INFORMATION RETRIEVAL IN THE SERVICE

OF GENERATING NARRATIVE EXPLANATION

What we Want from GALLURA

Ephraim Nissan

and Yaakov HaCohen-Kerner

Dept. of Computing, Goldsmiths’ College, Univ. of London, 25–27 St. James, New Cross, SE14 6NW, London, U.K.

Department of Computer Science, Jerusalem College of Technology (Machon Lev), P.O.B. 16031, Jerusalem, Israel

Keywords: Information extraction, Explanation generation, Story generation.

Abstract: Information retrieval (IR) and, all the more so, knowledge discovery (KD), do not exist in isolation: it is

necessary to consider the architectural context in which they are invoked in order to fulfil given kinds of

tasks. This paper discusses a retrieval-intensive context of use, whose intended output is the generation of

narrative explanations in a non-bona-fide, entertainment mode subject to heavy intertextuality and strictly

constrained by culture-bound poetic conventions. The GALLURA project, now in the design phase, has a

multiagent architecture whose modules thoroughly require IR in order to solve specialist subtasks. By their

very nature, such subtasks are best subserved by efficient IR as well as mining capabilities within large

textual corpora, or networks of signifiers and lexical concepts, as well as databases of narrative themes,

motifs and tale types. The state of the art in AI, NLP, story-generation, computational humour, along with

IR and KD, as well as the lessons of the DARSHAN project in a domain closely related to GALLURA’s,

make the latter’s goals feasible in principle.

1 CONCEPTUAL & TECHNICAL

BACKGROUND

In the history of full-text IR, tools for retrieval from

very large historical corpora in Hebrew and Aramaic

were prominent, with the RESPONSA project (see

e.g. Choueka, 1989a, 1989b; Choueka et al. 1971,

1987).

Before the rise of Web search engines,

RESPONSA tools were the ones which achieved the

more far-reaching effects on society, because how

they empowered the retrieval of legal precedents in

rabbinic jurisprudence, thus affecting especially

legal practice of family law in Israel (as for family

law, in the Ottoman successor states, the usual

jurisdiction is the courts of the various religious

communities).

Religious cultures, as being the “consumers” of

religious texts, were, in a sense, the customers of a

considerable portion of early projects in IR: apart

from RESPONSA, whose corpora comprise the

Jewish texts from the sacred sphere through the

ages, this was also the case of Padre Busa’s Index

Thomisticus in Milan, and of the humanities

computing at the Abbey of Maredsous, in Belgium.

Exegesis (such as biblical interpretations) and

homiletics involve layers of texts, where a secondary

text refers to and either just quotes, or discusses,

some locus in the primary text; or then (as in the

Jewish aggadic midrash) expands on a biblical

narratives, filling the gaps where the primary text is

silent. Collections of aggadic midrash from late

antiquity (e.g., the Midrash Rabbah) or the Middle

Ages (e.g., Yalqut Shim‘oni) are a digest of a

multitude of homilies on biblical fragments of texts,

developing several often alternative ideas and

subnarratives. Cf. Hirshman (2006), Braude (1982),

Fishbane (1993), Hartman and Budick (1986).

* HyperJoseph is a hypertextual tool on the story

of Joseph in Genesis, with the secondary texts

elaborating on it (Nissan and Weiss, 1994).

* DARSHAN is a tool that invents homilies in

Hebrew (HaCohen-Kerner et al. 2007).

Retrieval in DARSHAN is intensive, and so is

the use of networks of lexical concepts.

DARSHAN generates ranked sets of either one-

sentence or one-paragraph homilies. While

producing its output, DARSHAN is able to quote

487

Nissan E. and HaCohen-Kerner Y..

INFORMATION RETRIEVAL IN THE SERVICE OF GENERATING NARRATIVE EXPLANATION - What we Want from GALLURA.

