. A review of Arabic corpus analysis tools. In Bel, B & Marlien, I (eds.) Proceedings of TALN04: XI Conference sur le Traitement Automatique des Langues Naturelles. Vol. 2, pp. 229-234.
8. Abu Shawar B. 2004. The NWCL Research Training Programme. ELSnews: The Newsletter of the European Network in Human Language Technologies. Vol. 13.1, pp. 14-15.
9. Abu Shawar B. and Atwell E. 2004. A chatbot as a novel corpus visualisation tool. In Lino, M.T., Xavier, M.F., Ferreira, F., Costa, R., Silva, R., Pereira, C., Caevalho, F., Lopes, M., Catarino, M. and Barros, S. (eds.) Proceedings of Fourth International Conference on Language Resources and Evaluation (LREC’04). Vol. VI, pp. 2057-2060.
Proceedings of Eight Maghrebian Conference on Software Engineering and Artificial Intelligence.
11. Abu Shawar B. and Atwell E. 2004. Accessing an information system by chatting. In Meziane, F. and Metais, E. (eds.) Proceedings of 9th International conference on the Application of Natural Language to Information Systems, NLDB 2004 Salford, UK. LNCS Lecture Notes in Computer Science, Springer, pp. 396-401
12. Abu Shawar B. and Atwell E. 2005. Modelling turn-taking in a corpus-trained chatbot. In Fisseni B., Schmitz H., Schröder B. and Wagner P. (eds.), Sprachtechnologie, Mobile Communication und Linguistische Ressourcen (GLDV05). Peter Lang Verlag, Bonn, pp. 549-561.
13. Abu Shawar B., Atwell E. and Roberts, A. 2005. FAQchat as an information retrieval system. In: Zygmunt V. (ed.), Human Language Technologies as a Challenge for Computer Science and Linguistics: Proceedings of the 2ndLanguage and Technology Conference, Wydawnictwo Poznanskie, Poznan, pp. 274-278.
14. Abu Shawar B and Atwell E. 2005. A chatbot system as a tool to animate a corpus. ICAME Journal. Vol. 29, pp. 5-23.
15. Abu Shawar B. and Atwell E. 2005. Using corpora in machine-learning chatbot systems. International Journal of Corpus Linguistics [forthcoming].
“Do not consider even the smallest good deed as insignificant; even meeting your brother with a cheerful face (is a good deed)."
Prophet Mohammed (pbuh)
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A86494 7 59423 7 325553467 123453467 647274856 7 64727956 7 64727889542367379236 7 1. Introduction ... 1 1.1 Overview ... 1
1.2 Chatbots and A.I. disciplines... 2
1.2.1 Corpus and computational linguistics ... 2
1.2.2 Information seeking and knowledge acquisition... 3
1.3 Chatbot evaluation... 4
1.4 Thesis objectives ... 4
1.5 Thesis structure... 6
2. AI Disciplines and Chatbot History... 7
viii
2.2.1 Computational linguistics and language engineering... 9
2.3 Chatbot history ... 12
2.4 Recent chatbots... 13
2.5 Chatbot communication language ... 18
2.6 Chatbots and pattern-matching techniques... 20
2.6.1 Information retrieval... 21
2.6.2 Information extraction... 22
2.6.3 Question answering systems ... 25
2.6.4 Summary of pattern-matching techniques... 28
2.7 Machine learning techniques... 28
2.8 ALICE chatbot system ... 30
2.8.1 Overview of AIML... 30
2.8.2 Types of categories... 32
2.8.3 The recursive categories... 32
2.8.4 Preparation for pattern-matching in ALICE... 35
2.8.5 The ALICE pattern-matching algorithm ... 37
2.9 Elizabeth system... 41
2.9.1 Elizabeth’s script file format ... 41
2.9.2 Dynamic processing in Elizabeth ... 42
2.9.3 Elizabeth’s pattern-matching algorithm ... 43
2.9.4 Implementing grammatical rules in Elizabeth... 45
2.10 A comparison between ALICE and Elizabeth... 46
2.11 Evaluation methodologies ... 47
2.11.1 Evaluating spoken language dialogue systems ... 48
2.11.2 The Loebner Prize ... 49
3.1 Introduction ... 55
3.1 The Dialog Diversity Corpus ... 56
3.1.1 MICAS corpus problems... 57
3.1.2 CIRCLE corpus problems ... 57
3.1.3 CSPA corpus problems ... 59
3.1.4 The TRAINS dialog corpus problems... 60
3.1.5 ICE-Singapore problems ... 61
3.1.6 Mishler Book... 62
3.2 The Korpus Gesprooke Afrikaans corpus ... 62
3.2.1 Afrikaans corpus problems... 63
3.2.2 Principles adopted to solve the KGA problems ... 64
3.3 The British National Corpus... 65
3.3.1 The BNC problems... 66
3.3.2 The principles adopted to solve the BNC spoken text problems ... 70
3.4 The Qur’an corpus... 71
3.4.1 Problems of learning from the Qur’an text ... 72
3.4.2 The adopted principles to solve the Qur’an problems... 74
3.5 The SoC Frequently Asked Questions (FAQ) corpus ... 74
3.5.1 The FAQ problems ... 75
3.6 Summary ... 78
4. Retraining ALICE ... 80
4.1 Introduction ... 80
4.2 Software developments ... 81
4.2.1 Software requirements specification ... 82
