Proceedings of the COLING/ACL 2006 Main Conference Poster Sessions, pages 239–246,
Sydney, July 2006. c©2006 Association for Computational Linguistics
Robust Word Sense Translation by EM Learning of Frame Semantics   
Pascale Fung and Benfeng Chen 
Human Language Technology Center 
Department of Electrical & Electronic Engineering 
University of Science & Technology (HKUST) 
Clear Water Bay 
Hong Kong 
{pascale,bfchen}@ee.ust.hk 
Abstract 
We propose a robust method of auto-
matically constructing a bilingual word 
sense dictionary from readily available 
monolingual ontologies by using esti-
mation-maximization, without any an-
notated training data or manual tuning. 
We demonstrate our method on the 
English FrameNet and Chinese HowNet 
structures. Owing to the robustness of 
EM iterations in improving translation 
likelihoods, our word sense translation 
accuracies are very high, at 82% on av-
erage, for the 11 most ambiguous words 
in the English FrameNet with 5 senses 
or more. We also carried out a pilot 
study on using this automatically gener-
ated bilingual word sense dictionary to 
choose the best translation candidates 
and show the first significant evidence 
that frame semantics are useful for 
translation disambiguation. Translation 
disambiguation accuracy using frame 
semantics is 75%, compared to 15% by 
using dictionary glossing only.  These 
results demonstrate the great potential 
for future application of bilingual frame 
semantics to machine translation tasks.  
1 Introduction 
As early as in the 1950s, semantic nets were in-
vented as an “interlingua” for machine transla-
tion.  
The “semantic net” or “semantic map” that 
humans possess in the cognitive process is a 
structure of concept classes and lexicon (Illes and 
Francis 1999). In addition, the frame-semantic 
representation of predicate-argument relations 
has gained much attention in the research com-
munity. The Berkeley FrameNet (Baker et al. 
1998) is such an example.  
We suggest that in addition to dictionaries, bi-
lingual frame semantics (word sense dictionary) 
is a useful resource for lexical selection in the 
translation process of a statistical machine trans-
lation system. Manual inspection of the contras-
tive error analysis data from a state-of-the-art 
SMT system showed that around 20% of the er-
ror sentences produced could have been avoided 
if the correct predicate argument information was 
used (Och et al. 2003). Therefore, frame seman-
tics can provide another layer of translation dis-
ambiguation in these systems. 
We therefore propose to generate a bilingual 
frame semantics mapping (word sense diction-
ary), simulating the “semantic map” in a bilin-
gual speaker. Other questions of interest to us 
include how concept classes in English and Chi-
nese break down and map to each other.  
This paper is organized as follows. In section 
2, we present the one-frame-two-languages idea 
of bilingual frame semantics representation. In 
section 3, we explain the EM algorithm for gen-
erating a bilingual ontology fully automatically. 
In section 4, we present an evaluation on word 
sense translation. Section 5 describes an evalua-
tion on how well  bilingual frame semantics can 
improve translation disambiguation. We then 
discuss related work in section 6, conclude in 
section 7, and finally discuss future work in sec-
tion 8. 
2 One Frame Two Languages  
The challenge of translation disambiguation is to 
select the target word cl* with the correct seman-
tic frame f--(cl,f), among the multitude of transla-
tion candidates Pr(cl|el). We suggest that while a 
source word in the input sentence might have 
multiple translation candidates, the correct target 
word must have the same sense, i.e., belong to 
the same semantic frame, as the source word (i.e. 
Pr(cl,f|el,f) is high). For example, “burn| 烫
239
(tang)” carries the “ cause_harm|damage” 
sense, whereas “burn| 烧 (shao)” carries the 
“heat|cooking” sense. The source sentence “My 
hands are burned” has the 
“cause_harm|damage” sense, therefore the cor-
rect translation of “burn” is “烫(tang)” not 
“烧(shao)” . The frame semantics information 
of the source word can thus lead to the best trans-
lation candidate. 
Whereas some translation ambiguities are 
preserved over languages, most are not. In par-
ticular, for languages as different as English and 
Chinese, there is little overlap between how lexi-
con is broken-down (Ploux and Ji 2003). Some 
cognitive scientists suggest that a bilingual 
speaker tends to group concepts in a single se-
mantic map and simply attach different words in 
English and Chinese to the categories in this 
map.  
Based on the above, we propose the one-
frame-two-languages idea for constructing a bi-
lingual word sense dictionary from monolingual 
ontologies.  
FrameNet (Baker et al. 1998) is a collection of 
lexical entries grouped by frame semantics. Each 
lexical entry represents an individual word sense, 
and is associated with semantic roles and some 
annotated sentences. Lexical entries with the 
same semantic roles are grouped into a “frame” 
and the semantic roles are called “frame ele-
ments”. Each frame in FrameNet is a concept 
class and a single word sense belongs to only one 
frame. However, the Chinese HowNet represents 
a hierarchical view of lexical semantics in Chi-
nese.  
HowNet (Dong and Dong 2000) is a Chinese 
ontology with a graph structure of word senses 
called “concepts”, and each concept contains 7 
fields including lexical entries in Chinese, Eng-
lish gloss, POS tags for the word in Chinese and 
English, and a definition of the concept including 
its category and semantic relations (Dong and 
Dong, 2000). Whereas HowNet concepts corre-
spond roughly to FrameNet lexical entries, its 
semantic relations do not correspond directly to 
FrameNet semantic roles.  
A bilingual frame, as shown in Figure 1, 
simulates the semantic system of a bilingual 
speaker by having lexical items in two languages 
attached to the frame.   
3 Automatic Generation of Bilingual 
Frame Semantics 
To choose “burn|烫(tang)” instead of “burn|烧
(shao)” in the translation of “My hands are 
burned”, we need to know that  “烫(tang)” 
belongs to the “cause_harm” frame, but “ 烧
(shao)”  belongs to the “heat” frame. In other 
words, we need to have a bilingual frame seman-
tics ontology. Much like a dictionary, this bilin-
gual ontology forms part of the translation 
“lexicon”, and can be used either by human 
translators or automatic translation systems.  
Such a bilingual frame semantics ontology 
also provides a simulation of the “concept lexi-
con" of a bilingual person, as suggested by cog-
nitive scientists.  
Figure 1 shows an example of a bilingual 
frame that possibly corresponds to the semantic 
structure in a bilingual person. 
 
