VERB SEMANTICS AND LEXICAL SELECTION 
Zhibiao Wu 
Department of Information System 
& Computer Science 
National University of Singapore 
Republic of Singapore, 0511 
wuzhibia@iscs.nus.sg 
Martha Palmer 
Department of Computer and 
Information Science 
University of Pennsylvania 
Philadelphia, PA 19104-6389 
mpalmer@linc.cis.upenn.edu 
Abstract 
This paper will focus on the semantic representa- 
tion of verbs in computer systems and its impact 
on lexical selection problems in machine transla- 
tion (MT). Two groups of English and Chinese 
verbs are examined to show that lexical selec- 
tion must be based on interpretation of the sen- 
tence as well as selection restrictions placed on the 
verb arguments. A novel representation scheme 
is suggested, and is compared to representations 
with selection restrictions used in transfer-based 
MT. We see our approach as closely aligned with 
knowledge-based MT approaches (KBMT), and as 
a separate component that could be incorporated 
into existing systems. Examples and experimental 
results will show that, using this scheme, inexact 
matches can achieve correct lexical selection. 
Introduction 
The task of lexical selection in machine transla- 
tion (MT) is choosing the target lexical item which 
most closely carries the same meaning as the cor- 
responding item in the source text. Information 
sources that support this decision making process 
are the source text, dictionaries, and knowledge 
bases in MT systems. In the early direct replace- 
ment approaches, very little data was used for verb 
selection. The source verb was directly replaced by 
a target verb with the help of a bilingual dictio- 
nary. In transfer-based approaches, more informa- 
tion is involved in the verb selection process. In 
particular, the verb argument structure is used for 
selecting the target verb. This requires that each 
translation verb pair and the selection restrictions 
on the verb arguments be exhaustively listed in 
the bilingual dictionary. In this way, a verb sense 
is defined with a target verb and a set of selection 
restrictions on its arguments. Our questions are: 
Is the exhaustive listing of translation verb pairs 
feasible? Is this verb representation scheme suffi- 
cient for solving the verb selection problem? Our 
study of a particular MT system shows that when 
English verbs are translated into Chinese, it is dif- 
ficult to achieve large coverage by listing transla- 
tion pairs. We will show that a set of rigid se- 
lection restrictions on verb arguments can at best 
define a default situation for the verb usage. The 
translations from English verbs to Chinese verb 
compounds that we present here provide evidence 
of the reference to the context and to a fine-grained 
level of semantic representation. Therefore, we 
propose a novel verb semantic representation that 
defines each verb by a set of concepts in differ- 
ent conceptual domains. Based on this conceptual 
representation, a similarity measure can be defined 
that allows correct lexical choice to be achieved, 
even when there is no exact lexical match from 
the source language to the target language. 
We see this approach as compatible with other 
interlingua verb representation methods, such as 
verb representations in KBMT (Nirenburg,1992) 
and UNITRAN (Dorr, 1990). Since these methods 
do not currently employ a multi-domain approach, 
they cannot address the fine-tuned meaning dif- 
ferences among verbs and the correspondence be- 
tween semantics and syntax. Our approach could 
be adapted to either of these systems and incopo- 
rated into them. 
The limitations of direct transfer 
In a transfer-based MT system, pairs of verbs are 
exhaustively listed in a bilingual dictionary. The 
translation of a source verb is limited by the num- 
ber of entries in the dictionary. For some source 
verbs with just a few translations, this method is 
direct and efficient. However, some source verbs 
are very active and have a lot of different transla- 
tions in the target language. As illustrated by the 
following test of a commercial English to Chinese 
MT system, TranStar, using sentences from the 
Brown corpus, current transfer-based approaches 
have no alternative to listing every translation 
pair. 