4.2.2 Software design and implementation ... 83
x
4.4 The KGA prototype... 86
4.4.1 Learning techniques ... 86
4.4.2 Elaborating the system architecture... 88
4.5 The BNC prototype ... 90
4.5.1 The BNC frequency list... 90
4.5.2 The alteration method... 91
4.6 The Qur’an prototype ... 93
4.7 The SoC FAQ prototype (FAQchat) ... 96
4.8 Summary ... 99
5. Evaluation ... 100
5.1 Introduction ... 100
5.2 Human-to-human versus human-to-chatbot dialogues... 101
5.2.1 Semantic comparison ... 102
5.2.2 Part-of-Speech comparison ... 103
5.2.3 Lexical comparison ... 104
5.3 Evaluating the Afrikaans version ... 105
5.3.1 Dialogue efficiency metrics... 106
5.3.2 Dialogue quality metrics ... 107
5.3.3 User satisfaction ... 109
5.3.4 The methodology drawbacks... 109
5.4 Evaluating the BNC versions ... 110
5.4.1 Results and discussion... 110
5.5 Evaluating the Qur’an versions ... 116
5.5.1 Results and discussion of the Arabic version... 116
5.5.2 Results and discussion of the English/Arabic version... 117
5.6.1 Comparing the FAQchat with Google... 124
5.6.2 The methodology... 125
5.6.3 Results and discussions ... 127
5.6.4 Testing reliability ... 132
5.6.5 Samples of chatting ... 135
5.7 Summary ... 136
6. Conclusions ... 139
6.1 Summary of the work ... 139
6.2 Results ... 141 6.3 System drawbacks ... 143 6.4 Future work ... 144 6.4.1 Project aims ... 145 6.4.2 Project plans ... 145 6.5 Overall conclusions ... 146 7. References ... 147
xii Table 1. 1 Summary of system prototypes, corpora used, and the goals of
implementations ... 5
Table 2. 1 Some recent chatbots... 17
Table 2. 2 The normalisation process in ALICE pattern-matching processing ... 36
Table 2. 3 Producing the input path by the AIML interpreter... 37
Table 2. 4 Tracing an Elizabeth output ... 45
Table 2. 5 A list of the Loebner prize winners from 1991-2004... 53
Table 4. 1 The trainable corpora... 82
Table 5. 1 Response type frequencies for “Afrikaana”, Afrikaans chatbot... 107
Table 5. 2 Response type frequencies: subjective analysis of “Afrikaana” response .... ... 108
Table 5. 3 The BNC London teenager and loudmouth chatbots ... 112
Table 5. 4 Different BNC versions... 113
Table 5. 5 Characteristics of the English/Arabic Qur’an chatbots ... 117
Table 5. 6 Proportions of answers generated from the Qur’an... 121
Table 5. 7 Can you find the answer? Analysed per question ... 127
Table 5. 8 Proportion of users finding answers by FAQchat and Google... 128
Table 5. 9 Which tool do you prefer? Analysed per question ... 129
Table 5. 10 The proportion of users’ preference tool, analysed per question ... 129
Table 5. 11 Number of matches found per user ... 130
Table 5. 12 The mean for giving answers and preference each user found ... 131
Table 5. 13 Paired t-Test for finding answers analysed per question and per user .. 133
Table 5. 14 Paired t-Test for users preferred tool analysed per question and per user ... 134
Figure 2. 1 The AIML format... 31
Figure 2. 2 Samples of AIML categories, illustrates the usage of the recursive categories... 34
Figure 2. 3 Log-plot visualisation of 24,000-state Graphmaster... 37
Figure 2. 4 An example of an AIML code ... 39
Figure 2. 5 Tracing of ALICE matching process ... 40
Figure 2. 6 Elizabeth script command notations ... 41
Figure 2. 7 An example of Elizabeth script: illistrate different keyword types ... 44
Figure 2. 8 Grammar representation rules in Elizabeth... 46
Figure 3. 1 Astronomy transcripts sample/MICASE corpus... 57
Figure 3. 2 Algebra transcript sample/CIRCLE corpus... ... 58
Figure 3. 3 Physics transcript sample/CIRCLE corpus... 58
Figure 3. 4 CSPA corpus sample... 59
Figure 3. 5 TRAINS corpus sample ... 60
Figure 3. 6 ICE-Singapore corpus sample... 61
Figure 3. 7 Sample of the Korpus Gesprooke Afrikaans corpus... 63
Figure 3. 8 A sample of a BNC spoken transcript... 66
Figure 3. 9 A sample representing long monologue in the BNC ... 67
Figure 3. 10 A sample representing unclear utterances in the BNC. ... 68
Figure 3. 11 A sample showing the overlapping problem in the BNC ... 68
Figure 3. 12 Extra-linguistic entities cause problems in the BNC ... 69
Figure 3. 13 A sample of the Qur’an in Arabic and English... 73
Figure 3. 14 The extra notations used within FAQ of SoC files for avigation ... 76
Figure 3. 15 Questions-answers denoted by <DIV CLASS=“sect1”> and “<p>” tags... 76
Figure 3. 16 FAQ questions-answers denoted by “question” and “answer” tags... 77
xiv Figure 4. 1 A sample of MICASE transcript with its corresponding AIML
category ... 85
Figure 4. 2 An atomic category with its most significant word in 4 positions... 87