 
Figure 1. An example bilingual frame 
 
 
We previously proposed using the Chinese 
HowNet and a bilingual lexicon to map the Eng-
lish FrameNet into a bilingual BiFrameNet (Fung 
and Chen 2004). We used a combination of 
frame size thresholding and taxonomy distance 
to obtain the final alignment between FrameNet 
frames and HowNet categories, to generate the 
BiFrameNet.  
Our previous algorithm had the disadvantage 
of requiring the ad hoc tuning of thresholds. This 
results in poor performance on lexical entries 
from small frames (i.e. frames with very few 
lexical entries). The tuning process also means 
that a development set of annotated data is 
needed. In this paper, we propose a fully auto-
matic estimation-maximization algorithm in-
stead, to generate a similar FrameNet to HowNet 
240
bilingual ontology, without requiring any anno-
tated data or manual tuning. As such, our method 
can be applied to ontologies of any structure, and 
is not restricted to FrameNet or HowNet.  
Our approach is based on the following as-
sumptions: 
1. A source semantic frame is mapped to a tar-
get semantic frame if many word senses in 
the two frames translate to each other; 
2. A source word sense translates into a target 
word sense if their parent frames map to each 
other.  
The semantic frame in FrameNet is defined 
as a single frame, whereas in HowNet it is de-
fined as the category.  The formulae of our pro-
posed algorithm are listed in Figure 2.  
 
Variable definitions:  
cl : Chinese lexeme . 
cf : Chinese frame. 
(cl, cf) : the word sense entry in cf . 
el : English lexeme . 
ef : English frame.  
(el, ef) : the word sense entry in ef . 
(All variables are assumed to be independent of 
each other.) 
 
Model  parameters: 
Pr(cl|el): bilingual word pair probability from 
dictionary 
Pr(cf|ef): Chinese to English frame mapping 
probability. 
Pr(cl,cf|el,ef): Chinese to English word sense 
translation probability. 
 
