Projecting Corpus-Based Semantic Links on a Thesaurus* 
Emmanuel Morin 
IRIN 
2, chemin de la housini~re - BP 92208 
44322 NANTES Cedex 3, FRANCE 
morin@irin, univ-nant es. fr 
Christian Jacquemin 
LIMSI-CNRS 
BP 133 
91403 ORSAY Cedex, FRANCE 
j acquemin@limsi, fr 
Abstract 
Hypernym links acquired through an infor- 
mation extraction procedure are projected on 
multi-word terms through the recognition of se- 
mantic variations. The quality of the projected 
links resulting from corpus-based acquisition is 
compared with projected links extracted from a 
technical thesaurus. 
1 Motivation 
In the domain of corpus-based terminology, 
there are two main topics of research: term 
acquisition--the discovery of candidate terms-- 
and automatic thesaurus construction--the ad- 
dition of semantic links to a term bank. Sev- 
eral studies have focused on automatic acquisi- 
tion of terms from corpora (Bourigault, 1993; 
Justeson and Katz, 1995; Daille, 1996). The 
output of these tools is a list of unstructured 
multi-word terms. On the other hand, contri- 
butions to automatic construction of thesauri 
provide classes or links between single words. 
Classes are produced by clustering techniques 
based on similar word contexts (Schiitze, 1993) 
or similar distributional contexts (Grefenstette, 
1994). Links result from automatic acquisi- 
tion of relevant predicative or discursive pat- 
terns (Hearst, 1992; Basili et al., 1993; Riloff, 
1993). Predicative patterns yield predicative re- 
lations such as cause or effect whereas discursive 
patterns yield non-predicative relations such as 
generic/specific or synonymy links. 
* The experiments presented in this paper were per- 
formed on \[AGRO\], a 1.3-million word French corpus of 
scientific abstracts in the agricultural domain. The ter- 
mer used for multi-word term acquisition is ACABIT 
(Daille, 1996). It has produced 15,875 multi-word terms 
composed of 4,194 single words. For expository pur- 
poses, some examples are taken from \[MEDIC\], a 1.56- 
million word English corpus of scientific abstracts in the 
medical domain. 
The main contribution of this article is to 
bridge the gap between term acquisition and 
thesaurus construction by offering a framework 
for organizing multi-word candidate terms with 
the help of automatically acquired links between 
single-word terms. Through the extraction of 
semantic variants, the semantic links between 
single words are projected on multi-word can- 
didate terms. As shown in Figure 1, the in- 
put to the system is a tagged corpus. A par- 
tial ontology between single word terms and 
a set of multi-word candidate terms are pro- 
duced after the first step. In a second step, 
layered hierarchies of multi-word terms are con- 
structed through corpus-based conflation of se- 
mantic variants. Even though we focus here on 
generic/specific relations, the method would ap- 
ply similarly to any other type of semantic re- 
lation. 
The study is organized as follows. First, the 
method for corpus-based acquisition of semantic 
links is presented. Then, the tool for semantic 
term normalization is described together with 
its application to semantic link projection. The 
last section analyzes the results on an agricul- 
tural corpus and evaluates the quality of the 
induced semantic links. 
2 Iterative Acquisition of Hypernym 
Links 
We first present the system for corpus-based in- 
formation extraction that produces hypernym 
links between single words. This system is built 
on previous work on automatic extraction of hy- 
pernym links through shallow parsing (Hearst, 
1992; Hearst, 1998). In addition, our system 
incorporates a technique for the automatic gen- 
eralization of lexico-syntactic patterns. 
As illustrated by Figure 2, the system has two 
functionalities: 
389 
/000000 
Termer ~-~0 • • • • • 
/ Multi-word terms 
Corpus 
Single word hierarchy 
Term 
norrnalizer 
Hierarchies of multi-word terms 
Figure 1: Overview of the system for hierarchy projection 
1. The corpus-based acquisition of lexico- 
syntactic patterns with respect to a specific 
conceptual relation, here hypernym. 