DOI: 10.5220/0003688304790484

In Proceedings of the International Conference on Knowledge Discovery and Information Retrieval (KDIR-2011), pages 479-484

ISBN: 978-989-8425-79-9

 2011 SCITEPRESS (Science and Technology Publications, Lda.)

from Scripture, to search for an occurrence

elsewhere in the textual canon, to replace words or

letters, to resort to puns, to interpret a word as an

acronym, and so forth. Use is made of patterns

which consist of canned text with places where to

plug in strings obtained through IR and

manipulation. The user supplies as input a biblical

verse, or a sentence, or a set of words, and also

specifies which devices should be applied. Filters

applied to the candidate output are alert, e.g., to

positive vs. negative connotation.

The quality of an individual output homily is

assessed as a sum of weighted factors, including:

length (as an indicator of complicacy); the

percentage of relevant words in the homily, out of

the total of words in the homily how many sentences

there are; how complex it was to insert every motif

into the homily generated; how many motifs were

actualized in the output homily being evaluated; how

many transformations were carried out; how many

words were replaced in the homily.

Having mentioned acronyms, consider that

HaCohen-Kerner et al. (2010b) discussed an

abbreviation disambiguation system for rabbinic

texts in Hebrew or Aramaic. Cf. Stock and

Strapparava (2005) on the HAHA project, whose

purpose is the humorous interpretation of acronyms.

As to connotations, Strapparava and Valitutti (2004)

described an affective extension of WordNet.

2 FUNCTIONS IN GALLURA

The GALLURA project seeks to develop software

that would interpret in Hebrew names by folk-

etymology, but in the context of a generated

narrative (aetiological tales, usually brief or even

very brief). The most closely studied model is a

large textual corpus of playfully creative writing that

embodies midrashic literary devices, by explaining

fancifully place-names of names for animal kinds.

The GALLURA project, now in the design

phase, requires, among the other things, capabilities

of story-generation, and of generating a playful

explanation. By themselves, these two tasks draw

upon three areas in AI:

 explanation synthesis (for which, see e.g.

Schank, 1986,

1994; Walton, 2004),

 story-generation (see e.g. Liu and Singh,

2002; Lönneker et al., 2005; and a long survey

in Nissan, 2011a: Ch. 5), and

 computational humour (see e.g. Stock et al.,

2002; Ritchie, 2004; Waller et al., 2009).

Humour studies are interdisciplinary.

Moreover, GALLURA needs skills from

computational linguistics, including some that thus

far were modelled by linguistics, but not

computationally:

 folk-etymology (see e.g. Kirwin, 1985; Coates,

1994; Baldinger, 1973; Zuckermann, 2006),

and

 phono-semantic matching (PSM), a discussion

of which is found in Zuckermann (2000, 2006).

For example, one of several PSM rules as

occurring in neologisation by adapting a foreign

term (Zuckermann 2000) is as follows (where SL is

the source language. TL is the target language):

SL y ‘b’ Æ TL

(+PSM)

x ‘b’ Å TL x ‘a’

x is phonetically similar to y; a is similar to b

That is to say, the PSM introduced a new sense:

this was a PSM produced by shifting the meaning of

a pre-existent word in the target-language (TL).

Another rule of camouflaged borrowing (ibid.) is:

y ‘b’ÆTL

(+PSM)

{x}+{z} ‘b’ÅTL {x} ‘a’, {z}

x is a lexical morpheme (e.g. root) that is

phonetically similar to y;

z is a grammatical morpheme (e.g. noun-pattern);

{x}+{z} is one word; a is similar to b

GALLURA should also have quality evaluation

capabilities, e.g., evaluating a story generated

(Peinado and Gervás, 2006), or evaluating morality

within a story (Reeves, 1991). We also need to

resort to computational argumentation: some such

current research into argumentation in computer

science looks into legal narratives (Bex, 2011).

Explanation as sought in GALLURA need not

necessarily be realistic; it is non-bona-fide (like in

humour), and must conform to a set of conventions,

of which realism is just a particular case (cf. Nissan,

2008). There are constraints on style: the output text

generated conforms to the early rabbinic linguistic

stratum and style (thus emulating the aggadic

midrash), with constraints on which lexical items or

morphological forms can be selected.

Rabbinic stylemes are the subject of current IR

research,

including in the CUISINE text classifier.

So are the identification of rabbinic citations, and

chronological classification based on them. In fact,

HaCohen-Kerner et al. (2010a) discussed stylistic

feature sets for classification in CUISINE.