(1) Word senses that belong to mapped frames  
are translated to each other: 
()
()
()
clcl
efelcfcl
efcfelcl
efelcfcl
cf
∀=
⋅
=
∑
∀
   1)Pr(
y   probabilit priori a  theassume  wewhere
,|,Pr
|Pr)|Pr(
,|,Pr
 
 
(2) Frames that have translated word senses 
are mapped to each other: 
∑∑∑
∑∑
∀∀∀
∀∀
=
cf el cl
el cl
efelcfcl
efelcfcl
efcf
),|,Pr(
),|,Pr(
)|Pr(
 
 
Figure 2. The bilingual frame semantics formulae 
 
In the initialization step of our EM algorithm, all 
English words in FrameNet are glossed into Chi-
nese using a bilingual lexicon with uniform 
probabilities Pr(cl|el). Next, we apply the EM 
algorithm to align FrameNet frames and HowNet 
categories. By using EM, we improve the prob-
abilities of frame mapping in Pr(cf|ef) and word 
sense translations in Pr(cl,cf|el,ef) iteratively: We 
estimate sense translations based on uniform bi-
lingual dictionary probabilities Pr(cl|el) first. 
The frame mappings are maximized by using the 
estimated sense translation. The a priori lexical 
probability Pr(cl) is assumed to be one for all 
Chinese words. Underlining the correctness of 
our algorithm, we note that the overall likeli-
hoods of the model parameters in our algorithm 
improve until convergence after 11 iterations. 
We use the alignment output after the conver-
gence step. That is, we obtain all word sense 
translations and frame mapping from the EM 
algorithm: 
 
()
efefcfcf
efelefelcfclcfcl
cf
cfcl
∀=
∀=
  )|Pr(maxarg*
),(   ,|,Prmaxarg)*,(
),(
 
 
The mapping between FrameNet frames and 
HowNet categories is obviously not one-to-one 
since the two languages are different. The initial 
and final mappings before and after EM itera-
tions are shown in Figures 3a,b and 4a,b. Each 
point (i,j) in Figures 3a and b represents an 
alignment between FrameNet frame i to HowNet 
category j.  Before EM iterations, each English 
lexical item is glossed into its (multiple) Chinese 
translations by a bilingual dictionary. The parent 
frame of the English lexical item and those of all 
its Chinese translations are aligned to form an 
initial mapping. This initial mapping shows that 
each English FrameNet frame is aligned to an 
average of 56 Chinese HowNet categories. This 
mapping is clearly noisy. After EM iterations, 
each English frame is aligned to 5 Chinese cate-
gories on average, and each Chinese category is 
aligned to 1.58 English frames on average. 
241
 
Figure 3a. FrameNet to HowNet mapping before EM 
iterations. 
 
 
Figure 3b. FrameNet to HowNet mapping after EM 
iterations. 
 
We also plot the histograms of one-to-X 
mapping between FrameNet frames and HowNet 
categories before and after EM iterations in Fig-
ure 4. The horizontal axis is the number X in 
one-to-X mapping between English and Chinese 
frames. The vertical axis is the occurrence fre-
quency. For example, point (i,j) represents that 
there are j frames in English mapping to i catego-
ries in Chinese. Figure 4 shows that using lexical 
glossing only, there are a large number of frames 
that are aligned to over 150 of Chinese catego-
ries, while only a small number of English 
frames align to relatively few Chinese categories. 
After EM iterations, the majority of the English 
frames align to only a few Chinese categories, 
significantly improving the frame mapping 
across the two languages.  
  
Figure 4a. Histogram of one-to-X mappings between 
English frames and Chinese categories. Most English 
frames align to a lot of Chinese categories before EM 
learning.    
 
 
Figure 4b. Histograms of one-to-X mappings between 
English frames and Chinese categories. Most English 
frames only align to a few Chinese categories after 
EM learning.  
 
The above plots demonstrate the difference 
between FrameNet and HowNet structures. For 
example, “boy.n” belongs to “attention_getting” 
and “people” frames in FrameNet. 
“boy.n|attention_getting” should translate into “
茶房 /waiter” in Chinese, whereas “boy.n|people” 
has the sense of “男孩/male child”. However, 
in HowNet, both “茶房 /waiter” and “男孩/ male 
child” belong to the same category, human|人.  
 
burn.v,cause_harm --> 辣.v,damage|损害   
burn.v,cause_harm --> 烫.v,damage|损害  
burn.v,cause_harm --> 嘘.v,damage|损害  
burn.v,cause_harm --> 蜇.v,damage|损害  
burn.v,experience_bodily_harm --> 烧
伤.v,wounded|受伤 
burn.v,heat --> 烧.v,cook|烹调  
 
Figure 5. Example word sense translation of the 
English verb “burn” in our bilingual frame se-
mantics mapping. 
 
