Semantic Representations of English Adjectives-Noun Combinations for NLP

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S e m an t ic R e pre s e n t at io n s o f E n g li s h A dj e c t iv e s - N o u n Co m b in at io n s f o r N LP

M i e A e Ju n g

(In di an a U niv ers ity )

Jun g , M ie A e . 200 2 . S e m an tic Re pre s e nt at ion s of E ng lis h A dje c tiv e - n oun Com bin at ion s f or N LP . T he L ing uis tic A s s ociation of K orea J ournal, 10 (4), 93- 116. T his study deals w ith the sem antic repr esentation s of Eng lish adjective - n oun combin ation s for N atur al Langu ag e Pr oces sing . F or this purpos e, the pr og r am w as im plem ented in Pr olog +CG. T he b asic idea of this study is that in order t o account for the meaning of adjective - n oun com bin ation s , the type definition for an adjective sh ould be declared . T his paper show s that the m eaning of adjectiv e - n oun combin ation s can be rev ealed in three w ay s : m ax im alJ oin betw een the sen se g r aph (s ) for the adjective and the concept of the n oun , m ax im alJ oin betw een the sen se gr aph for the adjective and the s ens e g r aph for the n oun , and m ax im alJ oin betw een the sen se gr aph for the adjective and the schem a for the noun in adjective - n oun con struction s .

K e y w ord s : adjectiv e - noun com bin ation s , NLP , Pr olog +CG, type definition, schem a .

1 . In t ro du c t i o n

Representing the m eanin g of adj ectiv es is one of the challenging issues in natur al language processing . One of the w ell- kn ow n reas ons is 'plasticity ' of adj ect iv al m eaning or 'non - compositionality ' . In other w or ds , the m eaning of an adj ective shift s w ith t he m eaning of the noun it m odifies , depending on w hat pr operty of that noun t he adj ective pert ain s t o (Raskin and Nirenburg , 1995).

In m any cases , the analysis of adj ectiv e- noun combin ations is som ew hat complicat ed. It thus appears that sem ant ic an aly ses and lexico- s em antic descriptions of adj ectives hav e been rare in natural

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language processing . But as Raskin and Nirenburg (1995) m ent ion , this situat ion is changing r apidly . A s com put ational sem antics operat es on larger - scale syst em s , a larger lexicon is needed for t he lexical entries w it h all t heir possible lexical cat egories in them . F or this purpose, comput ation al sem ant icist s and lexicographers are paying m ore att ent ion t o the adj ectival cat egory , a pr eviously neglect ed one.

Motiv at ed by this tren d, this study focuses on the sem antic descriptions of adjectiv es and suggest s an expres siv e m et hod of representing the m eaning of adj ectiv e- noun com binations .

In w hat follow s , w e w ill briefly review som e of the pr evious approaches of comput ation al sem ant ics t o adjectiv es .

2 . P re v i o u s W o rk

A dj ect iv es can be clas sified in m any w ays from differ ent perspectives : synt actic, s em antic, and ont ological. Synt actically , adj ectives can be classified w ith respect t o their function , complem ent ation , an d alt ernation . Sem antically , accor ding t o their logical behavior , adj ectiv es can also be classified w ith respect t o other sem antic features such as aspect or gradat ion (Bouillon and Viegas , 1999).

In this sect ion , w e w ill review an ont ological approach an d a sem antic netw ork approach t o adj ectiv es .

In an ont ological approach like Mikr oKosm os , each lexical entity show s the lexical m apping from language unit t o ont ological concept s in the LEX- MAP part of the SEM - ST RUC zone and the linking betw een the synt actic and t he sem antic structure in t he SYN - ST RUC zone.

Raskin and Nir enburg (1995) propos e a m icrotheory for adjectiv al m eanin g w hich should be included in a comput at ional lexicon . T heir goal is t o dev elop a m ethod for describing the sem antics of adj ect iv es in a t ext . T heir w ork on adj ectiv es form s a m icrotheory used by the MikroKosm os sem antic analyzer , and as sum es that the lexicon is the locus of t he microt heory of adj ectiv al m eaning in Mikr oKosm os .

In this approach , English adj ect iv es are classified int o four subclasses :

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Sem antic Repr esentation s of English A djectiv es - N oun Com bination s 95

attit ude- bas ed (g ood, s up erb, imp ortan t), num erical scale (big , heavy , f orte, p ricey , op ulen t, rip e, y oung ), lit er al scale (red, m ag enta, oval, f ron t, backward), and m ember (authentic, f ak e, nom inal). Raskin an d Nirenburg (1995) argue t hat m any adj ect iv es do not m odify sem antically the noun s that they m odify synt actically , the synt act ic behavior of an adj ective does not det ermine it s lexical m eaning , and thus the attribut e/ predicat iv e distinction has no sem antic significance. T he follow ing display s t he lexical entry for g ood.