Figure 4. 3 The program algorithm of the KGA prototype. ... 88
Figure 4. 4 A sample of Kilgariff unlemmatised frequency list... 90
Figure 5. 1 Screenshot of the semantic comparison between ALICE (01) and CSPA (02) ... 102
Figure 5. 2 Semantic comparisons between ALICE and CSPA... 102
Figure 5. 3 Screenshot of PoS comparison between ALICE (01) and CSPA (02) .. 103
Figure 5. 4 Part-of-Speech comparison between ALICE (01) and CSPA (02)... 104
Figure 5. 5 Screenshot of word comparison between ALICE (01) and CSPA (02). 104 Figure 5. 6 Word comparison between ALICE (01) and CSPA (02)... 105
Figure 5. 7 Dialogue efficiency: "Afrikaana" matching types relative probabilities107 Figure 5. 8 The quality of the dialogue: "Afrikaana" response type relative probabilities ... 108
Figure 5. 9 A sample of chatting with the BNC chatbot ... 111
Figure 5. 10 A sample of chatting with Peter-BNC chatbot ... 113
Figure 5. 11 A sample of chatting with the sport-BNC chatbot... 114
Figure 5. 12 A sample of chatting with the Arabic Qur’an chatbot ... 117
Figure 5. 13 A sample of chatting with the English/Arabic Qur’an chatbot... 118
Figure 5. 14 A sample of matching a whole ayya via chatting with English/Arabic chatbot ... 119
Figure 5. 15 The Qur’an proportion of each answer type denoted by users ... 121
Figure 5. 16 FAQchat interface... 126
Figure 5. 17 The proportion of finding answers by the FAQchat and Google... 128
Figure 5. 18 Which tool do you prefer? Summary... 130
Figure 5. 19 Comparing t-Test values fro finding answers... 133
Figure 5. 20 Comparing t-Test values fro the preferred tool... 134
Figure 5. 21 Answers generated by FAQchat ... 135
List of abbreviations and acronyms
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1. Introduction
“Vague and nebulous is the beginning of all things, but not their end, And I fain would you remember me as a beginning.”
Gibran Khalil Gibran, “The Prophet”
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Prototype Learning technique(s) Goals
The DDC Simple approach:
the first turn is the pattern and the second is the template.
Exploring the problems of using a dialogue corpus (Abu Shawar and Atwell 2003a)
The KGA Simple approach, the first word approach, the first most significant word approach.
Conversational machine for a new language (Abu Shawar and Atwell 2003b).
The BNC Simple approach, the first word approach, the first most significant word,
and the second most significant words
Generating a large number of AIML categories (Abu Shawar and Atwell 2004a), and visualising/animating a corpus (Abu shawar and Atwell 2005a).
The Qur’an The same as the BNC
techniques. Exploring problems in using Arabic language and non-conversational text (Abu Shawar and Atwell 2004b).
The SoC FAQ
Same as the BNC, and the first word with the first most significant word, and the second most
significant word.
Exploring the use as a tool to retrieve answers from FAQ databases (Abu Shawar et al. 2005b).
Table 1. 1 Summary of system prototypes, corpora used, and the goals of implementations
12
345678984874
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“...If you thought you must measure time into seasons, let each season encircle all the other seasons, and let today embrace the past with remembrance and the future with longing.”
Gibran Kalil Gibran,”The prophet”
2.1 Introduction
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2.2.1 Computational linguistics and language engineering
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2.3 Chatbot history
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2.4 Recent chatbots
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3
Chatbot Application Communication mode
9133 13 57593 863 3 93 53 53 3 63 573 8563 73 5537753375385733 3935633 !339333 9655333"#$63 %&933
PC Therapist3 Simulate a Rogerian therapist, inspired from ELIZA. Different personalities have been developed such as: PC professor, discusses men versus women; PC Politician, discusses Liberals versus Conservatives.
Spoken mode
Speak2Me4 A female chatbot that is used to teach English language through chatting.
Input: textual mode Output: spoken and textual mode.
Marloes2 A female Dutch financial advisor. Spoken mode
MIA5 A German advisor on opening a bank account.
Textual mode
Cybelle6 A female avatar with body and uses gestures while talking. She directs you to discover the agent land, a new land where you can find more information about agents, what they are, how they work, how they could be useful for you.
