m 
m 
A Frame-Semantic Approach to Semantic Annotation 
John B. Lowe jblowe~garnet.berkeley.edu 
CoIHn F. Baker co11£nb@icsi.berkeley, edu 
Charles J. Fillmore fillmore@cogsci.berkeley, edu 
Department of Linguistics 
University of California 
Berkeley, CA 94720 
Abstract 
The number and arrangement of seman- 
tic tags must be constrained, lest the size 
and complexity of the tagging sets (tagsets) 
used for semantic annotation become un- 
wieldy both for humans and computers. 
The description of lexical predicates within 
the framework of frame semantics provides 
a natural method for selecting and struc- 
turing appropriate tagsets. 
1 Motivation 
The research present here is to be conducted under 
the FrameNet research product at the University of 
California. 1 On this project our primary aim is to 
produce frame-semantic descriptions of lexical items; 
our concern with semantically tagged corpora is at 
both ends of our research. That is, we expect to use 
partially semantically tagged corpora in the inves- 
tigation stage--perhaps nothing more than having 
WordNet hypernyms associated with nouns--but we 
will produce semantically tagged corpus lines as a 
by-product of our work. 
Most major grammatical theories now accept the 
general principle that some set of semantic roles 
("case roles", "thematic roles", or "theta roles") is 
necessary for characterizing the semantic relations 
that a predicate can have to its arguments. This 
would seem to be one obvious starting-point for 
choosing a tag set for semantically annotating cor- 
pora, but there is no agreement as to the size of 
the minimal necessary set of "universal" roles. Also, 
when we examine particular semantic fields, it is ob- 
vious that each field brings to mind a new set of 
more specific roles. In fact, the more closely we look 
at individual predicates, the more specific the argu- 
ment roles become, creating the specter of trying to 
define an unlimited number of very fine-grained tags 
and attributes. An adequate account of the syntax 
and semantics of a language will inevitably involve 
a fairly detailed set of semantic tags, but how can 
we find the right level of 9ranularity of tags for each 
semantic area? 
Consider the sentence: 
(1) The waters of the spa cure arthritis. 
A semantic annotation of the constituents must 
identify at least 
• the action or state associated with the verb, 
possibly expressed in terms of primitives or 
some kind of metalanguage; 
• the participants (normally expressed as argu- 
ments); and 
• the roles of the participants in the action or 
state. 
A basic parse will identify the sentence's syntactic 
constituents; from the point of view of the head verb 
cure, then, a semantic annotation should reveal the 
mapping between the syntactic constituents and the 
frame-semantic elements they instantiate. In sen- 
tence (1) above, for example, the grammatical sub- 
ject "the waters of the spa" corresponds to the the- 
matic ca~er of the curing effect on the entity ex- 
pressed as "arthritis", the verb's syntactic direct ob- 
ject and its thematic patient. 2 
However, there is something incomplete about 
such an analysis: it fails to anchor the arguments of 
2Here we use the word patient (in italics) as the name 
of a case role; we will also use the word in the medical 
sense later in this paper. Caveat lector/ 
1The work is housed in the International Computer 
Science Institute in Berkeley and funded by the National 
Science Foundation under NSF grant IRI 96-18838. The 
official name of the project is "Tools for lexicon build- 
ing"; the PI is Charles J. Fillmore. Starting date March 
1, 1997. 
18 
cure within a "generic medical event" where it would 
be understood that the disease (arthritis) must be 
borne by some sufferer, and that a sufferer under- 
going a treatment is participating as a patient in 
such an event. We identify such "generic events" as 
frames, and express our understanding of the struc- 
ture of such events and the relationship of linguistic 
material to them in terms of the theory of frame 
semantics. 
2 Frame Semantics. 
In frame semantics we take the view that word mean- 
ings are best understood in reference to the concep- 
tual structures which support and motivate them. 
We believe, therefore, that any description of word 
meanings must begin by identifying such underlying 
conceptual structures. 3 
Frames have many properties of stereotyped sce- 
narios -- situations in which speakers expect certain 
events to occur and states to obtain. 4 
In general, frames encode a certain amount of 
"real-world knowledge" in schematized form. Con- 
sider the common scenario which exemplifies the 
commercial transaction .frame: the elements of such 
frames are the individuals and the props that par- 
ticipate in such transactions (which we call FRAME 
ELEMENTS): the individuals in this case are the two 
protagonists in the transaction; the props are the 
two objects that undergo changes of ownership, one 
of them being money. 