2. The extraction of pairs of conceptually re- 
lated terms through a database of lexico- 
syntactic patterns. 
Shallow Parser and Classifier 
A shallow parser is complemented with a classi- 
fier for the purpose of discovering new patterns 
through corpus exploration. This procedure in- 
spired by (Hearst, 1992; Hearst, 1998) is com- 
posed of 7 steps: 
1. Select manually a representative concep- 
tual relation, e.g. the hypernym relation. 
2. Collect a list of pairs of terms linked by 
the previous relation. This list of pairs of 
terms can be extracted from a thesaurus, a 
knowledge base or manually specified. For 
instance, the hypernym relation neocortex 
IS-A vulnerable area is used. 
3. Find sentences in which conceptually re- 
lated terms occur. These sentences are 
lemmatized, and noun phrases are iden- 
tified. They are represented as lexico- 
syntactic expressions. For instance, the 
previous relation HYPERNYM(vulnerable 
area, neocortex) is used to extract the 
sentence: Neuronal damage were found 
in the selectively vulnerable areas such as 
neocortex, striatum, hippocampus and tha- 
lamus from the corpus \[MEDIC\]. The sen- 
tence is then transformed into the following 
lexico-syntactic expression: 1 
NP find in NP such as LIST (1) 
1NP stands for a noun phrase, and LIST for a succes- 
sion of noun phrases. 
. Find a common environment that gener- 
alizes the lexicoosyntactic expressions ex- 
tracted at the third step. This environ- 
ment is calculated with the help of a func- 
tion of similarity and a procedure of gen- 
eralization that produce candidate lexico- 
syntactic pattern. For instance, from the 
previous expression, and at least another 
similar one, the following candidate lexico- 
syntactic pattern is deduced: 
NP such as LIST (2) 
5. Validate candidate lexico-syntactic pat- 
terns by an expert. 
6. Use these validated patterns to extract ad- 
ditional candidate pairs of terms. 
7. Validate candidate pairs of terms by an ex- 
pert, and go to step 3. 
Through this technique, eleven of the lexico- 
syntactic patterns extracted from \[AGRO\] are 
validated by an expert. These patterns are ex- 
ploited by the information extractor that pro- 
duces 774 different pairs of conceptually related 
terms. 82 of these pairs are manually selected 
for the subsequent steps our study because they 
are constructing significant pieces of ontology. 
They correspond to ten topics (trees, chemical 
elements, cereals, enzymes, fruits, vegetables, 
polyols, polysaccharides, proteins and sugars). 
Automatic Classification of 
Lexico-syntactic Patterns 
Let us detail the fourth step of the preceding 
algorithm that automatically acquires lexico- 
syntactic patterns by clustering similar pat- 
terns. 
390 
Corpus -~ Loxical preprocessor 
iBniT:Slp:iP:rs of terms~ 
~ Lemmadzed 
and tagged corpus ~ 
Database of 
lexico-syntactic patterns 
Shallow parser + classifier 
Information extractor 
Lexico-syntactic 
patterns 
Partial hierarchies 
of single-word terms 
J 
Figure 2: The information extraction system 
As described in item 3. above, pattern 
(1) is acquired from the relation HYPER- 
NYM( vulnerable area, neocortex ). Similarly, 
from the relation HYPERNYM(complication, 
infection), the sentence: Therapeutic 
complications such as infection, recurrence, 
and loss of support of the articular surface have 
continued to plague the treatment of giant cell 
tumor is extracted through corpus exploration. 
A second lexico-syntactic expression is inferred: 
NP such as LIST continue to plague NP (3) 
Lexico-syntactic expressions (1) and (3) can 
be abstracted as: 2 
A = AIA2 " • Aj • .. Ak • "An 
HYPERNYM(Aj, Ak), k > j + 1 
and 
(4) 
B : B1 B2 "" Bj .... B k .... B n, 
HYPERNYM(Bj,, B k,), k' > j' + 1 (5) 
Let Sire(A, B) be a function measuring the 
similarity of lexico-syntactic expressions A and 
B that relies on the following hypothesis: 
Hypothesis 2.1 (Syntactic isomorphy) 
If two lexico-syntactic expressions A and B 
represent the same pattern then, the items Aj 
and Bj,, and the items Ak and B k, have the 
same syntactic function. 