242
An example of word sense translation from our 
algorithm output is shown in Figure 5. The word 
sense translations of the FrameNet lexical entries 
represent the simulated semantic world of a bi-
lingual person who uses the same semantic struc-
ture but with lexical access in two languages. For 
example, the frame “cause_harm” now contains 
the bilingual word sense pair 
“burn.v,cause_harm --> 辣.v,damage|损害 “; 
and the frame “experience_bodily_harm” con-
tains the bilingual word sense pair 
“burn.v,experience_bodily_harm --> 烧
伤.v,wounded|受伤”.   
4 Robust Word Sense Translation Using 
Frame Semantics 
We evaluate the accuracy of word sense transla-
tion in our automatically generated bilingual on-
tology,  by testing on the most ambiguous lexical 
entries in FrameNet, i.e. words with the highest 
number of frames. These words and some of 
their sense translations are shown in Table 1 be-
low.   
 
 
tie.n,clothing -> 襻.n,part|部件  
tie.v,cause_confinement -> 拘束.v,restrain|制止  
tie.v,cognitive_connection -> 联结.v,connect|连
接 
 
make.n,type -> 性质.n,attribute|属性  
make.v,building -> 建造.v,build|建造  
make.v,causation -> 令.v,CauseToDo|使动  
 
roll.v,body-movement -> 摇动.v,wave|摆动  
roll.v,mass_motion -> 翻滚.v,roll|滚  
roll.v,reshaping -> 卷.v,FormChange|形变 
 
feel.n,sensation -> 手感.n,experience|感受  
feel.v,perception_active -> 觉得.v,perception|感
知 
feel.v,seeking -> 摸.v,LookFor|寻 
 
Table 1. Example word sense translation out-
put 
 
The word sense translation accuracies of the 
above words are shown in Table 2. The results 
are highly positive given that those from previ-
ous work in word translation disambiguation us-
ing bootstrapping methods (Li and Li, 2003; 
Yarowsky 1995) achieved 80-90% accuracy in 
disambiguating between only two senses per 
word
1
.  
The only susceptibility of our algorithm is in 
its reliance on bilingual dictionaries. The sense 
translations of the words “tie”, “roll”, and “look” 
are relatively less accurate due to the absence of 
certain translations in the dictionaries we used. 
For example, the “bread/food” sense of the word 
“roll” is not found in the bilingual dictionaries at 
all.  
 
 
 
English 
word 
Number of 
frames/senses 
in FrameNet 
Sense 
translation 
accuracy 
tie 8 64% 
make 7 100% 
roll 6 55% 
feel 6 88% 
can 5 81% 
run 5 100% 
shower 5 100% 
burn 5 91% 
pack 5 85% 
drop 5 76% 
look 5 64% 
Average 5.6 82% 
Table 2. Translation accuracies of the most am-
biguous words in FrameNet 
 
We compare our results to that of our previ-
ous work (Fung and Chen 2004), by using the 
same bilingual lexicon. Table 3 shows that we 
have improved the accuracy of word sense trans-
lation using the current method.  
 
lexical 
entry 
Parent frame Accuracy 
(Fung & 
Chen 
2004) 
Accuracy
(this pa-
per) 
beat.v cause_harm 88.9% 100% 
move.v motion 100% 100% 
bright.a light_emission 79.1% 100% 
hold.v containing 22.4% 100% 
fall.v mo-
tion_directional 
87% 100% 
issue.v emanating 31.1% 100% 
Table 3. Our method improves word sense 
translation precision over Fung and Chen (2004).  
We note in particular that whereas the previ-
ous algorithm in Fung and Chen (2004) does not 
                                                           
1
 We are not able to evaluate our algorithm on the same 
set of words as in (Li & Li 2003; Yarowsky 1995) since 
these words do not have entries in FrameNet.  
 