Some frames encode patterns of opposition that 
human beings are aware of through everyday expe- 
rience, such as our awareness of the direction of grav- 
itational forces; still others reflect knowledge of the 
structures and functions of objects, such as knowl- 
edge of the parts and functions of the human body. 
The study of the frames which enter into human cog- 
nition is itself a huge field of research - we do not 
claim to know in advance how much frame knowl- 
edge must be specifically encoded in frame descrip- 
tions to make them useful for either linguistic or 
NLP purposes. We expect to be able to draw ten- 
tative conclusions about this based on what we find 
in corpora. 
3For a discussion of these ideas, see (Fillmore, 1968); 
(Fillmore, 1977b); (Fillmore, 1977a); (Fillmore, 1982); 
(Fillmore and Atkin.~, 1992); (Fillmore and Atkin.% 1994). 
4The word frame has been much used in AI and NLP 
research. We wish to give the word a formal interpreta- 
tion only to the extent that it helps us in our research 
and provides a container for the features and entities we 
describe. We do not, in this context, depend on any 
cialm.q about the cognitive status of frames. 
19 
We will say that individual words or phrases evoke 
particular frames or instantiate particular elements 
of such frames. So, for example, if we are examining 
the "commercial transaction" frame, we will need 
to identify such frame elements as BUYER, SELLER, 
PAYMENT, GOODS, etc., and we can speak of such 
words as buy, sell, pay, charge, customer, merchant, 
clerk, etc., as capable of evoking this frame. In 
particular sentences, we might find such words or 
phrases as John, the customer, etc. instantiating the 
BUYER, or a chicken, a new car, etc., instantiating 
the GOODS. 
3 Inheritance in Frame Semantics 
Of course, speakers of a language know something 
about the differences and similarities among vari- 
ons types of commercial transactions, e.g. that buy- 
ing a small item in a store often involves making 
change, etc. Strictly speaking, this is "world knowl- 
edge" rather than "linguistic knowledge", but this 
level of detail is required even to parse sentences 
correctly, e.g. to recognize the different functions of 
the PPs in "buy a candy bar with a red wrapper" 
and "buy a candy bar with a $20 bill" and thus to 
attach them appropriately. 
frame (CommercialTransaction) 
frame-elements{BUYER, SELLER, PAYMENT, GOODS} 
scenes (BUYER gets GOODS, 
SELLER gets PAYMENT) 
frame (Rea~stateTransaction) 
inherits (Corn mercialTransaction) 
link(BORROWER = BUYER, LOAN = PAYMENT) 
frame-elements{BORROWER, LOAN, LENDER} 
scenes (LOAN (from LENDER) creates PAYMENT, 
BUYER gets LOAN) 
Figure h A subframe can inherit elements and se- 
mantics from its parent. 
More complicated cases require more elaborated 
frames. Thus, "buy a house with a 30-year mort- 
gage" involves a different frame from buying a candy 
bar, and entails a slightly different interpretation 
of the PAYMENT element. The relationship be- 
tween frames is frequently hierarchical; for example, 
the frame elements BUYER, SELLER, PAYMENT, and 
GOODS will be common to all commercial transac- 
tions; the purchase of real estate contains all of them 
and (typically) adds a LOAN and a bank (typically) 
as LENDER. In Our database, these two frames might 
be represented as shown in Figure i. s 
Corpus tagging for a sentence like sentence (2): 
(2) Susan took out a huge mortgage to buy 
that new house. 
would have to recognize Susan as playing slightly 
different roles in the two associated frames. 
A similar problem in using labels from frame se- 
mantic descriptions in the tagging of corpus lines is 
due to the fact that separate parts of any single sen- 
tence can evoke different semantic frames. Consider 
the following sentence: 
(3) George's cousin bought a new Mercedes 
with her portion of the inheritance. 
In seeing this sentence merely as an expression evok- 
ing the commercial transaction frame, we could be- 
gin by tagging the subject of the sentence, "George's 
cousin", as the BUYER, and the object, "a new Met- 
cedes" as the GOODS, and the oblique object, "her 
portion of the inheritance", marked by the preposi- 
tion "with", as the PAYMENT. This could be done 
in a fairly natural and transparent way, as long as 
the tags were clearly seen as the names of frame ele- 
ments specifically related to the head verb "bought" 
in that sentence. But since the words "cousin" and 
"inheritance" evoke frames of their own, the same 
sentence could easily come up in our exploration of 
the semantics of those words as well. In the case 
of "inheritance", for example, the information that 
it gets used for buying something will make clear 
that this is an instance of estate-inheritance rather 
than genetic inheritance (or frame inheritance!), and 
the phrasing "her portion" fits frame understand- 
ings about the distribution of an inheritance among 
multiple heirs. In other words, if we find ourselves 
tagging the frame elements of Inheritance in that 
same sentence, the phrase "George's cousin" would 
be tagged as an HEIR in that frame. 