In the Brown corpus, 246 sentences take break 
as the main verb. After removing most idiomatic 
133 
usages and verb particle constructions, there are 
157 sentences left. We used these sentences to test 
TranStar. The translation results are shown be- 
low: 
d=ui pohui ji&nxie 
to hreuk into pieces to na&ke d~m&ge to to h~ve • break 
5 2 JIl t 0 
juelie weifzn bsofL 
to bresk (8 rel~tlon) to ~g~inst to bresk out 
0 0 o 
f~henguzh~ng chu&nlu d~du~n 
to break down to bresh into to break & continuity 
tupo deshixi&nd&n weibel 
to break through to bre&k even with to bre&k (~ promise) 
o 
w~nchenjued~bufen 
to bre&k with 
In the TranStar system, English break only 
has 13 Chinese verb entries. The numbers above 
are the frequencies with which the 157 sentences 
translated into a particular Chinese expression. 
Most of the zero frequencies represent Chinese 
verbs that correspond to English break idiomatic 
usages or verb particle constructions which were 
removed. The accuracy rate of the translation is 
not high. Only 30 (19.1%) words were correctly 
translated. The Chinese verb ~7\]i~ (dasui) acts 
like a default translation when no other choice 
matches. 
The same 157 sentences were translated by 
one of the authors into 68 Chinese verb expres- 
sions. These expressions can be listed according 
to the frequency with which they occurred, in de- 
creasing order. The verb which has the highest 
rank is the verb which has the highest frequency. 
In this way, the frequency distribution of the two 
different translations can be shown below: 
Figure 1. Frequency distribution of translations 
It seems that the nature of the lexical selec- 
tion task in translation obeys Zipf's law. It means 
that, for all possible verb usages, a large portion 
is translated into a few target verbs, while a small 
portion might be translated into many different 
target verbs. Any approach that has a fixed num- 
ber of target candidate verbs and provides no way 
to measure the meaning similarity among verbs, 
is not able to handle the new verb usages, i.e., 
the small portion outside the dictionary cover- 
age. However, a native speaker has an unrestricted 
number of verbs for lexical selection. By measur- 
ing the similarities among target verbs, the most 
similar one can be chosen for the new verb usage. 
The challenge of verb representation is to capture 
the fluid nature of verb meanings that allows hu- 
man speakers to contrive new usages in every sen- 
tence. 
Translating English into Chinese 
serial verb compounds 
Translating the English verb break into Chinese 
(Mandarin) poses unusual difficulties for two rea- 
sons. One is that in English break can be thought 
of as a very general verb indicating an entire set of 
breaking events that can be distinguished by the 
resulting state of the object being broken. Shatter, 
snap, split, etc., can all be seen as more special- 
ized versions of the general breaking event. Chi- 
nese has no equivalent verb for indicating the class 
of breaking events, and each usage of break has to 
be mapped on to a more specialized lexical item. 
This is the equivalent of having to first interpret 
the English expression into its more semantically 
precise situation. For instance this would probably 
result in mapping, John broke the crystal vase, and 
John broke the stick onto John shattered the crys- 
tal vase and John snapped the stick. Also, English 
specializations of break do not cover all the ways 
in which Chinese can express a breaking event. 
But that is only part of the difficulty in trans- 
lation. In addition to requiring more semantically 
precise lexemes, Mandarin also requires a serial 
verb construction. The action by which force is 
exerted to violate the integrity of the object being 
broken must be specified, as well as the description 
of the resulting state of the broken object itself. 
Serial verb compounds in Chinese - Chinese 
serial verb compounds are composed of two Chi- 
nese characters, with the first character being a 
verb, and the second character being a verb or ad- 
jective. The grammatical analysis can be found in 
(Wu, 1991). The following is an example: 
Yuehan da-sui le huapin. 
John hit-broken Asp. vase. 
