Evaluation of Semantic Clusters 
Rajeev Agarwal 
Mississippi State University 
Mississippi State, MS 39762 
USA 
rajeev@cs.msstate.edu 
Abstract 
Semantic clusters of a domain form an 
important feature that can be useful for 
performing syntactic and semantic disam- 
biguation. Several attempts have been 
made to extract the semantic clusters of a 
domain by probabilistic or taxonomic tech- 
niques. However, not much progress has 
been made in evaluating the obtained se- 
mantic clusters. This paper focuses on an 
evaluation mechanism that can be used to 
evaluate semantic clusters produced by a 
system against those provided by human 
experts. 
1 Introduction 1 
Most natural language processing (NLP) systems are 
designed to work on certain specific domains and 
porting them to other domains is often a very time- 
consuming and human-intenslve process. As the 
need for applying NLP systems to more and var- 
ied domains grows, it becomes increasingly impor- 
tant that some techniques be used to make these 
systems more portable. Several researchers (Lang 
and Hirschman, 1988; Rau et al., 1989; Pustejovsky, 
1992; Grishman and Sterling, 1993; Basili et al., 
1994), either directly or indirectly, have addressed 
issues that assist in making it easier to move an 
NLP system from one domain to another. One of 
the reasons for the lack of portability is the need for 
domain-specific semantic features that such systems 
often use for lexical, syntactic, and semantic disam- 
biguation. One such feature is the knowledge of the 
semantic clusters in a domain. 
Since semantic classes are often domain-specific, 
their automatic acquisition is not trivial. Such 
classes can be derived either by distributional means 
or from existing taxonomies, knowledge bases, dic- 
tionaries, thesauruses, and so on. A prime exam- 
ple of the latter is WordNet which has been used to 
1The author is currently at Texas Instruments and all 
inquiries should be addressed to rajeev@csc.ti.com. 
provide such semantic classes (Resnik, 1993; Basili 
et al., 1994) to assist in text understanding. Our 
efforts to obtain such semantic clusters with limited 
human intervention have been described elsewhere 
(Agarwal, 1995). This paper concentrates on the 
aspect of evahiating the obtained clusters against 
classes provided by human experts. 
2 The Need 
Although there has been a lot of work done in ex- 
tracting semantic classes of a given domain, rela- 
tively little attention has been paid to the task of 
evaluating the generated classes. In the absence of 
an evaluation scheme, the only way to decide if the 
semantic classes produced by a system are "reason- 
able" or not is by having an expert analyze them by 
inspection. Such informal evaluations make it very 
difficult to compare one set of classes against an- 
other and are also not very reliable estimates of the 
quality of a set of classes. It is clear that a formal 
evaluation scheme would be of great help. 
Hatzivassiloglou and McKeown (1993) duster ad- 
jectives into partitions and present an interest- 
ing evaluation to compare the generated adjective 
classes against those provided by an expert. Their 
evaluation scheme bases the comparison between 
two classes on the presence or absence of pairs of 
words in them. Their approach involves filling in a 
YES-NO contingency table based on whether a pair 
of words (adjectives, in their case) is classified in the 
same class by the human expert and by the system. 
This method works very well for partitions. How- 
ever, if it is used to evaluate sets of classes where the 
classes may be potentiaily overlapping, their tech- 
nique yields a weaker measure since the same word 
pair could possibly be present in more than one class. 
An ideal scheme used to evaluate semantic classes 
should be able to handle overlapping classes (as o1>. 
posed to partitions) as well as hierarchies. The tech- 
nique proposed by Hatzivassiloglou and McKeown 
does not do a good job of evaluating either of these. 
In this paper, we present an evaluation methodology 
which makes it possible to properly evaluate over- 
284 
Table 1: Two Example Classes 
Class A Class B 
(System) (Expert) 
cat 
dog 
stomach 
pig 
COW 
hair 
cattle 
goat 
horse 
COW 
cat 
pig 
lamb 
dog 
sheep 
mare 
cattle 
swine 
goat 
lapping classes. Our scheme is also capable of in- 
corporating hierarchies provided by an expert into 
the evaluation, but still lacks the ability to compare 
hierarchies against hierarchies. 