4 Applied frame semantics: a 
sample frame description. 
Tagsets for semantic annotation would be derivable 
from a database of frame descriptions like the ones 
in Figure 1 above. We can move to another frame 
to illustrate how frame-based annotation would be 
accomplished by considering a few words from the 
5We leave out of this account the inheritance of 
a higher-level EXCHANGE frame in the COMMERCIAL- 
TRANSACTION fralne, and the means for showing that 
a completed instance of the REALESTATETRANSACTION 
scene is a prerequisite to the enactment of the associated 
COMMERCIALTRANSACTION scene. 
20 
label meaning 
HEALER individual who tries to bring 
about an improvement in the 
PATIENT 
PATIENT individual whose physical well- 
being is low 
DISEASE sickness or health condition that 
needs to be removed or relieved 
WOUND tissue damage in the body of the 
PATIENT 
BODYPART limb, organ, etc. affected by the 
DISEASE or WOUND 
SYMPTOM evidence indicating the presence 
of the DISEASE 
TREATMENT process aimed at bringing about 
recovery 
MEDICINE substance applied or ingested in 
order to bring about recovery 
Table 1: Part of Frame-semantic "Tagset" for the 
Health Frame 
language of health and sickness and showing how the 
elements and structure of this frame would be iden- 
tiffed and described. First, appealing to common, 
unformalized knowledge of health and the body, the 
frame semanticist identifies the typical elements in 
everyday health care situations and scenarios, a pro- 
cess involving the interaction of linguistic intuition 
and the careful examination of corpus evidence. 
The first product of this analysis is a preliminary 
list of frame elements (FEN) from this domain, such 
as, for instance, those shown in Table 1. 
We have found it necessary to include all of these 
elements for our purposes, even though some of them 
are so closely related that they are unlikely to be 
given separate instantiation in the same clause. Our 
justification for distinguishing them is based on the 
results of corpus research and on comparison of the 
elements of this frame with those of other related 
frames. Corpus examples in which WOUND and DIS- 
EASE are both instantiated are of course rare, and 
given this complementary distribution we might be 
tempted to identify these as variants of a single 
frame element (which we might call AFFLICTION). 
But this would prevent us from being able to express 
certain syntactic and semantic generalizations, such 
as the fact that while we speak of curing diseases, 
we do not speak of curing wounds, and we speak of 
wounds but not diseases as heMing, s 
eThere might be alternative ways of considering such 
data. It is conceivable that a description with, say, AF- 
FLICTION as a single role element could be maintained 
In the specific case of the contrast between WOUND 
and DISEASE we find in metaphor further support 
for our decision to keep them separate. Metaphoric 
uses of "cure" and "heal" tend to take direct ob- 
jects which are target-domain analogues of DISEASE 
and WOUND respectively. One of the most com- 
mon instantiations of the DISEASE complement in 
metaphorical uses of cure is the word ills, a word 
which in fact appears to be used only in such 
metaphorical contexts (in talk about "curing soci- 
ety's ills", for example); and the direct objects of 
metaphorical heal tend to be based on the notion of 
a tear or cut or separation, the words wound and 
scar first of all, but also such words as r/ft, schism, 
and breach. 
For each semantic frame, the process of elucida- 
tion involves a series of steps: 
1. Identification of the most frequent lexical items 
which can serve as predicates in this frame, 
2. Formulation of a preliminary list of frame 
elements (encoded we expect as a TEL 
compliant SGML document using feature struc- 
tures (Sperberg-McQueen and Burnard, 1994), 
3. Annotation of examples from a corpus by tag- 
ging the predicate with the name of the frame 
and its arguments with the names of the FE's 
designating their roles relative to the predicate 
(also using SGML markup introduced with soft- 
ware developed for this purpose), 
4. Revision of the frame description -- specifica- 
tion of the co-occurrence constraints and pos- 
sible syntactic realizations in the light of the 
corpus data, and, 
5. Retagging of the corpus examples to fit the re- 
vised frames.7 
The last two steps will be repeated as needed to 
refine the frame description. 
by describing certain distinctions between "cure" and 
"heal" as involving selectionai restrictions. Our inclina- 
tion, however, is to maximize the separation of frame 
elements at the beginning, and to postpone the task of 
producing a parsimonious and redundancy-free descrip- 
tion until after we have completed our analysis. 