Textual mode
Table 2. 1 Some recent chatbots
2 http://www.mlab.uiah.fi/mummi/sanelma/ 3 http://www.loebner.net/Prizef/weintraub-bio.html 4 www.speak2me.net
2.5 Chatbot communication language
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2.6 Chatbots and pattern-matching techniques
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2.6.1 Information retrieval
3253 5#93 %2&'3 6663 5#3 93 763 7753 3 6875573633636753536753())3*632&36663633#7 6873932356393733376336"636333 633#733565936873 338663333593 63 5336"6333 53 3 63 3 763 3 3 673!3 2&3 663966336"63335639$5793635$6336"6363 3763775335936335993633763 3 #3 3 56373 3 3583 6388535336"63 3%&669933+#53(,,-'3&5#93876635636335$53 573 55763 3 763 3 3 !3 53 3 5$53 563 .3 5763 12345657833885335#3763339#3335763 23493 3 8853 3 9#3 763 3 3 5#/3%0 563 33 163(,,2'33 33 2535#9366636736339133367356383 3 3 75343 3 3 3 161#623 563 3 $893 3 2&3 6663 5736636$33657633963358335#3383 23$8763633638836563 63393$8763 63 3 7 http://www.google.co.uk53 3 63 8 753 75!3 63 53 "#2$13 %53 5&53 3!375363 56853'3 5'53 5(93 53 595337)3*353'33935633+659385793869556,3 '57593 3 93693 -3'3 95,3'59,3 3*3 6%6.353 )3*857932/366635&33799753'376,367363%386,3 3 3 763 3 753 -%63 '3 3 6603 583 !)3 53633655939,3&7833+76.3%353'7355(593 6763 '3 35(3 8695)313 363 '3 3 73 7(66,3 3 7376395-3367352397633-%63'3358333 '5633639(373'35635&376)323563567,33 73%5993 3 3 67,3 3 '3 3 7(653 73 -3 897)3*3 7356383(3'3357933,36335360333 636866)315599,353595663(953956333%3 6763633633'33!)3 3 395-35'535(93666,3373(65635353563656,3 %39335(368663653658938876)3*3535356363 3-56533676,3&753363655'573%63%57333 963'!3 63 37536603!3%53 63 655'573%63 3 5(36%6363%59933567663537834)33 3
2.6.2 Information extraction
*393'35'53&7532"366635633+89936875'5735'53 '3 93(963 '3 &3 3 63 563 5'53 53 673 66,3 %3663 793!57-93!,3,33%5639535).3*93 4)33 3 239,332"36635637863'365&386636367533%533 #36778)3*3'5635633'59538766,3%57356339(3 863'33&)31'338 ' 68739:;3535638895333%63 %553539:;,3)),3(,3,375(,37)3*35363563387663'3 6573 53%573 756633 869353 3-63 3%53 6573 5'5)3 23 3'3 63 3 9&5793 863 56363 3 6%3 3 956583%33869396)3'73695356388953533 '5'3 63 3 7563 35'3 '53 &86656)3<5993 3 &73 5'53 563'3 3 653 3 83 89)3 23 3 3 653 3875653379936353236663533596338333 9!53 "3 2#3 6663 563 63 3 785$3 3 !573 $3 8963 53 63 8!73 3 !63"3 3 63 %6&3'75653 563 3 87$3"33"36963"599377933393!3"369633663 "599337993563387$3"369633663"!37793336963 53363&3 3 2#366633!63535""35636!7363"55$3363"39938893 3897635337!35 5$35"53!3889(63$63"55$3993 863 "3 !63 "3 3 6883 3 75$3 5793 853 76&3 45""3 95$3 9$563 3 88953 3 93"3 863 3 579!3 3 )!53355378&3*3666383339 93"336)!73"3 6+363!6367573 965636!7363 55"5$3 3$!8633 !3 $!8633863363533"36!,7- -,73956&31369753 "32#3963563!9539.3 33 • /3013305!93673166756315$332333563 36"393393533"3!634!6566345"5$3443 65&3/335%763 3"563 673 3 93"3443 579633863395 337873)!55 36!3"39933 63 3 3 6753 53 3 96315$3 3 23 3&3/3 %753876635637863"336$6.33"56356335653 39%579387665$35735 9 63 !65$336$538-"-6873 $$5$3 3 -663 $$5$&3/3 673 6$3 563 67573 865$3 573 9663 3 $5793 6!7!3 "3 3 673 3 63 3 %65 3!63 "3 3-73 9%5793 6573 3 3 3 69 3 3 5$!53 89&3/3 963 6$3 563 53 96563 573 5 9 63 69 5$3 7"73 5$!53 !65$3 53 5"5&3 7"735$!538935633"5$333635353 6 93 5""3 63 6875993 53 95$3 53 63 &$&3 755$3 3358346356336386363358&46&3 3 • /3/%739 93 3 9&33 563 3 4!73,3 5663 2#3 66&34 9863833%379665"575388733%73693 "59963 !3 "3 3 %&3 /3 6573 53 563 3 993 3 69573 8653"335563359933%73637$56333
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2.6.3 Question answering systems
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2.6.4 Summary of pattern-matching techniques
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%4&543369953*7889,353331234565735363353 3 5953 3 563 65'3 93763 3 656'3 583 75536059963387575'335633093533753 865 336333:36373056339535633 7(56553355353393305385756333 ;3 *%4&543 3699537889, 33 63 7966553 753 953 75(635<3 • .856395'357357963553393385733833 33583653363389358=83856 3 • 965'3573 57963853 3 63 353 8963*53 3 667539995,353937966'3563563963799368563 95 3