Automated identification of citations from rabbinic

texts has been researched (HaCohen-Kerner et al.,

2010c). Automated classification of rabbinic

KDIR 2011 - International Conference on Knowledge Discovery and Information Retrieval

488

Coalition2

Inflection

ent

PSM agent

Word-

Formation

ent

con

(

with

emons

Triggered upon Access)

Coalition3

Quotation Agent

(Accesses

RESPONSA

Corpora & Tools)

Coalition4

Pool of

Stock

Characters

Motifs &

Tale-type

Agent

Emplotment a

ent

(

makin

plots

)

Thematic

ent

lemic

Agent

ntax

Agent

responsa by period based on what they cite or are

cited by, was attempted successfully: HaCohen-

Kerner and Mughaz (2010) defined and effectively

applied “various kinds of ‘iron-clad’, heuristic and

greedy constraints defining the birth and death years

of an author based on citations referring to him or

mentioned by him.”

3 THE MULTIAGENT

ARCHITECTURE

Several capabilities are required of GALLURA, and

many of them require retrieval. Fig. 1 shows

Coalition1 of agents, i.e. agents that often interact

among themselves. The control sequence is

opportunistic, according to the needs of the various

agents while they tackle a (sub) problem during a

particular run. They broadcast their need for help to

the other agents, and contract out the task. Some

agents however interact in a privileged manner with

one or more other gents, as they for a “coalition”.

Both the syntax agent and the stylemic agent

have to emulate early rabbinic language, but the pool

of stylemes and more abstract modes comprising

stylemes need actually be wider. Fig. 2 shows the

interplay of other coalitions of agents. In the

Lexicon, expected associations or behaviour are

triggered through demons, procedural code activated

upon access to individual lexical entries.

Coalition5 comprises an Encyclopedic agent, and

a Commonsense agent. The latter comprises two

modules: Concept-centred commonsense, and

Situational commonsense. Both the Emplotment

agent, and the Tex-generation agent closely interact

with the Argumentation agent.

4 A SIMPLE EXAMPLE:

AQUA & GENESIS 1:9

It is usually proper nouns that are playfully

etymologised in the modern, archaising Hebrew

narrative corpus which is the main model for

GALLURA, and whose own model is the already

mentioned early rabbinic genre of the aggadic

midrash. Nevertheless, sometimes common nouns

are folk-etymologised as well, and most often these

are non-Hebrew words.

Here is a concise example. The input is Latin

aqua ‘water’. In the model corpus, there is this item:

Ma ra’ú Bnei Rómi, še-hém qorín et ha-

máyim ’aqwa (aqua)? Le-fí še-katúv:

“yiqqawú ha-máyim”.

Here is a translation of this Hebrew text:

Why [literally: what did they see], the

Romans [lit.: The Sons of Rome], that they

call water aqua? Because [lit.: to mouth of] it

is written [in Scripture]: “Let the water be

gathered”.

Figure 1: Coalition1 of agents.

Figure 2: The interplay of coalitions of agents.

In fact, the intertextual reference is to Genesis

1:9. The verbal form yiqqawú (passive future, 3rd

Coalition5

Text-generation

Agent

INFORMATION RETRIEVAL IN THE SERVICE OF GENERATING NARRATIVE EXPLANATION - What we Want

from GALLURA

489

person plural) is from the root qwh. Corradicals one

can find in the Hebrew Bible include the verb and

noun for fluids gathering, for hoping and hope (the

word for ‘hope’ also has the little known sense

‘string’), and the noun now used for ‘line’.

Etymologically unrelated, Qwe also occurs, being

the name of a horse-trading land in Anatolia with

whom and with Egypt King Solomon traded in such

animals. Finding the apparent corradicals is trivial,

using the IR and NLP tools of the RESPONSA

project. What does require AI instead is for software

to be able to notice that Genesis 1:9, because it is

about water (and during an act of creation), is

splendidly apt an occurrence of the input aqua,

which PSM spuriously proposes as a derivative of

the root qwh (Semitic roots are “triliteral”).