ZIn the context of the FrameNet project, the question 
of how much text will be tagged is a practical one. Our 
direct purpose is not to create tagged corpora, but to 
tag enough corpus lines to allow us to make reliable gen- 
eralizations on the meanings and on the semantic and 
syntactic valence of the lexical entries we have set out 
to describe. Whether we choose to tag more than what 
we need for our analysis will depend on the extent to 
which the process becomes automated and the resources 
available. 
21 
Identifying the semantic flame associated with a 
word and the FEs with which it constellates does 
not, of course, constitute a complete representation 
of the word's meaning, and our semantic descrip- 
tions will not be limited to just this. However, we 
believe that such an analysis is a prerequisite to a 
theoretically sound semantic formalization, s While 
any given frame description could be made more pre- 
cise for other NLP/AI purposes (such as inference- 
generation), the development of such a formalism is 
not a central part of our current work. 
For our present purposes, the adequacy of lists of 
frame elements such as what we present in Table 1 
for the vocabulary domain of health care can be es- 
tablished only if precisely these elements are the ones 
that are needed for distinguishing the semantic and 
combinatorial properties of the major lexical items 
that belong to that domain. An initial formulation 
of the combinatorial requirements and privileges of 
a frame's lexical members -- here we concentrate on 
verbs -- can be presented as a list of the groups of 
FEs that may be syntactically expressed or perhaps 
merely implied in the phrases that accompany the 
word. 
A Frame Element Group (FEG) is a list of the 
FEs from a given frame which occur in a phrase or 
sentence headed by a given word. Table 2 gives ex- 
amples of such FEGs (including FEGs with only one 
member) paired with sentences whose constituents 
instantiate them. For purposes of this discussion, 
the frame elements are identified here using single 
letter abbreviations, and the structure of an FEG is 
shown as being merely a bracketed list. We recog- 
nize such a naming scheme is inadequate for a large 
annotation project, and certainly the representation 
of FEG structures will have to be more powerful. 
These, however, are minor problems with technical 
solutions. We focus below on other major issues 
we are confronting in interpreting the structure of 
frames as expressed by FEGs. 
At the lexicographic level of description we could 
simply list the full set of FEGs for a given lexical 
unit. However, in many cases the FEG potential 
of a verb can be expressed in one or more simpli- 
fying formulas, by, for example, recognizing some 
FEs as optional. Thus, since we find both (H, B} 
("The doctor cured my foot") and {H, B, T} ("The 
doctor cured my foot with a new treatment"), both 
sentences are using the verb cure in the same sense, 
we can represent both patterns in a single formula 
that treats the T element as an optional adjunct 
SThere are numerous suggestions, not reviewed here, 
on how to give full semantic representations (Jackendoff, 
1994); (Sowa, 1984); (Schank, 1975), etc. 
FEG Frame Ele- Example 
(abbr.) ment Group 
{H,B,T} HEALER, The doctor treated 
BODYPART, my knee with heat. 
TREATMENT 
(H,D} HEALER, The doctor cured 
DISORDER my disease. 
{P} PATIENT The baby recovered. 
{M,B} MEDICINE, The ointment cured 
BODYPART my foot. 
{B} BODYPART HIS foot healed. 
{W} WOUND The cut rapidly 
healed. 
Table 2: Examples of Frame Element Groups 
(FEGs) 
(expressed perhaps as {H, B, (T)}). 
It will not be quite that automatic, however; fur- 
ther distinctions are needed. For example, while we 
can agree that the TREATMENT element in the previ- 
ous examples was merely unmentioned, the omission 
of the DISEASE element in a sentence like "The doc- 
tor cured me" has a somewhat different status: there 
is clearly some DISEASE that the speaker has in mind, 
and its omission is licensed by the assumption that 
its nature is given in the context. That is, a possible 
"of" phrase was omitted from that sentence because 
its content had been previously mentioned or could 
otherwise be assumed to be known to both conver- 
sation participants. In the tagging of corpus lines, 
then, we will also indicate the status of "missing" 
elements to the extent that we can tell what that 
is. Such information will be presented in the repre- 
sentation of the FEG associated with the predicate. 