(1) (good

(good- A dj 1 (CAT adj ) (SYN - ST RUC

(1 ((root $v ar 1) (cat n )

(m ods ((root $v ar0))))) (2 ((root $v ar0)

(cat adj )

(subj ((root $v ar 1) (cat n )))))) (SEM - ST RUC

(LEX- MAP (attitude

(type ev aluativ e)

(attitude- value (v alue (> 0.75)) (relax able- t o (v alue (> 0.6)))) (scope ^$v ar 1)

(attribut ed- t o *speaker *))))))

In their m icrotheory , g ood select s a propert y of a noun and as signs it s high v alue on the ev aluation scale ass ociat ed w ith t he propert y of the n oun .

W or dNet , a sem antic netw ork approach , represent s the largest publicly av ailable online lexical resources : English nouns , v erbs , adj ectiv es and adv erbs are organized int o synonym set s , each repres enting on e underlyin g lexical concept . Different relation s link the synonym set s . Modifiers in W ordNet are coded by m eans of v arious structural features and relational point ers . T hese features and point ers are int erpret ed by the int erface, w hich represent s the inform ation t o the user in a direct

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w ay . T he sy st em divides adj ectiv es int o t w o m aj or classes : descriptiv e (such as big , interes ting , and p oss ible) and relational (such as p res idential and nuclear). W ordNet cont ains point ers betw een descriptiv e adj ectives expres sing a v alue of an att ribut e and the n oun by w hich that attribut e is lex icalized. T he int eract ion betw een adjectiv es and noun are not prest ored but comput ed as n eeded by an on - line int erpret at ion process (F ellbaum et al., 1993; Miller , 1998).

In W ordNet , each sense in an entry is det erm ined by a ' synset ' , a set of syn onym s . F or inst ance, 25 syns et s are giv en for adj ect iv e g ood by W ordNet 1.7.1 (http :/ / w w w .cogsci.princet on .edu/ cgi- bin/ w ebw n 1.7.1).

T he follow ing show three of the synset s :

(2) a . good (vs . bad) - - (having desirable or positive qualities especially those suit able for a thing specified; "good new s from the hospit al"; "a good report card"; "w hen she w as good she w as v ery v ery good"; "a good knife is one good for cut ting ";

"this st ump w ill m ake a good picnic t able"; "a good check "; "a good j oke"; "a good ext erior paint "; "a good secr et ary "; "a good dress for the office")

b . full, good - - (having the norm ally expect ed am ount ; "giv es full m easure"; "gives good m easur e"; "a good m ile from her e") c . good (v s . evil) - - (m orally adm irable)

Due t o it s v ast cov erage of lex ico- sem antic inform ation as show n in (2) abov e, W ordNet can be a useful resource t o dev elop a w or d sen se disambiguation algorithm .

T he present study w ill t ake adv ant age of the char act eristics of the tw o appr oaches m ent ioned abov e: the ont ological inform ation and the synonym set s of adj ectives .

3 . S e m a n t i c R e pre s e n t at i o n s f o r A dj e c t iv e - N o u n Co m b in at i o n s in P ro l o g +CG

T his paper deals w ith the sem antic represent ation s of adj ectiv e- noun

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combin ations for natural language processing . T o this end, program s are im plem ent ed in Prolog +CG 2.5, an obj ect - orient ed logic programming language based on Conceptual Graphs (CGs ).

CGs (Sow a, 1984, 1991, 1992, 2000) hav e been used in m any fields of artificial int elligence (AI), especially natural language processin g and kn ow ledge- based syst em s . CGs pr ovide the expressiv e pow er of an adv anced know ledge repr esent ation language, i.e., adv anced sem antic net s , type hierarchies , schem at a, t he notion of cont ex t , an d the like.

Prolog +CG (Kabbaj et al., 2001) is a cont extual ext ension of Prolog in the sen se that it int egrat es notion s like obj ect s an d inherit ance. T he int egration of Pr olog , obj ect - orient ed pr ogramm ing , CG, and Jav a provides a pow erful dev elopm ent al environm ent for t he creation of kn ow ledge- based applications . How ev er , although Prolog +CG pr ovides rich m anipulating operations of CG w it h the expres siv e pow er , and t here has been research t o resolve lin guistic phenom ena such as a narrative analy sis (Schärfe, 2002) and an indirect an aphora resolution (Jun g , 2002), there has been no pr actical res earch t o represent the m eaning of adj ective- noun combination s in the lan guage. T hus , t his paper w ill show how t o r epresent the m eaning of these combinations by implem enting Prolog +CG.

CGs include relations , structur es , and guidelines for representing sent ences in natural language. T heir basic principle is that cont ent w or ds m ap t o concept nodes , and function w ords like prepositions and conjunct ions m ap t o relation nodes . Ordinary nouns , v erbs , adjectiv es , and adv erbs m ap t o type labels in a concept node: lady⇒[Lady], dance

⇒[Dance], happy⇒[Happy] (Sow a, 1991). In t his m apping principle, adj ectives can ex ist independently w ithout any other connection t o another concept . But in Sow a (1999), the not at ion t o describe adj ect iv es slight ly chan ged. N ouns becom e type labels , w hile adj ectives becom e m onadic conceptual r elations . T hus , for inst ance, t he adj ectiv e available can be repr esent ed by [Av ailable] linked by t he attribut e relation (At tr ), i.e., [ ]- > (Att r )- > [Av ailable], and since [ ] is the univ ersal type, it m ay be any concept of any t ype.