There are features of the example considered,

pertaining to the lexicon, morphology, and style,

which clearly belong to the Mishnaic (i.e., early

rabbinic) historical stratum of Hebrew. Beginning

with a question, and in particular with one of the

many ways of asking ‘Why’ in Hebrew (i.e., lit.

“What did they see?”), which involve Coalition1 and

Coalition2, the Lexicon.

Asking and answering here also involves some

rather rudimentary involvement of argumentation. A

shortcut would be to use a canned-text encoding of a

pattern, in the manner of DARSHAN. Actually

however there is some sophistication in the example

considered, because we are not abstractly taking

about Latin; rather, the expression is made concrete,

with the Sons of Rome being invoked from the Pool

of stock characters. This dovetails with the

underscoring of their agency, when the option

selected for saying ‘Why’ is “What did they see?”

5 A COMPLEX EXAMPLE:

BABEL TO LAOS

The following would be a much more difficult

example for GALLURA to replicate, and both

retrieval and manipulation would be intensive and

laborious. In the model corpus we use, place-names

around the world are explained by both playful

etymology, and fantasy history narratives. It is often

the case that a story is told about one of the human

groups leaving the Tower of Babel. The Generation

of the Division (Dór ha-Pallagá) or the Ones

Leaving the Tower (Yots’ei ha-Migdal) would be

often resorted to in GALLURA’s Pool of stock

characters. Let us consider a story on Laos.

“Teach us, Sir” (yelammédenu Mar, a cliche

especially associated with the lost rabbinic Midrash

Yelammedenu), “What did the Nations see” (i.e.,

‘why’: má ra’ú ha-’ummót), “that they call” (še-

qorín: a Mishnaic verbal inflection) “one of them

Laos” (achát mehén Lá’os). “I shall answer you

immediately!” (Af aní mešivkhem mi-yád! a cliché).

A ready pattern of argumentation: “Instead of [lit.:

Until] you asking why that nation is called Laos”

(‘Ád še-attém šo’alín lámma otáh ’ummá qruyá

Lá’os), “be asking what did the Sons of Greece see”

(hevú šo’alín ma ra’ú Bnei Yaván), “that all

populations” (še-kól ’okhlosín, itself a Green

loanword in Hebrew) “were called in their mouths

[i.e., by them] λαός” (niqre’ú be-fihém lá’os). “Once

the Ones Leaving the Tower went out of Babel”

(Keván še-yats’ú Yots’éi ha-Migdál mi-Bavél), “they

were tired (le’ín) and exhausted on the road” (hayú

le’ín u-me‘uyyafín ba-dárekh).

Sustained walking is tiresome, and one term for

‘tired’ is related by PSM to Laos. Now, consider that

in a crowd (a spawned demon would inform

GALLURA), you would expect somebody trying to

sell snacks and drinks, unless circumstances exclude

this (e.g., if it’s a day of fast, or a famine causes

starvation). Such a situational cliché is funny if it

does not quite match the situation at hand. The

theme of the exodus from Babel, in the model

corpus, often has a wise old man advise the crowd,

but some other time, some individual takes

advantage, being cunning rather than altruistic.

“The more astute among them” (‘Armumiyyín se-

bahém), “who were traders and vendors of edibles”

(še-hayú ba‘aléi praqmátya [a typical early rabbinic

term] u-mokhréi mezonót), “this way they were

speaking to them” (kákha hayú ’omrím lahém): “Let

the legs be strong!” (Techezáqna ha-ragláyim!). The

latter contains a Biblical Hebrew verbal form, the

3rd person plural feminine (as ‘legs’ are feminine in

Hebrew), whereas Mishnaic Hebrew discarded that

form, using the masculine. As this is a modified

quotation, using a Biblical Hebrew morphological

(or lexical) form is legitimate for GALLURA. “Let

the legs be strong!” (Techezáqna ha-ragláyim!) is a

modification of “Let the hands be strong!”

(Techezáqna ha-yadáyim!), the title of a famous

labour song by Bialik. Such a temporal flashforward

for a story set at the times of the Tower of Babel is a

funny transgression (rather than an insipient

inconsistency).