9 
In contrast to cases where frame elements are 
"missing" (implied but unmentioned, optional, etc.), 
some examples require that we explicitly recognize 
(i.e. encode) multiple frame elements for a single 
constituent. Thus, the disorder may be identified in 
the description of the patient (e.g. leper, diabetic); 
we wish to annotate this constituent as Pd, which 
will be taken as indicating that the constituent sat- 
isfies the P role in the frame, but that it also secon- 
darily instantiates a D role, since these nouns des- 
ignate people who suffer specific diseases (leprosy, 
°Where feasible, because of our interest in sortal fea- 
tures of arguments, we will identify the nature of the 
missing element f~om the context. A similar issue arises 
in cases of anaphora; we may or may not resolve the 
anaphora's referent in the annotations, depending on 
practical considerations of time and effort involved. 
diabetes). It is important to recognize these cases, 
since the lexical semantics of verbs sometimes re- 
quire that certain frame elements be instantiated or 
clearly recoverable from the context: corpus research 
on the verb cure, for example, shows that the DIS- 
ORDER is regularly instantiated. Without explicit 
coding of the substructure of the PATIENT the sen- 
tence He cured the leper ({H,Pd}) would stand as a 
counter-example to this generalization. 
There are cases where different but related senses 
of a predicate have distinct FEG possibilities. For 
example, the verb heal has two uses, one of which 
participates in a Causative/Inchoative valency al- 
ternation (Levin, 1993) and one which does not. In 
the use where it refers to the growth of new tissue 
over a wound, it can be found in both transitive and 
intransitive clauses: "The cut healed" ({W}) and 
"The ointment healed the cut" (the ointment facil- 
itated the natural process of healing -- {M, W}). 
But there is also a purely transitive use with a mean- 
ing very close to that of cure, with {H, D} or {M, 
D}, as in "The shaman healed my influenza" or "The 
waters healed my arthritis", and this use of heal usu- 
ally implies something extra-medical or supernatu- 
ral. In this usage, there is no corresponding intran- 
sitive "*My influenza/arthritis healed." 
The verb sense distinctions we make may some- 
times be less detailed than those appearing in most 
dictionaries, since, as many researchers have noted, 
dictionary sense distinctions are often overprecise 
and incorporate pragmatic and world knowledge 
that do not properly speaking inhere in the word 
itself. An excellent example of this kind of excessive 
distinction ~ pointed out in (Ruhl, 1989), p.7: one 
of the dictionary definitions of break is "to rupture 
the surface of and permit flowing out or effusing" as 
in He broke an artery. On the other hand, we would 
expect to capture by this process all the kinds of al- 
ternations that (Levin, 1993) has shown to be linked 
to semantic distinctions, some of them quite subtle. 
The final versions of the lexical entries will encom- 
pass full semantic/syntactic valence descriptions, 
where the elements of each FEG associated with a 
verb sense will be linked to a specification of sortal 
.features, indicating the "selectional" and syntactic 
properties of the constituents that can instantiate 
them. 
5 Conclusion 
We have suggested a theoretical basis and a working 
methodology for coming up with an appropriate set 
of semantic tags for the semantic frame elements, 
and believe that such frames may constitute a sort 
of "basic level" of lexical semantic description. As 
22 
\[\] 
m 
\[\] 
mm 
U 
\[\] 
m 
m 
m 
such they would be an appropriate starting-point for 
both a broad-coverage semantic lexicon and for the 
semantic tagging of corpora. 
We have also pointed out the importance of incor- 
porating the notions of inheritance and other sub- 
structuring conventions in tagsets to reduce the size 
and complexity of the descriptions and to capture 
generalizations over natural classes. 
We recognize several shortcomings with our ap- 
proach which we hope to be able to address in the 
future. 