In the discussion that follows, the word "cluster- 
ing" is used to refer to the set of classes that may 
be either provided by an expert or generated by the 
system, and the word "class" is used to refer to a 
single class in the clustering. 
3 Evaluation Approach 
As mentioned above, we intend to be able to com- 
pare a clustering generated by a system against one 
provided by an expert. Since a word can occur in 
more than one class, it is important to find some 
kind of mapping between the classes generated by 
the system and the classes given by the expert. Such 
a mapping tells us which class in the system's clus- 
tering maps to which one in the expert's clustering, 
and an overall comparison of the clusterings is based 
on the comparison of the mutually mapping classes. 
Before we delve deeper into the evaluation pro- 
cess, we must decide on some measure of "closeness" 
between a pair of classes. We have adopted the 
F-measure (Hatzivassiloglou and McKeown, 1993; 
Chincor, 1992). In our computation of the F- 
measure, we construct a contingency table based 
on the presence or absence of individual elements 
in the two classes being compared, as opposed to 
basing it on pairs of words. For example, suppose 
that Class A is generated by the system and Class B 
is provided by an expert (as shown in Table 1). The 
contingency table obtained for this pair of classes is 
shown in Table 2. 
The three main steps in the evaluation process are 
the acquisition of "correct" classes from domain ex- 
perts, mapping the experts' clustering to that gener- 
ated by the system, and generating an overall mea- 
sure that represents the system's performance when 
compared against the expert. 
Table 2: Contingency Table for Classes A and B 
System- NO 5 0 
3.1 Knowledge Acquisition from Experts 
The objective of this step is to get human experts to 
undertake the same task that the system performs, 
i.e., classifying a set of words into several potentially 
overlapping classes. The classes produced by a sys- 
tem are later compared to these "correct" classifica- 
tions provided by the expert. 
3.2 Mapping Algorithm 
In order to determine pairwise mappings between 
the clustering generated by the system and one pro- 
vided by an expert, a table of F-measures is con- 
structed, with a row for each class generated by the 
system, and a column for every class provided by the 
expert. Note that since the expert actually provides 
a hierarchy, there is one column corresponding to 
every individual class and subclass provided by the 
expert. This allows the system's classes to map to 
a class at any level in the expert's hierarchy. This 
table gives an estimate of how well each class gen- 
erated by the system maps to the ones provided by 
the expert. 
The algorithm used to compute the actual map- 
pings from the F-measure table is briefly described 
here. In each row of the table, mark the cell with the 
highest F-measure as a potential mapping. In gen- 
eral, conflicts arise when more than one class gener- 
ated by the system maps to a given class provided 
by the expert. In other words, whenever a column 
in the table has more than one cell marked as a po- 
tential mapping, a conflict is said to exist. To re- 
solve a conflict, one of the system classes must be 
re-mapped. The heuristic used here is that the class 
for which such a re-mapping results in minimal loss 
of F-measure is the one that must be re-mapped. 
Several such conflicts may exist, and re-mapping 
may lead to further conflicts. The mapping algo- 
rithm iteratively searches for conflicts and resolves 
them till no more conflicts exist. Note also that a 
system class may map to an expert class only if the 
F-measure between them exceeds a certain threshold 
value. This ensures that a certain degree of similar- 
ity must exist between two classes for them to map 
to each other. We have used a threshold value of 
0.20. This value is obtained purely by observations 
made on the F-measures between different pairs of 
classes with varying degrees of similarity. 
285 
Table 3: Noun Clustering Results 
Expert System 
Precision I Recall I F-measure 
Expert A 75.38 29.09 0.42 
Expert B 77.08 25.23 0.38 
Expert C 73.85 37.88 0.50 
3.3 Computation of the Overall F-measure 
Once the mappings have been determined between 
the clusterings of the system and the expert, the next 
step is to compute the F-measure between the two 
clusterings. Rather than populating separate con- 
tingency tables for every pair of classes, construct 
a single contingency table. For every pairwise map- 
ping found for the classes in these two clusterings, 
populate the YES-YES, YES-NO, and NO-YES cells 
of the contingency table appropriately (see Table 2). 