• 573 953573 57963 653 3 9393 3 953 3 3 7563 53 673 33 3 53 3 93 3 3 63 375395367639363563563 6336875937633 685639538533555939!3 3 " #$3%&'''(37966553375395353353363996)33 • *76573753 953 88763573 6753 3 933 3 63 3 85956573 3 3 8653 3 85956573 87563335963!!3353"3"93 %""(!3 • *9573 753 953 88763 573 5793 953 75#63 3 3 363 859556!3+,8963 3 63 88763 )312345467892253573336856333633353 3 7873 5793 673123454678 4595357333956633 7753 963 3272943864958573 363 3 533 75633675736733378!3 3 "7539535635936353786-63785939556576363 3,893%1993.//03&''13&''2(!3468533733637633 3335953393373953887363535636563 3 593 58383 963 563 65593 3 3 563 3 6347597325218 2747826521827478352521827478887!3237633 75653 63 3 79653573 3 3 593 393 86533 573 56363 53 796655753%85753 3 6(3 3 567-63 9535633336856357539535738639933553 353563336635338573%79665(33633335833 6533 ,893 38 722598 72463 95!3 23 3 567-63 953 8873 3 3 563 863 3 63 3 56763 53 6875573 56!343 ,893 353%553 343.///(3 935695663 3 363393373386633567333796363 356!366356336353375387663379665333 567!33 3 23 3 8873 3 7863 563 7653 63 3 5593 !363 ,73 583839633378633995338895)3
• 73738535883856356376536335673 • 3563333 6365!5573963"363#73 37373376536356337966533633 33583 • 453863573$336 35833!835335673 533 395633863 3 563 35993367535359353783%356375"38$5633 33!5$5!3363636333 6365!55733 75!3 3
2.8 ALICE chatbot system
1&231557593&5!56573 23 835123'9973())*3 563 3 73 66 3 3 !!63 3 3 53 59!365!3 12+&31557593 2995!3+,-83&!!33$6533.+&3386335883 9633395!359!336573563312+&3/763312345678493 73 7!3 63 3233473573 763 3 583 3 66753342343 5735 8956331&2368633 3 65 895753 3 12+&3 ,63 53 633 -8! 63 6875993 63 393,3+&33!36355!37636331&203353 65 895563 3 6 9563 3 573 3 #653 3 76313 5563!93312+&35633533 33889373533763 735335"333#856331&23!5379395993 !-63 3!3533283 57993 55!3895737!563333 3 673 73 3 3 1&23 73 393 $5993 3 3 1&23 5365353337!3633483675353763 639933753332833
2.8.1 Overview of AIML
12+&3467563 3 79663 3 3 /763 7993 12+&3 /763 3 85993 67563 3 $53 3 7 83 8! 63 3 87663 53'9973 ())*337312+&3593756333/7633663533<aiml>3!3 10 http://www.alicebot.org/57386633123653123763338335637993 1234563 3 7563 3 3 3 8593 63 7993 <topic>3 3 <that>33 3<topic>386336337563!33<that>33 9633739636863 39931233563635353"#3 3 3 123 83 93 563 6589!3 765653 93 36!3 6876!3 3 3 5973 6963$3 3%3 3633 76563 3 9388763 963 3 963136593687356363368336!33359737763 753 95&363 35636563 3 123 993 93 3593 73 773 83 83 3 3 123#'#3 63 85639589359763 53 73833 3 389356331236863323563658963!338937656633 93895!3&3(3 389339637531236357373 63 !3 753 3 86!353 75593 6866!3 3 76593 7993 3 8373 3 563 3 68663 3 3 7563 3)3 (89!3 3 <javascript>3 3 9963 53 675853 5653 3 8963 3 3 *aiml version=”1.0”> <topic name=”the topic”> <category> <pattern>USER INPUT</pattern> <that>THAT</that> <template>Chatbot answer</template> </category> .. </topic> </aiml>
2.8.2 Types of categories
3338633756357393376537563
123456768293597363 538633333 59763 33
<category><pattern>10 DOLLARS</pattern>
<template>Wow, that is cheap!</template></category> 3 23 563 763 53 363 583 563!"3 996#3 3 3 6863 5993 3 !$ 3356378%#3 3 & 982768293593363 5386363 5976363863 6933 3 753 87663 593 3 73 563 67533 3 88853733 59763733583333553 53539857933 3 <category><pattern>5 *</pattern> <template>It is five</template></category> 3 23363583563!'3996#3356373563393733 36863563!235635#3 3 ( 96597 68293597 7 88563 3 8963 3 863 3 893799633837376593653<srai> or <sr>3 833
2.8.3 The recursive categories
)3 88957563 793 3 753653 3 7653 63 573 73 563 5333733!12#363557593599573 3!*+#3 3 3 !6596,686#3 !67573 5#3 !69573 75#3 !65893 765#3 3!63 695#3-657993 3 3 3 53 759556333<srai>33<sr>3635312).33
885366358353335333 • 58953393 573333!633933 673 53 !573 63 3 3 3 789"3 3 765#3 87663 863 673 5863 3 3 65893 63 83$3 "893%433&!3!3 763563'(356388353%)3 563 76(3 • 3493!53663199358633#3363533 363686338833363833%5(3%53 *(33%5(338833%99(3 • 73 689953 3 3 3 %3 3 (3 563 3 73 56&3!5735638833%3(3 • 473 &!63 23 73 63 863 3 773 !53 85793&!3966356385"63365"6363!#3 563&!3 563 73 53 3 673 3 63 6!3!5993 32356376353756333393!53 3&!3865533533&!63563%(333 99!53 583 863 3 3 3 93 3 593 9753%+3 3,-./30(33 93 3563%,-./30(3 3 93 3 963 %+(3 3 53 53 563 93 3 !33 %,-./(3 1 255533&3 !3663 583 53 3 33863 33 753 53 68663 7&3 3 3 3 673 663!53 %36(3 3%4(3!5993 3 3 63 3 !3 683 6763 3563 563 %36(333673563365"333673 3 $53113599663531253689635333631253 3 53 152.3 3 633 3#6563 #983 53 563 6563 3 86363338#5636333765#37563
3
Sample (a) shows the use of <srai> to simplify the language, obtained from the Afrikaans version developed in this thesis.