2Ai is the ith item of the lexico-syntactic expression 
A, and n is the number of items in A. An item can be 
either a lemma, a punctuation mark, a symbol, or a tag 
(N P, LIST, etc.). The relation k > j 4-1 states that there 
is at least one item between Aj and Ak. 
I winl(A) i wiFq_)ln2fA win3(A) I 
A = A1 A2 ...... Aj ... Ak ......... An 
B = B1 B2 ... Bj'. ....... Bk'... Bn' 
Figure 3: Comparison of two expressions 
Let Winl(A) be the window built from the 
first through j-1 words, Win2 (A) be the window 
built from words ranking from j+l th through k- 
lth words, and Win3(A) be the window built 
from k+lth through nth words (see Figure 3). 
The similarity function is defined as follows: 
3 
Sim(A, B) = E Sim(Wini(A), Wini(B)) (6) 
i=1 
The function of similarity between lexico- 
syntactic patterns Sim(Wini(A),Wini(B)) is 
defined experimentally as a function of the 
longest common string. 
After the evaluation of the similarity mea- 
sure, similar expressions are clustered. Each 
cluster is associated with a candidate pattern. 
For instance, the sentences introduced earlier 
generate the unique candidate lexico-syntactic 
pattern: 
NP such as LIST (7) 
We now turn to the projection of automat- 
ically extracted semantic links on multi-word 
terms. 3 
3For more information on the PROMI~THEE system, in 
391 
3 Semantic Term Normalization 
The 774 hypernym links acquired through the 
iterative algorithm described in the preceding 
section are thus distributed: 24.5% between two 
multi-word terms, 23.6% between two single- 
word terms, and the remaining ones between a 
single-word term and a multi-word term. Since 
the terms produced by the termer are only 
multi-word terms, our purpose in this section 
is to design a technique for the expansion of 
links between single-word terms to links be- 
tween multi-word terms. Given a link between 
fruit and apple, our purpose is to infer a simi- 
lar link between apple juice and fruit juice, be- 
tween any apple N and fruit N, or between ap- 
ple N1 and fruit N2 with N1 semantically related 
to N 2. 
Semantic Variation 
The extension of semantic links between sin- 
gle words to semantic links between multi-word 
terms is semantic variation and the process of 
grouping semantic variants is semantic normal- 
ization. The fact that two multi-word terms 
wlw2 and w 1~ w 2~ contain two semantically- 
related word pairs (wl,w~) and (w2,w~) does not 
necessarily entail that Wl w2 and w~ w~ are se- 
mantically close. The three following require- 
ments should be met: 
Syntactic isomorphy The correlated words 
must occupy similar syntactic positions: 
both must be head words or both must be 
arguments with similar thematic roles. For 
example, procddd d'dlaboration (process of 
elaboration) is not a variant dlaboration 
d'une mdthode (elaboration of a process) 
even though procddd and mdthode are syn- 
onymous, because procddd is the head word 
of the first term while mdthode is the argu- 
ment in the second term. 
Unitary semantic relationship The corre- 
lated words must have similar meanings 
in both terms. For example, analyse du 
rayonnement (analysis of the radiation) is 
not semantically related with analyse de 
l'influence (analysis of the influence) even 
particular a complete description of the generalization 
patterns process, see the following related publication: 
(Morin, 1999). 
though rayonnement and influence are se- 
mantically related. The loss of semantic 
relationship is due to the polysemy of ray- 
onnement in French which means influence 
when it concerns a culture or a civilization 
and radiation in physics. 
Holistic semantic relationship The third 
criterion verifies that the global meanings 
of the compounds are close. For example, 
the terms inspection des aliments (food 
inspection) and contrSle alimentaire (food 
control) are not synonymous. The first one 
is related to the quality of food and the 
second one to the respect of norms. 