H ow ev er , if [Av ailable] can be linked t o any concept of any type,

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the formula does n ot guarant ee that the graph is canonical. F or inst ance, if w e follow the not ation abov e, the formula for g reen w ill look like [ ]- > (Colr )- > [Green]. How ev er , since t he conceptual type of [ ] is unspecified, the formula cannot block anom alous combinations such as [Idea]- > (Colr )- > [Green ].

F or this reason , the present study suggest s t hat formulae for adj ectives should be can onical, w here concept s that can be linked t o the concept for an adj ective are specified. F or inst ance, since the adj ect iv e g reen w ill be represent ed by the gr aph [Obj ect ]- > (Colr )- > [Green] and [Idea] is not [Obj ect ], g reen cannot be joined w ith idea.

T he repr esent ations suggest ed in this study satisfy the compositionality r equir em ent in that the m eanin g of an adj ect iv e- noun combin ation is obt ained through the composition of the m eaning of t he adj ective and that of t he noun . A graph resultin g from that composition is w ell- form ed, since t he s ense graph for an adj ective must be canonical and a canonical graph rules out anom alies by enforcing selectional constr aint s .

Now , let us t ake a look at the ex ample red car. (3) and (4) below show tw o com ponent s of Prolog +CG: the program pane and the int erface pane.

A pr ogram in Prolog +CG is composed of type hierarchies w hich present ont ological inform ation of concept ual types an d concept ual graphs as seen in (3) below . (4) show s t he int erface betw een the us er and the syst em , w here if the user asks the syst em a question , t he syst em pr ovides the answ ers t o the request .

(3) Univ ersal > Obj ect , P erson , Propert y . Obj ect > Vehicle.

V ehicle > Car .

Property > Visual_Property , T actile_Pr operty . Visual_Property > Color .

Color > Red.

sen se("r ed", [Object ]- colr - > [Red]).

(4) ?- sense("red", g ). ? - sense("red", g ).

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{g = [Object ]- colr - > [Red]}

?- m axim alJoin ([Object ]- colr - > [Red], [Car], g ).

{g = [Car]- colr - > [Red]}

Prolog +CG has "sense" as a com ponent of the gramm ar . A sens e graph show s w hat concept s are necessary t o repres ent the m eaning of a w or d. Although Prolog +CG int roduce "sen se" in order t o represent the type definition of a noun , in this study , w e w ill use "sense" in order t o represent the canonical graph of an adj ectiv e as w ell as a noun .

A s seen in (4) abov e, w hen t he user request s the sense of red, t he syst em s earches the CG ass ociat ed t o "red" and then try t o solve it as follow s :

(5) ?- sense("red", g ).

{g = [Object ]- colr - > [Red]}

T hen , as seen in (6) below , a m axim alJoin operation betw een the graph [Obj ect ]- colr - > [Red] and concept [Car] is r equest ed on the int erface console.

(6) ?- m axim alJoin ([Obj ect ]- colr - > [Red], [Car], g ).

{g = [Car]- colr - > [Red]}

According t o the t ype hierarchies described in (3) abov e, since [Obj ect ] is a supertype of [Car], the tw o concept s can be j oin ed m axim ally t o be [Car]. T hus , the m axim alJoin betw een [Obj ect ]- colr - > [Red] and [Car] result s in [Car]- > colr - > [Red].

Fin ally , this gr aph g can be added int o t he original pr ogram through the expert sy st em query as in (7).

(7) ?- [Car]- colr - > [Red].

==> Is it true that : [Car ]- colr - > [Red] ? type y (for yes ) or n (for no) : y

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In t his w ay , the m eaning of t he com bination of red and car can be obt ain ed. Similarly , as can be seen in (8) and (9) below , the m eaning of wooden cross can be obt ained by m ax im alJoin betw een the sense graph for wooden an d the concept of cross :

(8) ?- sense("w ooden ", g ).

{g = [Artifact ]- m atr - > [W ood]}

(9) ?- m axim alJoin ([Artifact ]- m atr - > [W ood], [Cros s], g ).

{g = [Cross]- m at r - > [W ood]}

H ow ev er , s om e adj ectiv e- noun combination s are ambiguous . In order t o account for these am biguities , this st udy suggest s that an adj ect iv e can be represent ed through different canonical graphs , each of w hich includes concept s that can be linked t o the giv en sens e of t he adj ectiv e.

Let us look at crim inal lawy er, w hich is am biguous in tw o w ays : crim inal could m ean dealing w it h crim e, or it could m ean w ho commit s a crim e. T hese tw o sen ses of crim inal are show n in (10) below .