John broke the vase. (VA) 
Here, da is the action of John, sui is the result- 
ing state of the vase after the action. These two 
Chinese characters are composed to form a verb 
compound. Chinese verb compounds are produc- 
tive. Different verbs and adjectives can be com- 
posed to form new verb compounds, as in ilia, ji- 
sui, hit-being-in-pieces; or ilia, ji-duan, hit-being- 
in-line-shape. Many of these verb compounds have 
not been listed in the human dictionary. However, 
they must still be listed individually in a machine 
dictionary. Not any single character verb or single 
character adjective can be composed to form a VA 
type verb compound. The productive applications 
must be semantically sound, and therefore have to 
treated individually. 
134 
Inadequacy of selection restrictions for 
choosing actions - By looking at specific ex- 
amples, it soon becomes clear that shallow selec- 
tion restrictions give very little information about 
the choice of the action. An understanding of the 
context is necessary. 
For the sentence John broke the vase, a correct 
translation is: 
Yuehan da-sui le huapin. 
John hit-in-pieces Asp. vase. 
Here break is translated into a VA type verb 
compound. The action is specified clearly in 
the translation sentence. The following sentences 
which do not specify the action clearly are anoma- 
lous. , ~tr ~ T ~ 
Yuehan sui le huapin. 
John in-pieces Asp. vase. 
A translation with a causation verb is also 
anomalous: * ~ ~ ~t ~ T. 
Yuehan shi huapin sui le. 
John let vase in-pieces Asp. 
The following example shows that the trans- 
lation must depend on an understanding of the 
surrounding context. 
The earthquake shook the room violently, and 
the more fragile pieces did not hold up well. 
The dishes shattered, and the glass table was 
smashed into many pieces. 
Translation of last clause: 
na boli zhuozi bei zhenchen le euipian 
That glass table Pass. shake-become Asp. pieces 
Selection restrictions reliably choose result 
states - Selection restrictions are more reliable 
when they are used for specifying the result state. 
For example, break in the vase broke is translated 
into dasui (hit and broken into pieces), since the 
vase is brittle and easily broken into pieces. Break 
in the stick broke is translated into zheduan (bend 
and separated into line-segment shape) which is 
a default situation for breaking a line-segment 
shape object. However, even here, sometimes the 
context can override the selection restrictions on 
a particular noun. In John broke the stick into 
pieces, the obvious translation would be da sui in- 
stead. These examples illustrate that achieving 
correct lexical choice requires more than a simple 
matching of selection restrictions. A fine-grained 
semantic representation of the interpretation of 
the entire sentence is required. This can indicate 
the contextually implied action as well as the re- 
sulting state of the object involved. An explicit 
representation of the context is beyond the state 
of the art for current machine translation. When 
the context is not available, We need an algorithm 
for selecting the action verb. Following is a deci- 
sion tree for translating English Change-of-state 
verbs into Chinese: 
k, ti.m upremmi 
ia emt~ 
V .I. A ~ bs Ac~oo cu be inferred 
~,~,-~ \]ss.lcm o~ def~ ~clm ex~.s 
V t A wu:b but ud:cb 
aaa 
to Kleet vEb ~¢ifi~l 
U.. genre, ieti= gse carom 
h~=oa, (I=~, ¢j=) (=hi, ran, to ,=~.} 
Figure 2. Decision tree for translation 
A multi-domain approach 
We suggest that to achieve accurate lexical se- 
lection, it is necessary to have fine-grained selec- 
tion restrictions that can be matched in a flexible 
fashion, and which can be augmented when nec- 
essary by context-dependent knowledge-based un- 
derstanding. The underlying framework for both 
the selection restrictions on the verb arguments 
and the knowledge base should be a verb tax- 
onomy that relates verbs with similar meanings 
by associating them with the same conceptual do- 
mains. 
We view a verb meaning as a lexicalized con- 
cept which is undecomposable. However, this se- 
mantic form can be projected onto a set of con- 
cepts in different conceptual domains. Langacker 
(Langacker, 1988) presents a set of basic domains 
used for defining a knife. It is possible to define 
an entity by using the size, shape, color, weight, 
functionality etc. We think it is also possible to 
identify a compatible set of conceptual domains for 
characterizing events and therefore, defining verbs 
as well. Initially we are relying on the semantic 
domains suggested by Levin as relevant to syn- 
tactic alternations, such as motion, force, contact, 
change-of-state and action, etc, (Levin, 1992). We 
will augment these domains as needed to distin- 
guish between different senses for the achievment 
of accurate lexical selection. 