The three preceding constraints can be trans- 
lated into a general scheme representing two 
semantically-related multi-word terms: 
Definition 3.1 (Semantic variants) Two 
multi-word terms Wl W2 and W~l w~2 are semantic 
variants of each other if the three following 
constraints are satisfied: 4 
1. wl and Wll are head words and w2 and wl2 
are arguments with similar thematic roles. 
2. Some type of semantic relation $ holds be- 
tween Wl and w~ and/or between w2 and 
wl2 (synonymy, hypernymy, etc.). The non 
semantically related words are either iden- 
tical or morphologically related. 
3. The compounds wl w2 and Wrl wt2 are also 
linked by the semantic relation S. 
Corpus-based Semantic Normalization 
The formulation of semantic variation given 
above is used for corpus-based acquisition of 
semantic links between multi-word terms. For 
each candidate term Wl w2 produced by the ter- 
mer, the set of its semantic variants satisfying 
the constraints of Definition 3.1 is extracted 
from a corpus. In other words, a semantic 
normalization of the corpus is performed based 
on corpus-based semantic links between single 
words and variation patterns defined as all the 
4wl w2 is an abbreviated notation for a phrase that 
contains the two content words wl and w2 such that one 
of both is the head word and the other one an argument. 
For the sake of simplicity, only binary terms are consid- 
ered, but our techniques would straightforwardly extend 
to n-ary terms with n > 3. 
392 
licensed combinations of morphological, syntac- 
tic and semantic links. 
An exhaustive list of variation patterns is pro- 
vided for the English language in (Jacquemin, 
1999). Let us illustrate variant extraction on a 
sample variation: 5 
Nt Prep N2 -+ 
M(N1,N) Adv ? A ? Prep_Ar.t ? A ? S(N2) 
Through this pattern, a semantic variation is 
found between composition du fruit (fruit com- 
position) and composgs chimiques de la graine 
(chemical compounds of the seed). It relies on 
the morphological relation between the nouns 
composg (compound, .h4(N1,N)) and composi- 
tion (composition, N1) and on the semantic 
relation (part/whole relation) between graine 
(seed, S(N2)) and fruit (fruit, N2). In addition 
to the morphological and semantic relations, the 
categories of the words in the semantic variant 
composdsN chimiquesA deprep laArt graineN sat- 
isfy the regular expression: the categories that 
are realized are underlined. 
Related Work 
Semantic normalization is presented as semantic 
variation in (Hamon et al., 1998) and consists 
in finding relations between multi-word terms 
based on semantic relations between single-word 
terms. Our approach differs from this preceding 
work in that we exploit domain specific corpus- 
based links instead of general purpose dictio- 
nary synonymy relationships. Another origi- 
nal contribution of our approach is that we ex- 
ploit simultaneously morphological, syntactic, 
and semantic links in the detection of semantic 
variation in a single and cohesive framework. 
We thus cover a larger spectrum of linguistic 
phenomena: morpho-semantic variations such 
as contenu en isotope (isotopic content) a vari- 
ant of teneur isotopique (isotopic composition), 
syntactico-semantic variants such as contenu en 
isotope a variant of teneur en isotope (isotopic 
content), and morpho-syntactico-semantic vari- 
ants such as duretd de la viande (toughness of 
the meat) a variant of rdsistance et la rigiditd 
de la chair (lit. resistance and stiffness of the 
flesh). 
5The symbols for part of speech categories are N 
(Noun), A (Adjective), Art (Article), Prep (Preposition), 
Punc (Punctuation), Adv (Adverb). 
4 Projection of a Single Hierarchy 
on Multi-word Terms 
Depending on the semantic data, two modes 
of representation are considered: a link mode 
in which each semantic relation between two 
words is expressed separately, and a class 
mode in which semantically related words are 
grouped into classes. The first mode corre- 
sponds to synonymy links in a dictionary or 
to generic/specific links in a thesaurus such as 
(AGROVOC, 1995). The second mode corre- 
sponds to the synsets in WordNet (Fellbaum, 
1998) or to the semantic data provided by the 
information extractor. Each class is composed 
of hyponyms sharing a common hypernym-- 
named co-hyponyms--and all their common hy- 
pernyms. The list of classes is given in Table 1. 