<category>3
3<pattern>WAT IS JOU NAAM</pattern>33
3<template>My naam is Karike. Wat is jou naam?</template>33 </category>1
<category>
<pattern>HET JY N NAAM</pattern>
<template><srai>WAT IS JOU NAAM?</srai></template> </category>
<category>
<pattern>HOE HEET JY</pattern>
<template><srai>WAT IS JOU NAAM?</srai></template> </category> 1234567897552676742727926737672277567 <category> <pattern>YES *</pattern> <template><srai>YES</srai><sr/></template> </category>
Whatever input matched this pattern, the portion bound to the wildcard * may be inserted into the reply with the mark-up <sr/>, which is equivalent to <srai></star></srai>
Figure 2. 2 Samples of AIML categories, illustrate the usage of the recursive categories.
2.8.4 Preparation for pattern-matching in ALICE
3 653 3 8753 8763 73 583 3 3 123 5836386633387666 3 !" #3956538766"3 $" %7535838337367"3 3 12345678996547634 #3 95653 87663 5&9&63 3686 3 66553673 689553 3835539565363635393$"$"33 !" '65539565356335657388953335833863 3535535335833935633963533 673 689553 3 83553 9565"3 23 73 565563 3 3 53 53 53 563 63 63 3 &553 3 3 6763 3(63 3 85)33)653338973533563888535"3 $" '7368955395653563333753373 583 563 8553 53 6763 653 65893 963 95*3 *53 676338563+653*633)7953*6"3 ," %3553956535&9&6336*6 3 • -&53 87533 583 3 *3 53 7859353 68737&65"3 • &53 3 583 963 3 883 763 3 75953 3 75353312386"3 33 4853335836655336895533333889363 835539636"333 Input Substitution normalised form sentence-splitting normalised form pattern-fitting normalised form "What time is it?" "What time is it?" "What time is it" "WHAT TIME IS IT" "Quickly, go to
http://alicebot.org!" "Quickly, go to http://alicebot dot org!"
"Quickly, go to
http://alicebot dot org" "QUICKLY GO TO HTTP ALICEBOT DOT ORG"
":-) That's funny." "That is funny." "That is funny" "THAT IS FUNNY" "I don't know. Do
you, or will you, have a robots.txt file?"
"I do not know. Do you, or will you, have a robots dot txt file?"
"I do not know" "Do you, or will you, have a robots dot txt file"
"I DO NOT KNOW" "DO YOU OR WILL YOU HAVE A ROBOTS DOT TXT FILE"
Table 2. 2 The normalisation process in ALICE pattern-matching processing 3 123456789785326886 93356338766338753358383336733583 6339953 3283<that>39<topic>3!93358383 56378633396 33 " 39563583
<that> tag which 96338563736#39563533 63363583 93$3856336353%5663 &963 93$3'3 <Topic>3 3573 9963 3 583 3 83 68663 953 5338573 !9 3$38573353%5663 &963 !93$3'3
Normalised
input Previous chatbot output (normalised)
Value of topic
predicate Input path
"YES" "DO YOU LIKE CHEESE"
"" "YES <that> DO YOU LIKE CHEESE <topic> *"
"MY NAME
IS NOEL" "I GUESS SO" "MUSHROOMS" "MY NAME IS NOEL <that> I GUESS SO <topic> MUSHROOMS" Table 2. 3 Producing the input path by the AIML interpreter.
2.8.5 The ALICE pattern-matching algorithm
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5335633569565333 3638!63"!3#9973 $3 %3 3 3 3 &8!63 63 3 3 63 3 3 863 85799'3 63 563 563 563!883 53 3 53 3 73 3 563 3 53 68953 %563 8763 3 53 3 (53 3 3 9963 3 5) 753%56335633*(8+35336859335353363,3673 3 %3 8)!753 87663 793 3 6753 53 !63 3 3593 66!3 (59399359633963356357579935333%359366!3 563 7!863 3 3 3 673 63*7-.+3(573 63 763 8635963 3 96337393536363563(3596336)963/3593633 *83 !+3 3 !563 563 !3 3 8655303 3 6!3!3 3 112/3583(26333!89334653*#35633!5+3793 3(5363*-6#1%6276809:6;1</+3(3-35633&8!633 3 166!3366=3583663(533(3*>+338)!75395!3 889536538)56367337237253754636399(6-3 ? *23 393 63 3 693 363(53673 333*@6+33 6733533!73993(636533>3533!73-3 &37233933353369363(53(3>3536333 *>6+36733!75335933>3,633!73233!73 -3 &372333933353369363(5363535363 33*A6+33993!55365363358399(53*>+3363533 !73233!73(6337357372333833563 93383*>+37233333358+3"#9973 $3 #33!735633387663686333!893396333 7356387663358337673383233!7333 !3 6(3(5993 33 673 63*73 23 3 3 5+3533 5996633!75387663 3 3 3 3
3 3 3 3 3 3 33 63563 8553 3 573373 63 67385533336396385533 38633333 985799393859933633376333333 3 33 13 !893 3 1"2#3 73 3 3 75$3 3 375$3 87663363535$%3&'335$%3&(36875933 3 3 <category>
<pattern>_ WHAT IS 2 AND 2</pattern>
<template><sr/> <srai>WHAT IS 2 AND 2</srai></template> </category> <category> <pattern>WHAT IS 2 *</pattern> <template><random> <li>Two.</li> <li>Four.</li> <li>Six.</li> </random></template> </category> <category> <pattern>HIYA</pattern> <template><srai>HELLO</srai></template> </category> <category> <pattern>HELLO</pattern> <template><random>
<li>Well hello there!</li> <li>Hi there!</li>
<li>Hi there. I was just wanting to talk.</li> </random></template>
</category>
Figure 2. 5 Tracing of ALICE matching process Applying step 1, match with
Normalisation Process
Hiya, what is 2 and 2?