If words can be defined with concepts in a 
hierarchical structure, it is possible to measure 
the meaning similarity between words with an in- 
formation measure based on WordNet (Resnik, 
1993), or structure level information based on a 
thesaurus (Kurohashi and Nagao, 1992). How- 
ever, verb meanings are difficult to organize in a 
135 
hierarchical structure. One reason is that many 
verb meanings are involved in several different con- 
ceptual domains. For example, break identifies a 
change-of-state event with an optional causation 
conception, while hit identifies a complex event in- 
volving motion, force and contact domains. Those 
Chinese verb compounds with V + A construc- 
tions always identify complex events which involve 
action and change-of-state domains. Levin has 
demonstrated that in English a verb's syntactic 
behavior has a close relation to semantic com- 
ponents of the verb. Our lexical selection study 
shows that these semantic domains are also impor- 
tant for accurate lexical selection. For example, in 
the above decision tree for action selection, a Chi- 
nese verb compound dasui can be defined with a 
concept ~hit-action in an action domain and a 
concept ~separate-into-pieces in a change-of-state 
domain. The action domain can be further divided 
into motion, force, contact domains, etc. A related 
discussion about defining complex concepts with 
simple concepts can be found in (Ravin, 1990). 
The semantic relations of verbs that are relevant 
to syntactic behavior and that capture part of the 
similarity between verbs can be more closely re- 
alized with a conceptual multi-domain approach 
than with a paraphrase approach. Therefore we 
propose the following representation method for 
verbs, which makes use of several different con- 
cept domains for verb representation. 
Defining verb projections - Following is a rep- 
resentation of a break sense. LEXEME BREAK-I 
EXAMPLE I dropped my cup and it broke. 
CONSTRAINT (is-a physical-object El) 
(is-a animate-object EO) 
(is-a instrument E~) 
\[ ch.ofstate (~ehange-o\].integrity El) \] OBL 
OPT 
IMP 
causation (~cause EO *) 
instrument (~with-instrument EO E~ 
I time (~around-time @tO *) 
space (~at-location @10 EO) 
(~at-location 011 El) 
(~at-location @12 E2) 
I action @ 
L functionality @ 
The CONSTRAINT slot encodes the selection 
information on verb arguments, but the meaning 
itself is not a paraphrase. The meaning repre- 
sentation is divided into three parts. It identifies 
a %change-of-integrity concept in the change-of- 
state domain which is OBLIGATORY to the verb 
meaning. The causation and instrument domains 
are OPTIONAL and may be realized by syntactic 
alternations. Other time, space, action and func- 
tionality domains are IMPLICIT, and are neces- 
sary for all events of this type. 
In each conceptual domain, lexicalized con- 
cepts can be organized in a hierarchical struc- 
ture. The conceptual domains for English and 
Chinese are merged to form interlingua conceptual 
domains used for similarity measures. Following is 
part of the change-of-state domain containing En- 
glish and Chinese lexicalized concepts. 
c~tmp-, f-yatt, 
~pa~-h ~aM-h ~ka=In liu-~j~t pt~ ir~la:tkqm 
(C:du~,dltbu) (C:ni, l~jni) (C:p,y~po) 
Figure 3. Change-of-state domain for English and Chinese 
Within one conceptual domain, the similarity 
of two concepts is defined by how closely they are 
related in the hierarchy, i.e., their structural rela- 
tions. 
Figure 4. The concept similarity measure 
The conceptual similarity between C1 and C2 
is: 
ConSim(C1, C2) = 2,N3 Nl+N2+2*N3 
C3 is the least common superconcept of C1 
and C2. N1 is the number of nodes on the path 
from C1 to C3. N2 is the number of nodes on the 
path from C2 to C3. N3 is the number of nodes 
on the path from C3 to root. 
