# Word Sense Disambiguation Using Clustered Sense Labels
## 5 Discussion
The best value of (a) is higher than (b), i.e., 75.6% versus 69.1%. This means that a manually constructed thesaurus is better than our clustering method for sense label compression. Nonetheless, both models perform better than the baseline models that do not use compressed labels and have the highest performance when compression is used with appropriate relations or threshold restrictions. **Use of Korean thesaurus:** The sense vocabulary compression method [18] must have both a thesaurus and the corresponding sense annotated corpus. However our method needs a sense annotated corpus and the corresponding sense definitions contained in a dictionary or a thesaurus. In the case of Korean language, most of the sense annotated corpus is developed based on a dictionary. Examples are the Sejong sense annotated corpus based on Standard Korean Language Dictionary and the Modu sense annotated corpus [36] based on Urimalsaem dictionary [37]. Korean thesauri that have been developed so far are KAIST's CoreNet [38], Pusan National University's KorLex [39], University of Ulsan's UWordMap [40], Postech's WordNet translation [41], and others. Among these, KorLex, UWordMap, and Postech's WordNet use the same sense tag as Sejong sense annotated corpus. However, KorLex and Postech's WordNet contain only some part of the whole senses in Standard Korean Language Dictionary; KorLex contains only the senses that exist in English WordNet, and Postech's WordNet is partially translated from English WordNet. UWordMap has included most of sense vocabulary of Standard Korean Language Dictionary more recently, which is quite later than the sense annotated corpus release [42]. Modu sense annotated corpus has been recently developed based on the senses defined in Urimalsaem dictionary, which contains more vocabulary and uses finer sense number hierarchy than Standard Korean Language Dictionary. The corresponding the \begin{table}
\begin{tabular}{l l} \hline \hline (a) SVC model [18] using thesaurus. \\ \hline
**Comp. Level** & **F1 Value** \\ \hline Baseline (Ensemble) & 63.4 \\ Synonyms & 75.1 \\ Hypernyms & **75.6** \\ All relation & 73.9 \\ \hline (b) Our model without using thesaurus. \\ \hline
**Comp. Level** & **F1 Value** \\ \hline Baseline & 62.9 \\ Threshold 0.1 & 68.4 \\ Threshold 0.5 & **69.1** \\ Threshold 1.0 & 64.8 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Performance comparison of SVC model and proposed model at various compression levels in F1 measure. saurus to Urimalsaem dictionary has not been developed yet. Urimalsaem dictionary is being constructed and updated with online crowd sourcing, which makes updating the corresponding thesaurus, if it exists, non-trivial. Our sense clustering method using sense definitions in a dictionary is very useful for this environment, where a corresponding thesaurus does not exist to the sense annotated corpus. For future work, we will apply our method to the Modu sense annotated corpus, for which a full version was not available at implementation time. **Further improvement of clustering:** We can further improve our method using a clustering algorithm. For this we should consider the following points: 1. Because we are dealing with huge data points (146K in case of English nouns), the space and time complexity should be small enough to be executed in an appropriate time limit. 2. The clusters with appropriate size should be determined automatically, with the clustering criteria reflecting not only similarity measures but also homograph restrictions that avoid including the same homograph senses in the same cluster. Most clustering algorithms such as AP [24], HAC [22], GMM [43], and DBSCAN [44] having high computational complexity could not execute our clustering data with huge data points under our workstation level computation environment (Intel i7 CPU and 48 GB main memory). Although the k-means algorithm has relatively lower complexity than the other algorithms, it has to determine the optimal number of clusters automatically [45; 46], which will cause the complexity to increase.