(10) a. sens e("crimin al", [Conduct ] -

- agnt - > [P erson ],

- thm e- > [Law suit ]- att r - > [Crim e]).

b . sense("criminal", [Commit ] -

- thm e- > [Crim e], - agnt - > [P erson ]).

(10a) can be read "t he agent of Conduct is P erson , t he them e of the Conduct is Law suit , and the attribut e of t he Law suit is Crim e."

When the graph in (10a ) an d concept [Law yer] are j oined m axim ally , the follow ing graph g w ill be result ed.

(11) ?- m axim alJoin ([Conduct ] - - agnt - > [Per son],

- thm e- > [Law suit ]- attr - > [Crim e];, [Law y er], g ).

{g = [Conduct ] -

- agnt - > [Law yer],

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Sem antic Representations of English A djectives - N oun Com bin ation s 101

- thm e- > [Law suit ]- attr - > [Crim e]}

T he graph g in (11) m eans a lawy er who conducts crim inal laws uits . H ow ev er , if t he s ense graph of crim inal in (10b ) and concept [Law yer ] are j oined m axim ally , the graph g in (12) w ill be r esult ed. It m eans a lawy er who com m its a crim e.

(12) ?- m ax im alJoin ([Commit ] -

- thm e- > [Crim e],

- agnt - > [Per son];, [Law y er], g ).

{g = [Commit ] -

- t hm e- > [Crim e], - agnt - > [Law y er]}

T he adj ective m us ical is als o am biguous . Let us con sider m us ical ins trum ent an d m us ical child . T he adj ect iv e m us ical m ay m ean

"charact erized by or capable of producing music" or "t alent ed in or dev ot ed t o m usic." T hese tw o s enses are represent ed as follow s :

(13) a. sens e("musical", [Produce] - - thm e- > [Music], - in st - > [Obj ect ]).

b . sense("musical", [P erson]- poss - > [T alent ]- att r - > [Music]).

When the gr aph in (13a) and the concept for ins trum ent are j oined m axim ally , t he result w ill look like:

(14) [Produce] -

- thm e- > [Music], - inst - > [Instrum ent ].

On the other hand, t he m axim alJoin betw een the graph in (13b ) and the concept for child w ill produce the follow ing graph .

(15) [Child]- poss - > [T alent ]- attr - > [Music].

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H ow ev er , w hat if a m ax im alJoin bet w een the graph in (13a) and the concept for child , or a m axim alJoin betw een the graph in (13b ) and t he concept for ins trum ent is r equest ed? A s w e can see in (16a) an d (16b ) below , t hese j oins are aut om atically banned, since [Child] cannot be m axim ally j oin ed w ith any concept s in (13a) (i.e., [Produce], [Music], or [Obj ect ]) and [Inst rum ent ] cann ot be m axim ally j oined w ith any concept s in (13b ) (i.e., [Pers on], [T alent ], or [Music]).

(16) a. ?- m axim alJoin ([Produce] - - thm e- > [Music],

- inst - > [Obj ect ];, [Child], g ).

no.

b . ?- m axim alJoin ([P erson ]- poss - > [T alent ]- attr - > [Music], [In strum ent ], g ).

no.

S o far , w e hav e seen cases that the m eaning of an adj ectiv e- noun combin ation can be rev ealed thr ough the j oin betw een the sen se graph for t he adj ectiv e and the concept for the noun . H ow ever , w hen it com es t o such cases as j us t ruler, things are differ ent . In order t o underst and it s m eanin g, the specification of the m eaning of ruler is required.

(17) a. sense("just ", [Act ]- m anr - > [Justice]).

b . s ense("ruler ", [P erson ]< - agnt - [Rule]).

T he m axim alJoin betw een the sense graph for j us t an d that for ruler yields the graph as follow s .

(18) [P erson]< - agnt - [Rule]- m anr - > [Justice]

Let us now look at beautif ul dancer. T he sent ence M ary is a beautif ul dancer has tw o different m eanings : M ary is beautif ul as a dancer or M ary is beautif ul and a dancer. T he tw o gr aphs in (19) show the tw o senses of beautif ul.

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(19) a. sens e("beautiful",

[Beauty ]< - char - [Appear ance]< - pos s - [Per son]).

b. sen se("beaut iful", [Act ]- m anr - > [Beauty]).

T he m axim alJoin betw een the graph in (19a) and [Dancer] yields a graph in (20) below , w here [Dancer] is the result of the m axim alJoin bet w een [Pers on] and [Dancer] since Pers on can be m erged int o it s subtype Dancer .

(20) [Beauty]< - char - [Appearance]< - poss - [Dancer]

On the other han d, the graph in (19b) cannot be combined w ith [Dancer], since Dancer cannot be m ax im ally j oin ed w ith [Act ] or [Beauty]. T hus , for the graph in (19b ) t o be j oined w ith [Dancer], the sense graph for dancer should be introduced int o t he program as show n in (21) below .