After defining the similarity measure in one 
domain, the similarity between two verb mean- 
ings, e. g, a target verb and a source verb, can 
be defined as a summation of weighted similari- 
ties between pairs of simpler concepts in each of 
the domains the two verbs are projected onto. 
WordSim(Vt, V2) = ~-\]~i Wl * ConSim(Ci,,, el,2) 
136 
UNICON: An implementation 
We have implemented a prototype lexical selec- 
tion system UNICON where the representations 
of both the English and Chinese verbs are based 
on a set of shared semantic domains. The selec- 
tion information is also included in these repre- 
sentations, but does not have to match exactly. 
We then organize these concepts into hierarchical 
structures to form an interlingua conceptual base. 
The names of our concept domain constitute the 
artificial language on which an interlingua must 
be based, thus place us firmly in the knowledge 
based understanding MT camp. (Goodman and 
Nirenburg, 1991). 
The input to the system is the source verb ar- 
gument structure. After sense disambiguation, the 
internal sentence representation can be formed. 
The system then tries to find the target verb real- 
ization for the internal representation. If the con- 
cepts in the representation do not have any target 
verb realization, the system takes nearby concepts 
as candidates to see whether they have target verb 
realizations. If a target verb is found, an inexact 
match is performed with the target verb mean- 
ing and the internal representation, with the se- 
lection restrictions associated with the target verb 
being imposed on the input arguments. Therefore, 
the system has two measurements in this inexact 
match. One is the conceptual similarity of the in- 
ternal representation and the target verb meaning, 
and the other is the degree of satisfaction of the 
selection restrictions on the verb arguments. We 
take the conceptual similarity, i.e., the meaning, as 
having first priority over the selection restrictions. 
A running example - For the English sentence 
The branch broke, after disambiguation, the inter- 
nal meaning representation of the sentence can be: 
\[ INTER-REP sentence-I \] 
ch-of-state (change-of-integrity branch-I) 
Since there is no Chinese lexicalized concept 
having an exact match for the concept change-of- 
integrity, the system looks at the similar concepts 
in the lattice around it. They are: 
(%SEPARAT E-IN-PIEC ES-STATE 
%SEPARATE-IN-NEEDLE-LIKE-STATE 
9~SEPARATE-IN-D UAN-STATE 
9~SEPARATE-IN-PO-STATE 
%SEPARATE-IN-SHANG-STATE 
%S EPARAT E-IN-F ENSUI-STAT E) 
For one concept %SEPARATE-IN-DUAN- 
STATE, there is a set of Chinese realizations: 
• ~-J~ dean la ( to separate in line-segment shape). 
• ~-1 da dean ( to hit and separate the object in line-segment 
shape). 
• ~ dean cheat ( to separate in li ..... gment shape into). 
• ~\]~ zhe duan ( to bend and separate in line-segment shape with 
human hands) 
• ~'~ gua dean ( to separate in line-segment shape by wind blow- 
ing). 
After filling the argument of each verb rep- 
resentation and doing an inexact match with the 
internal representation, the result is as.follows: 
conceptions 6/7 0 0 0 0 
constraints 3/14 0 3/7 0 0 
The system then chooses the verb ~-J" (duan 
la) as the target realization. 
Handling metaphorical usages - One test of 
our approach was its ability to match metaphorical 
usages, relying on a handcrafted ontology for the 
objects involved. We include it here to illustrate 
the flexibility and power of the similarity measure 
for handling new usages. In these examples the 
system effectively performs coercion of the verb 
arguments (Hobbs, 1986). 
The system was able to translate the following 
metaphorical usage from the Brown corpus cor- 
rectly. 
cfO9:86:No believer in the traditional devotion 
of royal servitors, the plump Pulley broke the 
language barrier and lured her to Cairo where 
she waited for nine months, vainly hoping to 
see Farouk. 