“Whatever you shall put under your teeth, you

shall find in your legs!” (Má še-tittnú táchat

šinneikhém, timtse’ú be-ragleikhúm!). This is a

Hebrew adaptation of an Aramaic early rabbinic

proverb. “Be chewing” (hevú lo‘asín, associated by

PSM with Laos), “as for this you were created!” (še-

KDIR 2011 - International Conference on Knowledge Discovery and Information Retrieval

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la-zé notsártem! This is evocative of le-khakh

notsarta, “for that purpose [of studying] thou hast

been created”, in Maxims of the Fathers, 2:8).

“Every population” (lit.: population population,

’okhlosín ’okhlosín), “all of them are chewers!”

(kullam la‘osot!). “As they were hearing them

saying so” (Keván še-hayú som‘ín ’otám ‘omrím

ken, partly a quotation of how the crowd in the

Temple used to respond to a given utterance of the

High Priest on the Day of Atonement, a day of fast),

“their saliva flowed, they paid the price, would take

and eat” (záv hayá rirám, notnín mamón, notlín ve-

‘okhlín, with typical Mishnaic wording).

Clearly, obtaining from GALLURA output such

as this story from our model literary corpus would

be as ambitious a goal as it can get. Anything in the

middle would be nice to achieve. See Nissan’s

(2011b) 150-page discussion of playful narrative

explanations.

6 WHENCE AND WITHER? VON

IN GERMAN ONOMASTICS,

AND ELDAD & MEDAD

It is important to realise that knowledge discovery or

information extraction as involved in accessing the

historical textual canon as well as ontologies and

representations of commonsense or encyclopedic

knowledge, can be easier in one direction, while

very difficult in the other. We exemplify this with an

item from our model corpus. Edom (medieval for

‘Europeans’) in the Land of Ashkenaz (medieval for

‘Germany’) the text relates, for many generations

were eager to insert fon (i.e., von) before their family

names, as it would signal their patrician ancestry.

“What did cause that? The episode of Eldad and

Medad caused that”, at Numbers 11:26–29. Moses

appointed seventy elders, but those two did not

come, and prophesised nevertheless. Joshua tells

Moses to put them under arrest, but Moses retorts:

“Are you jealous on my behalf? If only” all the

people were prophets.

U-mí yittén (lit.: “And who would give”) was

rendered, in the canonical Jewish Aramaic

translation (the Targum by Onqelos) as: Ra‘ena fon,

i.e., “I wish fon”, where fon (a grammaticalised

denominal conjunction) means any of ‘face’, ‘turn’,

‘that would’, or ‘lest’. If you were reading Onqelos,

you may happen to notice this locus serendipitously.

But had you begun with eagerness for ennoblement,

it would be very difficult to devise an appropriate

search that would retrieve a biblical “I want fon”.

7 CONCLUDING REMARKS

GALLURA is an ambitious project, now in the

design phase, requiring the interplay of various

agents or coalitions of agents specialised per domain

of expertise. Several of these agents have retrieval-

intensive requirements. GALLURA has to devise

playful etymologies with a backup story to go with.

It builds upon the experience and part of the

architectural features of DARSHAN — especially

how the pool of devices is organised, and the

approach to retrieval, which is mostly from the same

textual corpora. GALLURA is much more difficult

to achieve, but at the stage reached by a number of

domains within AI, NLP, IR, and KD, it is in

principle feasible. Any progress on any part of the

architecture would by itself be a valuable

achievement. A global advantage already at present,

in this project, is that thanks to manual analysis of

many items in the creative writing corpus which is

our main model, it is possible to model

algorithmically all devices required.

REFERENCES

Baldinger, K. 1973. À propos de l’influence de la langue

sur la pensée: Étymologie populaire et changement

sémantique parallèle. Revue de Linguistique Romane,

37, pp. 241–273.

Bex, F. 2011. Arguments, Stories and Criminal Evidence:

A Formal Hybrid Theory, Law and Philosophy Series,

vol. 92. Springer, Dordrecht.

Braude, W. G. 1982. Midrash as deep peshat. In: S. R.

Brunswick (ed.), Studies in Judaica, Karaitica and

Islamica (Presented to Leon Nemoy on his Eighties

Birthday). Bar-Ilan University Press, Ramat-Gan,

Israel, pp. 31–38 [English].

