Evaluation of Similarity Measureemploying Point-of-View Reinforcement

Kenji Nagamatsu

andHidehiko Tanaka

University of Tokyo
Hongo 7-3-1, Bunkyo-ku, Tokyo, Japan, 113

1Introduction

coverage of non-synonym pairs
100.00%90.00%
depth
80.00%
link#
70.00%
copov(1.2)
60.00%
pov(1.5)
pov(2.0)
50.00%
resnik95

Two di erent words may not be similar in general,
rather they are similar under some aspects or point-ofviews. This paper proposes a new similarity measure
between words based on point-of-views. The method
utilizes co-occurrence probability-based similarity as abasis and extends it by weighting the values according
to the relevance between input words and point-of-view
words(called point-of-view reinforcement).
240.00%30.00%

20.00%

Similarity with Point-of-View

Based on both corpus- and feature-based measures theformulation of our similarity Sim(w1 ; w2 ; wp ) is de ned.
P r(wjw1 ; wp ) + P r(wjw2 ; wp )Sim(w1 ; w2 ; wp ) =X

810.00%80%

0The

is responsible forthis basic similarity by point-of-view words.
(wp ;w) f (wjw )Pr(wjw ; wp ) = (wp ;w)(0 1)f (wjw ) + x Co(w ) f (xjw )f (wjw ) denotes the normal co-occurrence frequency
and is a parameter controlling how the relatedness
between two point-of-views(wp ; w) a ects the similarity.
(wp ; w ) is the factor indicating the relatedness between input words and a point-of-view word. It is dened as the mutual information content between wp andw and approximated with another type of co-occurrence
data extracted from a tagged corpus.
point-of-view reinforcement
modulating
0003Experiments

P08 200

One experiment is a selectivity test([Nagamatsu andTanaka, 1996]) with large word-pair sets of synonyms
and non-synonyms. This evaluates the whole attitude ofsimilarity measures(see the gure).
The result shows clearly that the corpus-based measures(co, pov*) are superior to the thesaurus-basedones(link#, depth). Moreover, among these corpusbased measures, employing the point-of-view reinforcement(pov) makes the selectivity higher than its originalco(the lower a data sequence is located, the higher theselectivity of the measure becomes).
The other is a experiment employing human subjects.
This shows the correlation between similarity values andrating scores by human subjects(see the table).

86%

88%

Sim measure Whole
resnik95
0.426
pov(2.0)
0.424
pov(1.2)
0.390
depth
0.380
link#0.365
co0.3442

w2Co(w1 )\Co(w2 )

84%

90%

92%

94%

96%

98%

100%

coverage of synonym pairs

Pr(wjw ; wp ) denotes the co-occurrence probability of
w conditioned by w and reinforced by a point-of-view
wp , Co(w) the set of words co-occurring with w.
082%

IPAL Bunrui-Goi-Hyo
0.235
0.420
0.232
0.495
0.210
0.415
0.164
0.449
0.104
0.442
0.211
0.306

This experiment shows that the thesaurus-based measures(depth, link#, resnik95) have higher correlation
with human judgment than the corpus-based ones(co).
By employing point-of-view reinforcement, however, thederived measures(pov*) have become even better than
the thesaurus-based measures and when the parameteris adequately selected, the highest correlation has beenachieved.
4Conclusion

From the experiments it is concluded that the proposed
similarity measure can distinguish synonym pairs from
non-synonym pairs better than other similarity measures(selectivity test) and that the measure has high correlation with the rating scores by human subjects.
References[Nagamatsu and Tanaka, 1996] Kenji Nagamatsu and Hidehiko Tanaka. Estimating point-of-view-based similarity using pov reinforcement & similarity propagation. In Proceedings of PACLIC 11 Language, Information and Computation, pages 373{382, December 1996.
[Resnik, 1995] Philip Resnik. Using information content toevaluate semantic similarity in a taxonomy. In Proceedingsof the 14th International Joint Conference on Arti cial Intelligence, volume 1, pages 448{453, 1995.