Analysis of the Projection 
Through the projection of single word hierar- 
chies on multi-word terms, the semantic relation 
can be modified in two ways: 
Transfer The links between concepts (such as 
fruits) are transferred to another concep- 
tual domain (such as juices) located at a 
different place in the taxonomy. Thus the 
link between fruit and apple is transferred 
to a link between fruit juice and apple juice, 
two hyponyms of juice. This modification 
results from a semantic normalization of ar- 
gument words. 
Specialization The links between concepts 
(such as fruits) are specialized into parallel 
relations between more specific concepts lo- 
cated lower in the hierarchy (such as dried 
fruits). Thus the link between fruit and 
apple is specialized as a link between dried 
fruits and dried apples. This modification 
is obtained through semantic normalization 
of head words. 
The Transfer or the Specialization of a given 
hierarchy between single words to a hierarchy 
between multi-word terms generally does not 
preserve the full set of links. In Figure 4, the 
initial hierarchy between plant products is only 
partially projected through Transfer on juices 
or dryings of plant products and through Spe- 
cialization on fresh and dried plant products. 
Since multi-word terms are more specific than 
393 
Table 1: The twelve semantic classes acquired from the \[AGRO\] corpus 
Classes Hypernyrns and cc~hyponyms 
trees 
chemical elements 
cereals 
enzymes 
fruits 
olives 
apples 
vegetables 
polyols 
polysacchaxides 
proteins 
sugars 
arbre, bouleau, chine, drable, h~tre, orme, peuplier, pin, poirier, pommier, sap)n, dpicda 
dldment, calcium, potassium, magndsium, mangandse, sodium, arsenic, chrome, mercure, 
sdldnium, dtain, aluminium, fer, cad)urn, cuivre 
cdrdale, mais, mil, sorgho, bld, orge, riz, avoine 
enzyme, aspaxtate, lipase, protdase 
fruit, banane, cerise, citron, figue, fraise, kiwi, no)x, olive, orange, poire, pomme, p~che, raisin 
fruit, olive, Amellau, Chemlali, Chdtoui, Lucques, Picholine, Sevillana, Sigoise 
fruit, pomme, Caxtland, Ddlicious, Empire, McIntoch, Spartan 
ldgume, asperge, carotte, concombre, haricot, pois, tomate 
polyol, glycdrol, sorbitol 
polysaccharide, am)don, cellulose, styrene, dthylbenz~ne 
protdine, chitinase, glucanase, thaumatin-like, fibronectine, glucanase 
sucre, lactose, maltose, raffinose, glucose, saccharose 
p(roduit v~g~tal plant products) 
cH~ale ~pice fruit l~gurae (cereal) (spice) (fruit) (vegetable) 
ma)~ or e tomate endive (maize) (b~y) (tomatoes) (chicory) 
fruit a noyau fruit ~ p~pins petit fruit (stone frmts) (point fruits) (soft tnlits) 
(apples) (pears) (grapes) ~ (strawberries) 
abricot cassis (apricots) 
" (black currants) 
Specialization "~k 
Specialization 
Transfer .,~ 1 "~ fruit frais Idgume frais fruit sec sdchage de c~r~ale \] s~chage de I~gume (fresh fruits) (fresh vegetables) (dried/~ruits) 
jus de.fruit (cereal drying) V (vegetable drying) /\ (fruit juice) 
....... ~ .... I ~ sdchagedecarotte fi~u~ee:~Cgsh~ 
• a=~,~sc ~c .~,a carrot m Jus de ananas . . ,.o~.~ ~'~7.'~ ~ / "N~ ( dry" g) 
(ananas juice) /\ \ ........ "'~" V ~/ "% / x k .... F sdcha~e de la banane raisinfrais raisin sec 
j \ ~ secnage ae nz X'anana d in ~ P \ ju~ de raisin (rice drying) \ W ry g, (fresh grapes) (dried grapes) 
jusdepomme \ (grape juice) \ (apple juice) ~ 
jus de poire sdchage de l'abricot (peat juice) (apricot drying) 
Figure 4: Projected links on multi-word terms (the hieraxchy is extracted from (AGROVOC, 1995)) 
single-word terms, they tend to occur less fre- 
quently in a corpus. Thus only some of the pos- 
sible projected links axe observed through cor- 
pus exploration. 