(21) sens e("dancer ", [Dance]- agnt - > [P erson]).

Aft er t he gr aph for dancer is ret riev ed, the graph in (19b ) and the graph in (21) can be m axim ally joined, resulting in the graph (22) as follow s :

(22) [Dance] -

- m anr - > [Beauty ], - agnt - > [P erson].

If the graph for beautif ul in (19a) and the graph for dancer in (21) are j oined, t hen the result w ill be (23), w hich has t he s am e m eaning as (20) abov e.

(23) [Beauty]< - char - [Appearance]< - poss - [P erson]- agnt - > [Dance].

Now , let us consider t he adjectiv e g ood. Good m echanic has tw o

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int erpr et ations in accor dance w ith t he tw o senses of g ood. (24a) s ays that the quality of t he skill is good, w hile (24b ) m eans that the attitude is good.

(24) a. s ense("good", [Act ]- qual- > [Superiorit y]).

b . s ense("good", [Anim at e]- poss - > [Attitude]- attr - > [Goodness]).

(25) sense("m echanic", [P erson]< - agnt - [Repair]- obj - > [Machine]).

(26) a. [Repair] -

- qual- > [Superiority], - agnt - > [Pers on], - obj - > [Machine].

b . [Repair] -

- agnt - > [Pers on]- poss - > [Attit ude]- attr - > [Goodness], - obj - > [Machine].

T he similar procedure can be found in such cases as p oor teacher/ liar/ ling uis t. T he program has t w o different graphs for p oor (i.e., the economic st atus v s . t he quality of a skill) and the graphs for the n ouns such as teacher, ling uis t, an d liar.

(27) a . sense("poor ", [Per son]- poss - > [P osses sion ]- am ount - > [F ew ]).

b . sense("poor ", [Act ]- qual- > [Inferiorit y]).

(28) a . sense("t eacher ",

[Clas s_subject ]< - thm e- [T each]- agnt - > [P erson ]).

b . sense("linguist ", [Study ] -

- t h m e - > [L i n g u i s t i c s ] ) . c . sense("liar ", [Lie]- agnt - > [P erson ]).

E ach of the tw o graphs for p oor in (27) can be com bined w ith each of the graphs for teacher, ling uis t, and liar in (28). F or inst ance, for p oor teacher, tw o different m axim alJoin operations can be request ed, resulting in tw o differ ent graphs as in (29) and (30).

(29) ?- m axim alJoin ([P erson]- poss - > [Possession]- am ount - > [F ew ],

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[Class_subj ect ]< - thm e- [T each]- agnt - > [P erson], g ).

{g = [T each] -

- thm e- > [Class_subj ect ],

- agnt - > [P erson ]- poss - > [P os session]- am ount - > [F ew ]}

(30) ?- m axim alJoin ([Act ] -

- agnt - > [P erson],

- qual- > [Inferiority ];, [T each] - - agnt - > [P erson],

- t hm e- > [Class_subj ect ];, g ).

{g = [T each] -

- agnt - > [P erson ], - qual- > [Inferiority ], - thm e- > [Class_subj ect ]}

In a similar m anner , the m eanin g for p oor ling uis t an d p oor liar w ill be rev ealed as follow s : (31a) and (31b ) are for p oor ling uis t, and (32a) and (31b ) for p oor liar.

(31) a. [Study ] -

- agnt - > [P erson ]- poss - > [P os session]- am ount - > [F ew ], - thm e- > [Lin guistics].

b . [St udy] -

- agnt - > [P erson], - qual- > [Inferiority ], - t hm e- > [Linguist ics].

(32) a. [Lie]- agnt - > [Per son]- poss - > [P osses sion ]- am ount - > [F ew ].

b . [Lie] -

- agnt - > [P erson ], - qual- > [Inferiority ].

Noting that these adj ectiv es like beautif ul, g ood, and p oor can m odify an im plicit ev ent of a kind appr opriat e for the obj ect denot ed by the nominal, Larson (2000) st at es that w it hout inv ocation of an indepen dent , implicit event there w ould be a v ery serious compositionality problem . How ev er , he does not specify w hat ' an implicit ev ent of a kin d appropriat e ' for the object is .

T he virtue of the approach pres ent ed in this study is that it can

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explicitly rev eal the appropriat e ev ent s that adj ect iv es m odify since the senses of n ouns as w ell as t hose of adj ectiv es can be declared in the program .

Now , let us consider E ng lish teacher, w hich has t w o r eadin gs (i.e., 'person w ho t eaches English ' and ' person w ho t eaches and w ho is English ' ). We have t w o s ense graphs for E ng lish in (33) and a sense graph for teacher in (34) as follow s :

(33) a. sense("En glish ", [P erson]- st at - > [Born ]- place- > [Englan d]).

b . sense("English ", [Language]- at tr - > [English]).