243
perform well on lexical entries from small frames 
(e.g. on “hold.v” and “issue.v”) due to ad hoc 
manual thresholding, the current method is fully 
automatic and therefore more robust.  In Fung 
and Chen (2004), semantic frames are mapped to 
each other if their lexical entries translate to each 
other above a certain threshold. If the frames are 
small and therefore do not contain many lexical 
entries, then these frames might not be correctly 
mapped. If the parent concept classes are not cor-
rectly mapped, then word sense translation accu-
racy suffers.  
The main advantage of our algorithm over our 
work in 2004 lies in the hill-climbing iterations 
of the EM algorithm. In the proposed algorithm, 
all concept classes are mapped with a certain 
probability, so no mapping is filtered out prema-
turely. As the algorithm iterates, it is more prob-
able for the correct bilingual word sense to be 
translated to each other, and it is also more prob-
able for the bilingual concept classes to be 
mapped to each other. After convergence of the 
algorithm, the output probabilities are optimal 
and the translation results are more accurate.  
5 Towards Translation Disambiguation 
using Frame Semantics  
As translation disambiguation forms the core of 
various machine translation strategies, we are 
interested in studying whether the generated bi-
lingual frame semantics can complement existing 
resources, such as bilingual dictionaries, for 
translation disambiguation.  
The semantic frame of the predicate verb and 
the argument structures in a sentence can be 
identified by the syntactic structure, part-of-
speech tags, head word, and other features in the 
sentence. The predicate verb translation corre-
sponds to the word sense translation we de-
scribed in the previous sections, Pr(cl,cf | el,ef).   
We intend to evaluate the effectiveness of bi-
lingual frame semantics mapping in disambiguat-
ing between translation candidates. For the 
evaluation set, we use 202 randomly selected 
example sentences from FrameNet, which have 
been annotated with predicate-argument struc-
tures.  
In the first step of the experiment, for each 
predicate word (el,ef), we find all its translation 
candidates of the predicate word in each sen-
tence, and annotate them with their HowNet 
categories to form a translated word sense 
Pr(cl,cf|el,ef). For the example sentence in Fig-
ure 6, there are altogether 147 word sense trans-
lations for (hold,detaining).  
 
Under South African law police could HOLD the man 
for questioning for up to 48 hours before seeking the 
permission of magistrates for an extension 
 ##HOLD,detaining 
# 召开,engage|从事-- 
# 牵,guide|引导-- 
# 以为,regard|认为-- 
# 束缚,restrain|制止-- 
# 装,load|装载 -- 
# 装,pretend|假装-- 
# 手持,hold|拿-- 
… 
# 拿,hold|拿 -- 
# 拿,occupy|占领 -- 
#  握住,hold|拿 -- 
# 占,occupy|占领 -- 
… 
# 持,hold|拿 -- 
# 握,hold|拿 -- 
… 
# 操,hold|拿 -- 
# 操,speak|说 -- 
# 承,KeepOn|使继续 -- 
… 
# 开,function|活动 -- 
# 开,manage|管理 -- 
# 扣留,detain|扣住 ++  
# 要塞,facilities|设施 -- 
#  拥有,own|有 – 
 
Figure 6. A FrameNet example sentence and predicate verb 
translations {Pr(cl,cf|el,ef)}.  
 
We then find the word sense translation with 
the highest probability among all HowNet and 
FrameNet class mappings from our EM algo-
rithm: 
  
( )
()
()
∑
∀
⋅
=
=
cf
cl
cl
efelcfcl
efcfelcl
efelcfclcl
,|,Pr
|Pr)|Pr(
maxarg
,|,Prmaxarg
*
 