HIYA WHAT IS 2 AND 2
_ WHAT IS 2 AND 2
<srai> WHAT IS 2 AND 2 </srai>
WHAT IS 2 AND *
WHAT IS 2 *
Two. Four. Six
No match. Go back one step. Try to find
No match. Back another step. Try to find
Match found, select an answer randomly from the list <sr/>
HIYA
HELLO Match with
Well hello there! Hi there!
Hi there. I just want to talk.
Hi there! Four. Atomic match, select an answer
2.9 Elizabeth system
95359957335633853332138 !"353#5733 $5%63 6975"3 6%65%5"3 3 863 6 3 !756!63 $3 3 733 95633576339&55953385938595'3 (#9 35636363367583533&359"3#37395356363#533 67583 7!!3 5'3)63 563 3 65 93 776"3 33 73 %9*83636#3535 %3'+'373675837!!36335&3733563 3%!5799'3237396335&3%65 33%636875937'3 3W: Welcome message Q: Quitting message
N: No match V: Void input
I: Input transformation / : Comment
K: Keyword pattern R: Keyword response
M: Memorise phrase N: No match
O: Output transformation &: Action to be perform Figure 2. 6 Elizabeth script command notations
2.9.1 Elizabeth’s script file format
1367583593!37533!63%386,3 -' .75837!!3956395 3#97!"3$5333/#3!66 6'33 ' 28%36!53%96"3#57363#5302133!86358%333 !3337!8593#53353/#6'33 2' 3%8%36!53%96"3#57363#53031337 638693 8%633388853633686'33 4' (#386"3#57363#530(1333/#38333 !7"399#3 3 3 6863 3 3051'3)3 3 #3 863 3/#3 86,3 65!893 86"3#573!73 93 3 65 93 #"3 3 7!8653 86"3#573!73 676"3 866"3#6"3 65 6"396"35 335 '333 3
2.9.2 Dynamic processing in Elizabeth
3 573 87663 589563 853 3 63 3 75635735563 3 675835933765356338663637563339953 3 & {script command}3 3 573 87663 7563 593 5 3 5653 3 953 763 63 876663 73 3 835793653!3333753876633 ! 153 3 67583 761363 3 3 3 67583 53 5363 3 "56338973533335353563933#3"89 3 I my sister => my sister & { K MOTHER
& { N DOES ANYTHING ELSE ABOUT YOUR MOTHER? } R HOW WELL YOUR MOTHER AND SISTER GET ON? }
3 3 3 7563 3 3 83#569 33753 7763 5335839 33663599367333$3%&'( 3 5356333686333(3599333336863 9563 3 3$3 23 3 3 3 3 3 793 7$635993 3 3 3 3 67583)79 3375353%&'(3 7763 93 3 3 3 3 3$336635993 3 3 3 3 9563 3 8973393353"5663 * %56537633633933633368633 375337356333%3993333 9933393#3"89 3 3 K [ ] MY NAME [phrase] & { M label [phrase]} R YOUR NAME [phrase]? 3 23 363 583 5633 3 563+3 3 3 3 635993 3 ,&-(3.1%'3 2)3+&.3 13 3 63 53 33 993 953 +35633 / 49537613631933675837336533 7$3696353#3"89 3
V\: deletes all current void messages.
2.9.3 Elizabeth’s pattern-matching algorithm
36533753876633753356333735833 753 63 583 3 93 763 5653 68763 3 63 3 8753353637763335837831232425262728292 992 2 2 2 99 3!53"#3 663 3 893 3$95%3 73 3 93"&3 599663 3 753 83 3 3753 8766 3'3753 87663 59633995353663 3 ( )753 583 653 963 3 73 3 8893 3 573 876663 32336393563889579333753333 (*356353677665335356388953+637 3 " )753,38633993,633889535363593 3733373563 3'3686356353775333 56373 3 53 3 563 3 9563 3 68663 3 3 3 563 6973 93335373356368635993373533 3 363 53 53 3 6373 7763 53 3 3 6665 3 23 3 563 3 5738766353599338895337333835363 5 32337339933333683 3 - .83653963633889533639536353 633337337533388376 33 & 233737763533,386335337533563 83 3 3 3 35366635993 3 6973 93 3 3 ,3333333,36663599335689 3 / 05335738766633853373756353 356 33
/ The Script begins with Welcome, Void, No-Keyword and Quit responses:
‘001’ W HELLO, I'M Elizabeth. WHAT WOULD YOU LIKE TO TALK ABOUT? ‘001’ V CAN'T YOU THINK OF ANYTHING TO SAY?
‘002’ V ARE YOU ALWAYS CHIE ?