(34) sens e("t eacher ", [Per son]< - agnt - [T each]- thm e- > [Class_subj ect ]).

T he graph in (34) can be com bined w ith (33a) or (33b ), yielding tw o m axim ally - j oined graphs in (35a ) and (35b ) respectiv ely .

a. [T each] -

- thm e- > [Language]- at tr - > [English].

b . [T each] -

- agnt - > [P erson ]- st at - > [Born]- place- > [England], - thm e- > [Class_subj ect ].

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Now , let us t ake a look at old rival. Larson (2000) present s that old displays an apparent three- w ay ambiguity : "aged", "longst anding ", and

"form er ."

(36) Max is an old riv al.

a. "an aged riv al" Max is a riv al and he is old.

b . "a longst anding riv al" T he riv alry w ith Max is old.

c. "a form er riv al" Max w as a riv al, but he is no longer one.

F or t he m eaning of old rival, three graphs for old can be giv en as seen in (37) below . Each of the gr aphs in (37a), (37b ), and (37c) corresponds t o (36a), (36b ), and (36c) respectiv ely . T he sense gr aph for rival can be structur ed as in (38).

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(37) a. sens e("old", [Live] - - dur - > [Long], - agnt - > [P erson ]).

b . sense("old", [Act ]- dur - > [Long]).

c. sen se("old", [Act ]- ptim - > [T im e]- succ- > [Present ]).

(38) sens e("riv al", [Defeat ] -

- ptnt - > [Cont est ant ], - agnt - > [Per son]).

T hen , the m ax im alJoins are r equest ed betw een each graph for old and the graph for rival, yielding three different graphs as in (39).

(39) a. ?- m axim alJoin ([P erson]< - agnt - [Liv e]- dur - > [Long], [Riv al], g ).

{g = [Liv e] -

- agnt - > [Riv al], - dur - > [Long]}

b . ?- m ax im alJoin ([Act ]- dur - > [Long],

[P erson ]< - agnt - [Defeat ]- pt nt - > [Cont est ant ], g ).

{g = [Defeat ] -

- dur - > [Long], - agnt - > [P erson ], - ptnt - > [Cont est ant ]}

c. ?- m ax im alJoin ([Act ]- pt im - > [T im e]- succ- > [Pr esent ], [P erson ]< - agnt - [Defeat ]- pt nt - > [Cont est ant ], g ).

{g = [Defeat ] -

- ptim - > [T im e]- succ - > [Present ], - agnt - > [Per son],

- ptnt - > [Cont est ant ]}

(39c) conv eys t he 'form er ' reading . Now , let us consider t he t emporal adj ective f orm er as in f orm er p res iden t. F orm er must be represent ed ex act ly the sam e as the t hird sense of old m entioned in (37c) abov e. T he graph for f orm er and the graph for p res iden t w ill be represent ed as follow s .

(40) sens e("form er ", [Act ]- ptim - > [T im e]- succ- > [Present ]).

(41) sens e("president ",

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[Per son]< - agnt - [A dminist er]- obj - > [Gov ernm ent ]).

T he m axim alJoin betw een the tw o graphs in (40) and (41) w ill yield the gr aph in (42).

(42) [A dm inist er] -

- pt im - > [T im e]- succ- > [Pr esent ], - agnt - > [P erson],

- obj - > [Gov ernm ent ].

In this w ay , the m eaning of adj ectiv e- noun com binations such as beautif ul dancer, p oor ling uis t, etc. can be account ed for t hrough s ense graphs of the nouns . How ev er , adj ectiv es and nouns can be com bined in a m or e indirect w ay t hrough collections of schem at a (i.e., w orld kn ow ledge) for nouns . T he pos sible ex amples of this type include recent letter an d quick cup of coff ee. Ev en though neither letter nor cup appears t o be an ev entive n oun , lett ers readily inv oke "surr ounding event s " of w riting , sending , r eceiving and r eadin g .

Violi (2001) m entions that know ledge is structured on the basis of organized set s of concept s that are sy st em atically linked t o each ot her in schem at a, an d, t hus , thes e not ations can be extrem ely helpful for sem antic represent ation an d in general for a t heory of lexical m eanin g .

In CGs , the schem a is t he basic structur e for representing background kn ow ledge for hum an - like inference. T he schem a describes t he conv entional, norm ally occurring , or default roles that a concept plays w it h respect t o other concept s . By joining schem at a, the infer ence engine expands a query graph t o a w orking graph that incorporat es additional backgroun d inform ation .

F or recen t letter, w e m ay hav e tw o different schem a graphs as given in (43): one relat ed t o the ev ent of w ritin g and the other relat ed t o the event of receivin g .

(43) a. Lett er (schem a)::[Writ e] - - agnt - > [S ender],

- r slt - > [Lett er]< - obj - [Read]- agnt - > [Receiv er].

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b . Lett er (schem a)::[Receiv e] - - agnt - > [Receiver ],

- obj - > [Let t er]< - rslt - [Writ e]- agnt - > [Sender].