Choueka, Y. 1989a. RESPONSA: An operational full-

text retrieval system with linguistic components for

large corpora. In: E.I. Cuomo (ed.), Law in

Multicultural Societies, Proceedings of IALL, the

International Association of Law Libraries Meeting,

Jerusalem, 1985. The Hebrew University, Jerusalem,

1989, pp. 47–82.

Choueka, Y. 1989b. Responsa: A full-text retrieval system

with linguistic processing for a 65 million-word

corpus of Jewish heritage in Hebrew. In a Special

Issue on non-English Interfaces to Databases, IEEE

Data Engineering, 12(4), pp. 22–31.

Choueka, Y., Cohen, M., Dueck, J., Fraenkel, A.S., Slae,

M. 1971. Full-text Document Retrieval: Hebrew

Legal Texts (Report on the first phase of the Responsa

Retrieval Project). In: M. Minker, S. Rosenfeld (eds.),

Proceedings of the ACM Symposium on Information

Storage and Retrieval, Maryland, 1971. Association

for Computing Machinery, New York, 1971, 61–79.

INFORMATION RETRIEVAL IN THE SERVICE OF GENERATING NARRATIVE EXPLANATION - What we Want

from GALLURA

491

Choueka, Y., Fraenkel, A. S., Klein, S.T., Segal, E. 1987.

Improved techniques for processing queries in full-text

systems. In: C.T. Yu, C.J. van Rijsbergen (eds.),

Proceedings of the Tenth Annual International ACM-

SIGIR Conference on Research and Development in

Information Retrieval, New Orleans 1987. ACM, New

York, 1987, pp. 306–315.

Coates, R. 1994. Folk etymology. In: R.E. Asher (ed.),

The Encyclopedia of Language and Linguistics,

Pergamon Press, Oxford, Vol. 3, pp. 1267–1270.

Fishbane, M., ed. 1993. The Midrashic Imagination,

University of New York Press, New York.

HaCohen-Kerner, Y., Mughaz, D. 2010. Estimating the

birth and death years of authors of undated documents

using undated citations. Proceedings of the Seventh

International Conference on Natural Language

Processing (IceTAL 2010), August 16–18, 2010,

Reykjavik, Iceland (LNCS 6233), pp. 138–149.

Springer-Verlag, Berlin.

HaCohen-Kerner, Y., Avigezer, T.S.-T., Ivgi, H. 2007.

The Computerized Preacher: A prototype of an

automatic system that creates a short rabbinic homily

[Hebrew]. B.D.D. (Bekhol Derakhekha Daehu):

Journal of Torah and Scholarship (Bar-Ilan

University, Ramat-Gan) 18, pp. 23–46.

HaCohen-Kerner, Y., Beck, H., Yehudai, E., Rosenstein,

M., Mughaz, D. 2010a. Cuisine: Classification using

stylistic feature sets and/or name-based feature sets.

Journal of the American Society for Information

Science and Technology, 61(8), pp. 1644–1657.

HaCohen-Kerner, Y., Kass, A., Peretz, A. 2010b. A

Hebrew Aramaic abbreviation disambiguation system.

Journal of the American Society for Information

Science and Technology, 61(9), pp. 1923–1932.

HaCohen-Kerner, Y., Schweitzer, N., Shoham, Y. 2010c.

Automatic identification of biblical quotations in

Hebrew-Aramaic documents. Int. Conf. on Knowledge

Discovery and Information Retrieval (KDIR), pp. 320–

325, Oct. 2010, Valencia.

Hartman, G. H., Budick, S., eds. 1986. Midrash and

Literature, Yale University Press, New Haven, CT.

Hirshman, M. 2006 . Aggadic midrash. Ch. 2 in: S.

Safrai, Z. Safrai, J. Schwartz, P. J. Tomson (eds.), The

Literature of the Sages, Second Part, Royal Van

Gorcum, Assen, Netherlands, and Augsburg Fortress

Press, Minneapolis, MN, pp. 107–132.

Kirwin, W. 1985. Folk etymology: Remarks on linguistic

solving and who does it. Lore and Language

(Sheffield, U.K.), 4(1), pp. 18–24.