‘001’ N TELL ME WHAT YOU LIKE DOING. ‘001’ Q GOODBYE! DO COME BACK SOON. / Next come the Input transformations: ‘001’ I mum => mother
‘002’ I dad => father
/ Then the Output transformations: ‘001’ O i am => YOU ARE ‘002’ O you are => I AM ‘003’ O my => YOUR ‘004’ O your => MY ‘005’ O me => YOU ‘006’ O I IS => I AM ‘007’ O YOU IS => YOU ARE
/ And four groups of Keyword transformations: ‘001’ K I THINK [phrase]
‘001’ R WHY DO YOU THINK [phrase]? ‘002’ K MOTHER
‘003’ K FATHER
‘001’ R TELL ME MORE ABOUT YOUR FAMILY.
‘002’ R WHAT DO YOU REMEMBER MOST ABOUT YOUR CHILDHOOD? ‘003’ R ARE YOU THE YOUNGEST IN YOUR FAMILY?
‘004’ K [phrase1] IS YOUNGER THAN [phrase2] ‘001’ R SO [phrase2] IS OLDER THAN [phrase1]. ‘005’ K I LIKE [string]ING
‘001’ R HAVE YOU [string]ED AT ALL RECENTLY? ‘006’ K [ ] my [phrase]
& {M [phrase]} ‘001’ R YOUR [phrase]?
3 User Input
Input Transforming
Keyword Patterns Output Transforming Respond Actions Dad loves Mum ‘001’ mum=>mother ‘002’ Dad => father ‘001’ K MOTHER ‘001’ R - TELL ME MORE ABOUT YOUR FAMILY. - My sister is a teacher. - ‘006’ K my [phrase] [phrase] => sister is a teacher ‘001’ R - YOUR SISTER IS A TEACHER? ‘fam’ M sister is a teacher My brother is younger than me. - ‘004’ K [phrs1] IS YOUNGER THAN [phrs2] [phrs1] => my brother [phrs2] => me ‘001’ R [phrs1] ‘003’ my => YOUR [phrs2] ‘005’ me => YOU SO YOU IS OLDER THAN YOUR BROTHER. - I like reading - ‘005’ K I LIKE [string]ING [string] => read ‘001’ R - HAVE YOU READED AT ALL RECENTLY? -
Table 2. 4 Tracing an Elizabeth output
2.9.4 Implementing grammatical rules in Elizabeth
95363359533873336739656333673 653 3 63 3 583 653 963 3 8633 963563 85633575336333377863375 63393 938766536553399!53"33 S => NP VP NP => D N VP => V NP 3 56373386333583963393!5368755736#3633 563636!35353$%3
I a => (A d) I the => (THE d) I cat => (CAT n) I dog => (DOG n) I likes => (LIKES v)
I ([brak1] d) ([brak2] n) => (([brak1] D) ([brak2] N) np) I ([brak1] v) ([brak2] np) => (([brak1] V) ([brak2] NP) vp) I ([brak1] np) ([brak2] vp) => (([brak1] NP) ([brak2] VP) s) K [any?]
R [any?]
W TYPE A SENTENCE USING: A, THE, CAT, DOG, RABBIT, BITES, CHASES, LIKES
Figure 2. 8 Grammar representation rules in Elizabeth 3 5636758366333973663 953 3 13!""3#!2"$3 3 133 13 4%$3&1''23 '2!31!!3(2)!3 #61337395*6333 9533+++34,3+13",3" ,3++(2)!3-,3++34,3+4%$3",3" ,3- ,36,3
2.10 A comparison between ALICE and Elizabeth
.338/5063136756339915377906563133 3 • 1(230633658938389338635803308033 9630653 65893 82753 9533639530663 58030963*1386330803096333368633 • 37065/375403063531(235637653633*3033 36633235630633658955335803379953737563 7065/933 1/3 3 03 3 63 0963 539533 7063 779533553157356369/3388953309393735353563 88957933375/35333 • 23 1(23 3 563 3 5953 3 753 13 6163 53 3 763 3 689553 0533 95653 87663 338555370633 37065/38766335638/563313385533673313 676337535360963035635633/5993539533 • 36365557303531(23563382753953 157356363386338256367335639535633
53 3 963 83 73 593 953 563 3 6863 77533356338373 • 3 6663 73 73 8693 8!6"3 3 563 5633 789573 53953 !3 3 763 665553#5633 7!63 63 353!6!5936633 • 366636!883565395$6357387663573 996337563377!359337653563!38663563 765333!3573633%563531&23 • 2336358!3863!5337653953563353 536633!653533697536866333 9563 1&23 563 63 3 3 69753573353 !89573 68663 • 953 63 3 5953 353 3 553 6!7!33 3 673 !6533579396563573563385331&23 • 953 57863 96563 9633 993 6863 53753 9853 3!633!633663333 '993 1&2363 65893 3 3 6!533 !573 553 87663 886353563673573563567!66353783(3 3
2.11 Evaluation methodologies
)75793 88957563 3 9!53 33 566!63 53 9!3 553 !53*+,,,-37756639!355353633./5637!63 3906793875793663331!5539!53386633563 5995663373356333751!6233 3 33 4&)3 6663 73 3 9!3 3 7853 !8!3 563 3.93 6238733!8!3*3!6331993+,,(35363393 788(39953393788(-3!3563887363333575359!3 66633!8!38633!6358!335753 3 #3 &3 )53 *&3 7889-3 78553 63 3 !63 3 9!3 753 7653 763 #3 &3 )53 563