In our system, break has one sense which means 
loss of functionality. Its selection restriction is 
that the patient should be a mechanical device 
which fails to match language barrier. However, 
in our ontology, a language barrier is supposed to 
be an entity having functionality which has been 
placed in the nominal hierachy near the concept of 
mechanical-device. So the system can choose the 
break sense loss of functionality over all the other 
break senses as the most probable one. Based on 
this interpretation, the system can correctly se- 
lect the Chinese verb ?YM da-po as the target re- 
alization. The correct selection becomes possible 
because the system has a measurement for the de- 
gree of satisfaction of the selection restrictions. In 
another example, 
ca43:lO:Other tax-exempt bonds of State and 
local governments hit a price peak on Febru- 
ary P1, according to Standard gJ Poor's av- 
erage. 
hit is defined with the concepts %move-toward-in- 
space %contact-in-space %receive-fores. Since tar- 
exempt bonds and a price peak are not physical ob- 
jects, the argument structure is excluded from the 
HIT usage type. If the system has the knowledge 
that price can be changed in value and fixed at 
some value, and these concepts of change-in-value 
137 
and fix-at-value are near the concepts ~move- 
toward-in-space ~contact-in-space, the system can 
interpret the meaning as change-in.value and fix- 
at-value. In this case, the correct lexical selection 
can be made as Ik~ da-dao. This result is pred- 
icated on the definition of hit as having concepts 
in three domains that are all structurally related, 
i.e., nearby in the hierarchy, the concepts related 
to prices. 
Methodology and experimental 
results 
Our UNICON system translates a subset (the 
more concrete usages) of the English break verbs 
from the Brown corpus into Chinese with larger 
freedom to choose the target verbs and more ac- 
curacy than the TranStar system. Our coverage 
has been extended to include verbs from the se- 
mantically similar hit, touch, break and cut classes 
as defined by Beth Levin. Twenty-one English 
verbs from these classes have been encoded in the 
system. Four hundred Brown corpus sentences 
which contain these 21 English verbs have been se- 
lected, Among them, 100 sentences with concrete 
objects are used as training samples. The verbs 
were translated into Chinese verbs. The other 300 
sentences are divided into two test sets. Test set 
one contains 154 sentences that are carefully cho- 
sen to make sure the verb takes a concrete object 
as its patient. For test set one, the lexical selec- 
tion of the system got a correct rate 57.8% be- 
fore encoding the meaning of the unknown verb 
arguments; and a correct rate 99.45% after giving 
the unknown English words conceptual meanings 
in the system's conceptual hierarchy. The second 
test set contains 116 sentences including sentences 
with non-concrete objects, metaphors, etc. The 
lexical selection of the system got a correct rate 
of 31% before encoding the unknown verb argu- 
ments, a 75% correct rate after adding meanings 
and a 88.8% correct rate after extended selection 
process applied. The extended selection process 
relaxes the constraints and attempts to find out 
the best possible target verb with the similarity 
measure. 
From these tests, we can see the benefit of 
defining the verbs on several cognitive domains. 
The conceptual hierarchical structure provides a 
way of measuring the similarities among differ- 
ent verb senses; with relaxation, metaphorical pro- 
cessing becomes possible. The correct rate is im- 
proved by 13.8% by using this extended selection 
process. 
Discussion 
With examples from the translation of English to 
Chinese we have shown that verb semantic repre- 
sentation has great impact on the quality of lexical 
selection. Selection restrictions on verb arguments 
can only define default situations for verb events, 
and are often overridden by context information. 
Therefore, we propose a novel method for defin- 
ing verbs based on a set of shared semantic do- 
mains. This representation scheme not only takes 
care of the semantic-syntactic correspondence, but 
also provides similarity measures for the system 
for the performance of inexact matches based on 
verb meanings. The conceptual similarity has pri- 
ority over selection constrants on the verb argu- 
ments. We leave scaling up the system to future 
work. 

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