First, it is clear that the size of the descriptions 
will increase rapidly as the annotation proceeds and 
we will need to find some explicit means of abbrevi- 
ating representations, of collapsing FEGs in a prin- 
cipled way, and of relating frames together (both 
within and across semantic fields). This is both a 
practical and theoretical problem. We have shown 
a few clear examples in which the judicious use of 
the notion of inheritance, along the general lines 
of the ACQUILEX Project (Briscoe et al., 1993), 
should permit the concise representation of the lexi- 
cal knowledge required to give a useful and relatively 
complete description of a word's semantic range. If 
the valence description (the FEG together with links 
to grammatical functions) associated with individual 
words is attached to each valence-bearing lexical to- 
ken in a corpus, then if the corpus is parsed accord- 
ing to the same criteria by which the linking has 
been stated, we can avoid the problem of actually 
tagging the phrases that instantiate frame elements 
(and hence avoid the problem of multiple tagging 
for constituents that figure in more than one frame 
in the same sentence), because the constituents that 
play specific semantic roles in the sentence can be 
computed from the parse. The ability to accomplish 
something like that is desirable, but it is not some- 
thing to which we are presently committed. 
We intend first to focus on prototypical or core uses 
of the words. However, our preliminary research in- 
dicates that it would be difficult, and undesirable, 
to exclude metaphorical uses, if only because the 
metaphorical uses can often shed light on the struc- 
ture of the core uses. However, we are limiting our 
attention to a limited number of semantic domains, 
and metaphorical extensions from the words in our 
wordlist that go far beyond our semantic fields will 
probably have to be set aside. 
Finally, we should make a few remarks on the 
scope of our intended effort. We plan to create a 
"starter lexicon" containing some 5,000 lexical items 
indexed to examples of their use. With each entry 
we shall associate token frequencies with the various 
FEGs for each word sense, in order to assist NLP 
23 
programs in picking likely interpretations. Initially 
the frequencies would be generated using our hand- 
tagged corpus examples; eventually we hope to be 
able to train on the hand-tagged examples and ulti- 
mately automate (at least partially) the tagging of 
instances, at least for preliminary word sense dis- 
ambiguation, to be reviewed by a researcher. The 
automatic categorization of the arguments would 
use such information as WordNet synonyms and hy- 
pernyms (cf.(Resnik, 1993)), machine-readable the- 
sauri, etc., 

References 
Ted Briscoe, Valeria De Paiva, and Ann Copes- 
take, editors. 1993. Inheritance, Defaults and 
the Lexicon. Studies in Natural Language Pro- 
cessing. Cambridge University Press, Cambridge, 
England. 
Charles J. Fillmore and B.T.S. Atkins. 1992. To- 
wards a frame-based lexicon: the semantics of risk 
and its neighbors. In A. Lehrer and E. F. Kittay, 
editors, Prames, Fields and Contrasts, pages 75- 
102. Lawrence Erlbaum Associates, Hillsdale, NJ. 
Charles J. Fillmore and B.T.S. Atkins. 1994. Start- 
ing where the dictionaries stop: the challenge for 
computational lexicography. In B.T.S. Atkins and 
A. Zampolli, editors, Computational Approaches 
to the Lexicon. Oxford University Press, New 
York. 
Charles J. Fillmore. 1968. The case for case. In 
Universals in linguistic theory, pages 1-90. Holt, 
Rinehart and Winston, New York. 
Charles J. Fillmore. 1977a. The need for a frame 
semantics within linguistics, statistical Methods 
in Linguistics, pages 5-29. 
Charles J. Fillmore. 1977b. Scenes-and-frames se- 
mantics. In Antonio Zampolli, editor, Linguistics 
Structures Processing, volume 59 of Fundamental 
Studies in Computer Science, pages 55-82. North- 
Holland Publishing. 
Charles J. Fillmore. 1982. Frame semantics. In 
Linguistics in the morning calm, pages 111-137. 
Hanshin Publishing Co., Seoul, South Korea. 
Ray S. Jackendoff. 1994. Patterns in the mind: lan- 
guage and human nature. Basic Books, New York. 
Beth Levin. 1993. English Verb Classes and Alter- 
nations: A Preliminary Investigation. University 
of Chicago Press, Chicago. 
Philip Resnik. 1993. Selection and Information: 
A Class-Based Approach to Lexical relationships. 
University of Pennsylvania dissertation. 
Charles Ruhl. 1989. On monosemy : a study in lin- 
gusitic semantics. Albany, N.Y.: State University 
of New York Press. 
Roger C. Schank. 1975. Conceptual information 
processing. North-Holland., New York. 
John F. Sowa. 1984. Conceptual structures: infor- 
mation processing in mind and machine. Addison- 
Wesley systems programming series. Addison- 
Wesley, Reading, Mass. 
Michael Sperberg-McQueen and Lou Burnard. (eds.) 
1994. Guidefines for electronic text encoding 
and interchange (TEI P3). ACH, ACL, ALLC, 
Chicago. 