 An example (el,ef) is (hold, detaining) and the 
cl*=argmax P(cl,cf|el,ef) found by our program 
is 扣留 . (cl,cf)* in this case is (扣留,detain|扣住). 
Human evaluators then look at the set of {cl*} 
and mark cl* as either true translations or erro-
neous. The accuracy of word sense translations 
on this evaluation set of example sentences is at 
74.9%.  
In comparison, we also look at Pr(cl|el), trans-
lation based on bilingual dictionary only, and 
find 
244
() ( )elclelclcl
clcl
,Prmaxarg|Prmaxarg
*
==  
The translation accuracy of using bilingual 
dictionary only, is at a predictable low 15.8%.  
Our results are the first significant evidence 
of, in addition to bilingual dictionaries, bilingual 
frame semantics is a useful resource for the 
translation disambiguation task.  
6 Related Work 
The most relevant previous works include word 
sense translation and translation disambiguation 
(Li & Li 2003; Cao & Li 2002; Koehn and 
Knight 2000; Kikui 1999; Fung et al., 1999), 
frame semantic induction (Green et al., 2004; 
Fung & Chen 2004), and bilingual semantic 
mapping (Fung & Chen 2004; Huang et al. 2004; 
Ploux & Ji, 2003, Ngai et al., 2002; Palmer & 
Wu 1995).  Other than the English FrameNet 
(Baker et al, 1998), we also note the construction 
of the Spanish FrameNet (Subirats & Petruck, 
2003), the Japanese FrameNet (Ikeda 1998), and 
the German FrameNet (Boas, 2002). In terms of 
learning method, Chen and Palmer (2004) also 
used EM learning to cluster Chinese verb senses.  
Word Sense Translation  
Previous word sense translation methods are 
based on using context information to improve 
translation. These methods look at the context 
words and discourse surrounding the source 
word and use methods ranging from  boostrap-
ping (Li & Li 2003), EM iterations (Cao and Li, 
2002; Koehn and Knight 2000),  and the cohe-
sive relation between the source sentence and 
translation candidates (Fung et al. 1999; Kikui 
1999).   
Our proposed translation disambiguation 
method compares favorably to (Li & Li 2003) in 
that we obtain an average of 82% precision on 
words with multiple senses, whereas they ob-
tained precisions of 80-90% on words with two 
senses. Our results also compare favorably to 
(Fung et al. 1999; Kikui 1999) as the precision of 
our predicate verb in the input sentence transla-
tion disambiguation is about 75% whereas their 
precisions range from 40% to 80%, albeit on an 
independent set of words.  
Automatic Generation of Frame Semantics  
Green et al. (2004) induced SemFrame automati-
cally and compared it favorably to the hand-
constructed FrameNet (83.2% precision in cover-
ing the FrameNet frames). They map WordNet 
and LDOCE, two semantic resources, to obtain 
SemFrame. Burchardt et al. (2005) used Frame-
Net in combination with WordNet to extend cov-
erage. 
Bilingual Semantic Mapping 
Ploux and Ji, (2003) proposed a spatial model for 
matching semantic values between French and 
English. Palmer and Wu (1995) studied the map-
ping of change-of-state English verbs to Chinese. 
Dorr et al. (2002) described a technique for the 
construction of a Chinese-English verb lexicon 
based on HowNet and the English LCS Verb Da-
tabase (LVD). They created links between 
HowNet concepts and LVD verb classes using 
both statistics and a manually constructed “seed 
mapping” of thematic classes between HowNet 
and LVD. Ngai et al. (2002) induced bilingual 
semantic network from WordNet and HowNet. 
They used lexical neighborhood information in a 
word-vector based approach to create the align-
ment between WordNet and HowNet classes 
without any manual annotation. 
7 Conclusion 
Based on the one-frame-two-languages idea, 
which stems from the hypothesis of the mind of a 
bilingual speaker, we propose automatically gen-
erating a bilingual word sense dictionary or on-
tology. The bilingual ontology is generated from 
iteratively estimating and maximizing the prob-
ability of a word translation given frame map-
ping, and that of frame mapping given word 
translations. We have shown that for the most 
ambiguous 11 words in the English FrameNet, 
the average word sense translation accuracy is 
82%. Applying the bilingual ontology mapping 
to translation disambiguation of predicate verbs 
in another evaluation, the accuracy of our 
method is at an encouraging 75%, significantly 
better than the 15% accuracy of using bilingual 
dictionary only. Most importantly, we have dem-
onstrated that bilingual frame semantics is poten-
tially useful for cross-lingual retrieval, machine-
aided and machine translation.  
8 Future Work 
Our evaluation exercise has shown the promise 