If a recen t letter m eans a lett er r ecently w ritt en , then the schem a graph in (43a) w ill be joined w ith the sen se graph for recen t (i.e., sense("recent ", [Act ]- ptim - > [T im e]- char - > [Close]< - char - [Pres ent ]), yieldin g the follow in g graph :

(44) [Writ e] -

- ptim - > [T im e]- char - > [Close]< - char - [Pres ent ], - agnt - > [Sen der],

- rslt - > [Lett er]< - obj - [Read]- agnt - > [Receiv er].

If a recen t letter m eans a lett er recent ly receiv ed, t hen t he schem a graph in (43b ) an d t he sens e graph for recen t w ill be j oined m axim ally , resulting in the follow ing graph :

(45) [Receiv e] -

- pt im - > [T im e]- char - > [Close]< - char - [Present ], - agnt - > [Receiv er],

- obj - > [Lett er ]< - rslt - [Writ e]- agnt - > [Sender ].

Similarly , in order t o int erpret quick cup of coff ee, a schem a for coff ee should be retriev ed from t he program as w ell as t he sense graph for quick . If w e con sider a t erm like coff ee, w e find that it s sem ant ic represent ation includes a w ide r ange of encyclopedic pr operties . W e kn ow that coffee is an obj ect of drinking , it is dark , it has a particular flav or , it has st imulating effect s because it cont ains caffeine, and so on .

(46) ?- sens e("quick ", g ).

{g = [Act ]- m anr - > [Quick]}

(47) ?- Coffee(schem a):: G.

{G = [Coffee] -

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< - obj - [Drink]- in st - > [Cup], - cont - > [Caffein e],

- poss - > [Flavor]}

T he schem a in (47) show s that , am ong other things , [Coffee] is relat ed t o Act [Drink] and, in turn , [Drink] has an instrum ent relation w it h [Cup]. T hus , these links allow quick and cup of coff ee t o be joined m axim ally . T he result ed graph show n in (48) below says that t he obj ect of [Drink] is [Coffee] and the m ann er of [Drink] is [Quick].

(48) [Coffee] -

< - obj - [Drink] -

- m anr - > [Quick], - inst - > [Cup];, - cont - > [Caffeine],

- poss - > [F lav or].

Now , let us consider the "celebrat ed" ex ample f ak e g un. T here hav e been different approaches t o a class of concept combination kn ow n as

"priv ativ es ". Kamp (1975) defin es priv ativ e adj ectives as adj ect iv es such that , giv en adj ective A and noun N , the claim ' No AN is a N ' is necessarily t rue.

H ow ev er , P art ee (2001) proposes that adj ectiv es f ak e and im ag inary are not actually priv ativ e, but subsect iv e, and that no adj ectiv es ar e actually privativ e. She hypothesizes that in int erpretin g a sent ence like I don 't care whether that f ur is f ak e f ur or real f ur, w e actually expan d the denot ation of 'fur ' t o include both fake and real fur . Fr ank (1995) also says that if x is a f ak e g un, t hen x is a g un in som e sense and he suggest s the sense generat ion m odel by show ing how t o handle f ak e g un. He m entions Fir es (Bullet s ), Made- of(Met al), and Function (Kill) as central features of the concept gun , an d T rigger (+), Barrel(+), an d Handle(+) as diagnostic features .

On t he other han d, Coulson and F auconnier (1999) and Coulson (2001) suggest that the charact er of a f ak e g un w ill depen d on the faker ' s m otivation , the scenario in w hich the gun is t o funct ion , and the

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kn ow ledge of the prospective victim . T hey also say that the charact eristics of fake obj ect s arise becaus e of the w ay in w hich the int ent t o deceiv e is centr al t o the concept of f ak e, and t hat the blendin g process enables speaker s t o m ake connections bet w een elem ent s w hose obj ectiv e propert ies m ay be m at erially different .

Wit hin the CG fram ew ork, this st udy int egrat es the sense generation m odel and the concept ual blendin g m odel, suggesting that in the combin ation of f ak e g un , g un has a schem a w hich includes it s m at erial, part s , and function s . T he sense graph for f ak e should include both the int ent t o deceiv e and the fake obj ect w hich is believ ed t o be a r eal one.

T he schem a for g un is show n in (49), an d the sense graph for f ak e is giv en in (50), w hich has the multi- refer ent not ation t o specify sev eral occurr ences of the sam e concept : the multi- referent *1 is used t o specify that the tw o concept of [Obj ect : *1] ar e in fact tw o occurrence of the sam e concept .

(49) Gun (schem a)::[Gun] -

- m atr - > [Met al], - part - > [T rigger], - part - > [Barr el], - part - > [Handle], - poss - > [Bullet ], - purp- > [Kill].

(50) s ense("fake", [Pers on]< - agnt - [Deceiv e]- - ptnt - > [P erson ]- st at - > [Believ e]- thm e- >

[Obj ect ]- st at - > [Ident ity ]< - st at - [Obj ect :*1], - in st - > [Obj ect ]).