Once all the mapped classes have been incorporated 
into this contingency table, add every element of all 
unmapped classes generated by the system to the 
YES-NO cell and every element of all unmapped 
classes provided by the expert to the NO-YES cell 
of this table. Once all classes in the two clusterings 
have been accounted for, calculate the precision, re- 
call, and F-measure as explained in (Hatzivassiloglou 
and McKeown, 1993). 
4 Results and Discussion 
In one of our experiments, the 400 most frequent 
nouns in the Merck Veterinary Manual were clus- 
tered. Three experts were used to evaluate the gen- 
erated noun clusters. Some examples of the classes 
that were generated by the system for the veteri- 
nary medicine domain are PROBLEM, TREAT- 
MENT, ORGAN, DIET, ANIMAL, MEASURE- 
MENT, PROCESS, and so on. The results obtained 
by comparing these noun classes to the clusterings 
provided by three different experts are shown in Ta- 
ble 3. We have also experimented with the use of 
WordNet to improve the classes obtained by a dis- 
tributional technique. Some initial experiments have 
shown that WordNet consistently improves the F- 
measures for these noun classes by about 0.05 on an 
average. Details of these experiments can be found 
in (Agarwal, 1995). 
It is our belief that the evaluation scheme pre- 
sented in this paper is useful for comparing different 
clusterings produced by the same system or those 
produced by different systems against one provided 
by an expert. The resulting precision, recall, and 
F-measure should not be treated as a kind of "gold 
standard" to represent the quality of these classes 
in some absolute sense. It has been our experience 
that, as semantic clustering is a highly subjective 
task, evaluating a given clustering against different 
experts may yield numbers that vary considerably. 
However, when different clusterings generated by a 
system are compared against the same expert (or 
the same set of experts), such relative comparisons 
are useful. 
The evaluation scheme presented here still suffers 
from one major limitation -- it is not capable of 
evaluating a hierarchy generated by a system against 
one provided by an expert. Such evaluations get 
complicated because of the restriction of one-to-one 
mapping. More work definitely needs to be done in 
this area. 

References 
Rajeev Agarwal. 1995. Semantic feature eztraction 
from technical tezts with limited human interven- 
tion. Ph.D. thesis, Mississippi State University, 
May. 
Roberto Basili, Maria Pazienza, and Paola Velardi. 
1994. The noisy channel and the braying donkey. 
In Proceedings of the ACL Balancing Act Work- 
shop, pages 21-28, Las Cruces, New Mexico, July. 
Nancy Chincor. 1992. MUC-4 evaluation metrics. 
In Proceedings of the Fourth Message Understand- 
ing Conference (MUC-4). 
Ralph Grishman and John Sterling. 1993. Smooth- 
ing of automatically generated selectional con- 
straints. In Proceedings of the ARPA Workshop 
on Human Language Technology. Morgan Kauf- 
mann Publishers, Inc., March. 
Vasileios Hatzivassiloglou and Kathleen R. McKe- 
own. 1993. Towards the automatic identifica- 
tion of adjectival scales: Clustering adjectives ac- 
cording to meaning. In Proceedings of the 31st 
Annual Meeting of the Association for Computa- 
tional Linguistics, pages 172-82. 
Francois-Michel Lang and Lynette Hirschman. 1988. 
Improved portability and parsing through interac- 
tive acquisition of semantic information. In Pro- 
ceedings of the Second Conference on Applied Nat- 
ural Language Processing, pages 49-57, February. 
James Pustejovsky. 1992. The acquisition of lex- 
ical semantic knowledge from large corpora. In 
Proceedings of the Speech and Natural Language 
Workshop, pages 243--48, Harriman, N.Y., Febru- 
ary. 
Lisa Rau, Paul Jacobs, and Uri Zernik. 1989. In- 
formation extraction and text summarization us- 
ing linguistic knowledge acquisition. Information 
Processing and Management, 25(4):419-28. 
Philip Resnik. 1993. Selection and Information: 
A Class-Based Approach to Lezical Relationships. 
Ph.D. thesis, University of Pennsylvania, Decem- 
ber. (Institute for Research in Cognitive Science 
report IRCS-93-42). 