of using bilingual frame semantics for translation 
task. We are currently carrying out further work 
in the aspects of (1) improving the accuracy of 
source word frame identification and (2) incorpo-
rating bilingual frame semantics in a full fledged 
245
machine translation system. In addition, Frame-
Net has a relatively poor coverage of lexical en-
tries. It would be necessary to apply either semi-
automatic or automatic methods such as those in 
(Burchardt et al. 2005, Green et al 2004) to ex-
tend FrameNet coverage for final application to 
machine translation tasks. Last but not the least, 
we are interested in applying our method to other 
ontologies such as the one used for the Propbank 
data, as well as to other language pairs.  
Acknowledgement 
This work was partially supported by CERG# 
HKUST6206/03E and CERG# HKUST6213/02E 
of the Hong Kong Research Grants Council. We 
thank Yang, Yongsheng for his help in the final 
draft of the paper, and the anonymous reviewers 
for their useful comments.  
References 
Collin F. Baker, Charles J. Fillmore and John B. 
Lowe. (1998).The Berkeley FrameNet project. 
In Proceedings of the COLING-ACL, Montreal, 
Canada.  
Hans C. Boas. (2002). Bilingual FrameNet Diction-
aries for Machine Translation. In Proceedings of 
the Third International Conference on Language 
Resources and Evaluation. Las Palmas, Spain. 
Vol. IV: 1364-1371 2002. 
A. Burchardt, K. Erk, A. Frank. (2005). A WordNet 
Detour to FrameNet.  In 
Proceedings of the 2nd GermaNet Workshop, 
2005. 
Cao, Yunbo and Hang Li. (2002). Base Noun 
Phrase Translation Using Web Data and the EM 
Algorithm. In COLING 2002. Taipei, 2002. 
Jinying Chen and Martha Palmer. (2004). Chinese 
Verb Sense Discrimination Using EM Clustering 
Model with Rich Linguistic Features. In ACL 
2004. Barcelona, Spain, 2004. 
Dong, Zhendong., and Dong, Qiang. (2002) 
HowNet [online]. Available at 
http://www.keenage.com/zhiwang/e_zhiwang.ht
ml 
Bonnie J. Dorr, Gina-Anne Levow, and Dekang 
Lin.(2002).Construction of a Chinese-English 
Verb Lexicon for Machine Translation. In Ma-
chine Translation, Special Issue on Embedded 
MT, 17:1-2.  
Pascale Fung and Benfeng Chen. (2004). BiFra-
meNet: Bilingual Frame Semantics Resource 
Construction by Cross-lingual Induction. In 
COLING 2004. Geneva, Switzerland. August 
2004. 
Pascale Fung, Liu, Xiaohu, and Cheung, Chi Shun. 
(1999). Mixed-Language Query Disambiguation. 
In Proceedings of ACL ‘99, Maryland: June 
1999. 
Daniel Gildea and Daniel Juraf-
sky.(2002).Automatic Labeling of Semantic 
Roles. In Computational Linguistics, Vol 28.3: 
245-288.  
Rebecca Green, Bonnie Dorr, Philip Resnik. 
(2004). Inducing Frame Semantic Verb Classes 
from WordNet and LDOCE. In ACL 2004.  
François Grosjean in Lesley Milroy and Pieter 
Muysken (editors). One Speaker, Two Lan-
guages. Cambridge University Press, 1995.  
Chu-Ren Huang, Ru-Yng Chang, Hsiang-Pin Lee. 
(2004).Sinica BOW (Bilingual Ontological 
Wordnet): Integration of Bilingual WordNet and 
SUMO. In Proceedings of the 4th International 
Conference on 
Language Resources and Evaluation, (2004). 
Judy Illes and Wendy S. Francis. (1999). Conver-
gent cortical representation of semantic process-
ing in bilinguals. In Brain and Language, 
70(3):347-363, 1999.  
Satoko Ikeda. (1998). Manual response set in a 
stroop-like task involving categorization of Eng-
lish and Japanese words indicates a common 
semantic representation. In Perceptual and Mo-
tor Skills, 87(2):467-474, 1998. 
Liu Qun and Li, Sujian. (2002). Word Similarity 
Computing Based on How-net. In Computa-
tional Linguistics and Chinese Language Proc-
essing，Vol.7, No.2, August 2002, pp.59-76 
Philipp Koehn and Kevin Knight. (2000). Estimat-
ing Word Translation Probabilities from Unre-
lated Monolingual Corpora Using the EM 
Algorithm. In AAAI/IAAI 2000: 711-715 
Grace Ngai, Marine Carpuat, and Pascale Fung. 
(2002). Identifying Concepts Across Languages: 
A First Step towards a Corpus-based Approach 
to Automatic Ontology Alignment. In Proceed-
ings of COLING-02, Taipei, Taiwan. 
Franz Och et al. (2003). 
http://www.clsp.jhu.edu/ws2003/groups/translate
/ 
Martha Palmer and Wu Zhibiao. (1995).Verb Se-
mantics for English-Chinese Translation. In Ma-
chine Translation 10: 59-92, 1995.  
Sabine Ploux and Hyungsuk Ji. (2003). A Model 
for Matching Semantic Maps between Lan-
guages (French/English, English/French). In 
Computational Linguistics 29(2):155-178, 2003. 
Carlos Subitrats and Miriam Petriuck. (2003). Su-
prirse: Spanish FrameNet. Workshop on Frame 
Semantics, International Congress of Linguists, 
July 2003. 
 
246