Liu, H., Singh, P. 2002. MAKEBELIEVE: Using

commonsense knowledge to generate stories. In Proc.

of the 18th National Conf. on Artificial Intelligence

and 14th Conf. on Innovative Applications of Artificial

Intelligence, pp. 957–958.

Lönneker, B., Meister, J. C., Gervás, P., Peinado, F.,

Mateas, M. 2005. Story generators: Models and

approaches for the generation of literary artefacts. In

the ACH/ALLC-2005 Conference Abstracts, Victoria,

BC, Canada, June 15–18, 2005, pp. 126–133.

Nissan, E. 2008. Chance vs. causality, and a taxonomy of

explanations. In: M. Negrotti (ed.), Natural Chance,

Artificial Chance, thematic volume of Yearbook of the

Artificial, Vol. 5. Peter Lang, Basel, pp. 195–258.

Nissan, E. 2011a. Computer Applications for Handling

Legal Evidence, Police Investigation, and Case

Argumentation. Springer, Dordrecht.

Nissan, E. 2011b. A Study of Humorous Explanatory

Tales. In: N. Dershowitz and E. Nissan (ed.),

Language, Culture, Computation: Essays in Honour of

Yaacov Choueka. Springer, Berlin, in press.

Nissan, E., Weiss, H. 1994. The HyperJoseph project (2

parts). In: F. Poswick (ed.), Proc. 4th International

Conference on Bible and Computers (AIBI’94),

Amsterdam, August 15–18, 1994. Champion-Slatkine,

Geneva & Paris, 1995, pp. 154–162 & 163–173.

Peinado, F., Gervás, P. 2006. Evaluation of automatic

generation of basic stories. In a special issue on

Computational Creativity, New Generation

Computing, 24(3), pp. 289–302.

Reeves, J. 1991. Computational Morality: A Process

Model of Belief Conflict and Resolution for Story

Understanding, Tech. Rep. 910017, Comp. Science

Dept.. Univ. of California, Los Angeles. ftp: // ftp.

cs.ucla.edu/tech-report/1991-reports/910017.pdf

Ritchie, G. 2004. The Linguistic Analysis of Jokes,

Routledge, London.

Schank, R. C., ed. 1986. Explanation Patterns:

Understanding Mechanically and Creatively,

Lawrence Erlbaum Associates, Hillsdale, NJ.

Schank, R. C., Kass, A., Riesbeck, C. K., eds. 1994.

Inside Case-Based Explanation, Erlbaum, Hillsdale, NJ.

Stock, O., Strapparava, C. 2005. The act of creating

humorous acronyms. Applied Artificial Intelligence,

19(2), pp. 131–151.

Stock, O., Strapparava, C., Nijholt, A., eds. 2002. The

April Fools’ Day Workshop on Computational

Humour: Proceedings of the 20th Twente Workshop

on Language Technology (TWLT20), Trento, Italy,

April 2002. University of Twente, The Netherlands.

Strapparava, C., Valitutti, A. 2004. WordNet-Affect: An

affective extension of WordNet. Proceedings of the

Fourth International Conference on Language

Resources and Evaluation (LREC 2004), Lisbon, May

2004, pp. 1083–1086.

Waller, A., Black, R., O’Mara, D. A., Pain, H., Ritchie,

G., Manurung, R. 2009. Evaluating the STANDUP

pun generating software with children with cerebral

palsy. ACM Transactions on Accessible Computing

(TACCESS), 1(3), article no. 16, at the ACM site.

Walton, D. N. 2004. Abductive Reasoning, University of

Alabama Press, Tuscaloosa, Alabama.

Zuckermann, G. 2000. Camouflaged Borrowing: Folk-

Etymological Nativization in the Service of Puristic

Language Engineering, D.Phil. Dissertation in

Modern Languages, University of Oxford, Oxford.

Zuckermann, G. 2006. “Etymythological othering” and the

power of “lexical engineering”. Ch. 16 in T. Omoniyi,

J.A. Fishman (eds.), Explorations in the Sociology of

Language and Religion, Benjamins, Amsterdam, pp.

237–258.

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