When thes e tw o graphs are j oined m axim ally , the graph in (51) is result ed.

(51) [Deceiv e] -

- ptnt - > [P erson ]- st at - > [Believ e]- thm e- > [Gun] - - st at - > [Identity ]< - st at - [Obj ect : *1], - m atr - > [Met al],

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- part - > [T rigger], - part - > [Barrel], - part - > [Handle], - pos s - > [Bullet ], - purp- > [Kill];, - in st - > [Obj ect : *1].

T he gr aph in (51) says t hat an obj ect is the in strum ent of deception and som eone is deceiv ed int o believing that the obj ect is a gun w hich has those properties m entioned in (49). But since the instrum ent has the conceptual type of Obj ect , it can refer t o anything w hose conceptual type is a subtype of Object . T hat is , it might be a pencil, a finger , or even a zucchini. Since, in the schem a graph , the gun has the inform ation about the m at erial, t he funct ion , and part s of a gun , the graph g in (51) show s the gen eral know ledge about a gun . T hus , the graph show s not m erely Couls on and F auconnier ' s view point (i.e., the act or ' s int ention t o deceiv e an d the vict im ' s w ould- be- belief), but Frank ' s (i.e., the fact that t here seem t o be basic gun features (e.g ., being a w eapon (Gun is a subtype of W eapon ), having a barr el, theoretical ability t o shoot bullet s , and so forth ) that cannot be alt ered in a cont ex t such as a robbery or ev en in a gam e cont ex t such as a cops and robber s gam e).

Now , let us t ake a look at the cas e of tall p ers on. T he sem antic contribution of adj ectiv es such as tall and heavy is dependent on the head nouns that they m odify . Tall den ot es one range of height s for a person , another for a tree, still another for a building . It is likely that part of the m eaning of each of the nouns is a ran ge of ex pect ed v alues for the attribut e HEIGHT . Tall is int erpret ed relativ e t o the expect ed height of obj ect s of the kind denot ed by the head noun . T he gr aph in (52) represent s t hat the height is superior t o the av erage.

(52) sens e("t all",

[Aver age]- sup- > [Measur e]< - m esr - [Height ]< - attr - [T hing]).

T he default av erage w ill be retrieved lat er w hen the sense graph of

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tall is com bined w ith the schem a of a noun w hich follow s the adj ectiv e.

F or inst ance, as seen in (53) below , the schem a of p ers on m ay include Height w hose default height might be 170cm .

(53) P erson (schem a)::[P erson] -

- attr - > [Height ] -

- m esr - > [Measure], - char - > [Av erage = 170];, - attr - > [W eight ].

In t his case, the m axim alJoin betw een the graphs in (52) and (53) w ill produce t he graph as follow s :

(54) [P erson] -

- attr - > [Height ] -

- m esr - > [Measur e]< - sup- [Aver age], - char - > [Av erage = 170];,

- attr - > [Weight ].

T he gr aph in (54) says t hat a per son has attribut es such as height and w eight , and the m easure (i.e., v alue) of his or her height is superior t o the aver age of 170cm .

4 . Co n c lu s i o n

T his st udy has dealt w ith the s em antic repr esent ations of English adj ective- noun com binations , w hich are implem ent ed in Prolog +CG.

T his study suggest s t hat in or der t o account for the m eaning of adj ective- noun combin ations , the type definitions of the concept s for the adj ectives should be declared. An adj ectiv e should not be m apped int o a sin gle concept , as in the case of a noun , but rat her should be m apped int o a canonical graph cont aining other concept s that consist of the m eanin g of t he adj ectiv e. T his study also show s that ther e are cases w here in addition t o the t ype definition s of the adj ect iv es , the type definitions and the schem at a of the concept s for the nouns should be

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specified in order t o underst and the m eaning of adj ectiv e- noun combin ations

T his paper has show n that the m eaning of adj ectiv e- noun combin ations can be rev ealed in three w ays . T he first type is dem on strat ed in the cases w here the m eaning of adj ectiv e- noun combin ations can be specified t hrough the m axim alJoin betw een the sense graph for the adj ect iv e and the concept for the n oun .

T he second type is ex amplified in the cases w here the m eaning can be obt ained by m ean s of the m axim alJoin bet w een the s ense of gr aph for the adj ectiv e an d the s ense graph for the noun .

T he third type is rev ealed in the cases w here schem at ic know ledge about the noun is r elat ed : since a s ense gr aph for an adj ectiv e has a concept w hich is included in t he schem a graph for a noun , the m eaning of the combin ation can be obt ained through t he m axim alJoin betw een the t w o graphs .

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MieA e Jung

Comput er Science Departm ent Indiana Univ ersity

Bloomin gt on , IN 47405, USA Phone: 1- 812- 857- 3238 Em ail: m ajung @indiana.edu

Received in July , 2002 Accept ed in Nov ember , 2002