5 Evaluation 
Projection of Corpus-based Links 
Table 2 shows the results of the projection of 
corpus-based links. The first column indicates 
the semantic class from Table 1. The next 
394 
three columns indicate the number of multi- 
word links projected through Specialization, the 
number of correct links and the corresponding 
value of precision. The same values are pro- 
vided for Transfer projections in the following 
three columns. 
Transfer projections are more frequent (507 
links) than Specializations (77 links). Some 
classes, such as chemical elements, cereals and 
fruits are very productive because they are com- 
posed of generic terms. Other classes, such as 
trees, vegetables, polyols or proteins, yield few 
semantic variations. They tend to contain more 
specific or less frequent terms. 
The average precision of Specializations is 
relatively low (58.4% on average) with a high 
standard deviation (between 16.7% and 100%). 
Conversely, the precision of Transfers is higher 
(83.8% on average) with a smaller standard 
deviation (between 69.0% and 100%). Since 
Transfers are almost ten times more numer- 
ous than Specializations, the overall precision 
of projections is high: 80.5%. 
In addition to relations between multi-word 
terms, the projection of single-word hierar- 
chies on multi-word terms yields new candidate 
terms: the variants of candidate terms produced 
at the first step. For instance, sdchage de la 
banane (banana drying) is a semantic variant 
of sdchage de fruits (fruit drying) which is not 
provided by the first step of the process. As 
in the case of links, the production of multi- 
word terms is more important with Transfers 
(72 multi-word terms) than Specializations (345 
multi-word terms) (see Table 3). In all, 417 rele- 
vant multi-word terms are acquired through se- 
mantic variation. 
Comparison with AGROVOC Links 
In order to compare the projection of corpus- 
based links with the projection of links ex- 
tracted from a thesaurus, a similar study was 
made using semantic links from the thesaurus 
(AGROVOC, 1995). 6 
The results of this second experiment are very 
similar to the first experiment. Here, the preci- 
6(AGROVOC, 1995) is composed of 15,800 descrip- 
tors but only single-word terms found in the corpus 
\[AGRO\] are used in this evaluation (1,580 descriptors). 
From these descriptors, 168 terms representing 4 topics 
(cultivation, plant anatomy, plant products and flavor- 
ings) axe selected for the purpose of evaluation. 
sion of Specializations is similar (57.8% for 45 
links inferred), while the precision of Transfers 
is slightly lower (72.4% for 326 links inferred). 
Interestingly, these results show that links re- 
sulting from the projection of a thesaurus have 
a significantly lower precision (70.6%) than pro- 
jected corpus-based links (80.5%). 
A study of Table 3 shows that, while 197 
projected links are produced from 94 corpus- 
based links (ratio 2.1), only 88 such projected 
links are obtained through the projection of 
159 links from AGROVOC (ratio 0.6). Ac- 
tually, the ratio of projected links is higher 
with corpus-based links than thesaurus links, 
because corpus-based links represent better the 
ontology embodied in the corpus and associate 
more easily with other single word to produce 
projected hierarchies. 
6 Perspectives 
Links between single words projected on multi- 
word terms can be used to assist terminologists 
during semi-automatic extension of thesauri. 
The methodology can be straightforwardly ap- 
plied to other conceptual relations such as syn- 
onymy or meronymy. 
Acknowledgement 
We are grateful to Ga~l de Chalendar (LIMSI), 
Thierry Hamon (LIPN), and Camelia Popescu 
(LIMSI & CNET) for their helpful comments 
on a draft version of this article. 

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