## 6 Experimental Results
### Experiment Datasets
In fact, we can observe that the datasets have different characteristics. The Wikipedia documents together with the category PubMed articles, are characterized by a greater length and a higher lexical richness than the others; in fact, the Guiraud Index is higher for these datasets, reaching the maximum value with the PubMed articles. The dictionary, even after Hapax removal, is extremely high, and reflects the complexity of the datasets chosen to test ESCAPE. Moreover, the PubMed collection presents a further complexity, i.e., the expected number of topics is not known a priori. On the other side, we have also included a dataset represented by smaller lexical richness, i.e., the Twitter collection. The average document length decreases considerably, as does the average frequency. Nevertheless, the Hapax rate is comparable with the other \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Features** & \multicolumn{4}{c}{**Wikipedia**} \\ \hline Dataset ID & D1 & D2 & D3 \\ \hline \# categories & 5 & 10 & 10 \\ \hline \# documents & 990 & 2469 & 4939 \\ \hline Max frequency & 5394 & 13,344 & 19,546 \\ \hline
**Features** & **WH** & **WoH** & **WH** & **WoH** & **WH** \\ \hline Min frequency & 1.0 & 2.0 & 1.0 & 2.0 & 1.0 & 2.0 \\ \hline Avg. frequency & 25 & 45 & 36 & 69 & 39 & 78 \\ \hline Avg. document length & 852 & 836 & 970 & 957 & 705 & 697 \\ \hline \# terms & 843,967 & 828,372 & 2,395,721 & 2,363,958 & 3,486,016 & 3,442,508 \\ \hline Dictionary \(|\)V\(|\) & 33,635 & 18,040 & 65,629 & 33,866 & 87,419 & 43,911 \\ \hline TTR & 0.04 & 0.03 & 0.02 & 0.01 & 0.03 & 0.01 \\ \hline Hapax \% & 46.3 & 0.0 & 48.2 & 0.0 & 49.1 & 0.0 \\ \hline Guiraud Index & 36.61 & 19.82 & 42.40 & 22.02 & 46.82 & 23.66 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Statistical features for the Wikipedia collections. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Features** & **Twitter** & **PubMed** & **Reuters** \\ \hline Dataset ID & D4 & D5 & D6 \\ \hline \# categories & 6 & - & 90 \\ \hline \# documents & 60,005 & 1000 & 15,437 \\ \hline Max frequency & 6936 & 775 & 42,886 \\ \hline
**Features** & **WH** & **WoH** & **WH** & **WoH** \\ \hline Min frequency & 1.0 & 2.0 & 1.0 & 2.0 & 1.0 & 2.0 \\ \hline Avg. frequency & 19 & 36 & 15 & 18 & 55 & 76 \\ \hline Avg. document length & 5 & 5 & 3600 & 3469 & 87 & 85 \\ \hline \# terms & 312,718 & 304,666 & 3,600,153 & 3,469,305 & 1,337,225 & 1,316,988 \\ \hline Dictionary \(|\)V\(|\) & 16,345 & 12,136 & 227,210 & 96,362 & 24,239 & 17,153 \\ \hline TTR & 0.05 & 0.03 & 0.06 & 0.05 & 0.02 & 0.01 \\ \hline Hapax \% & 49.26 & 0.0 & 57.02 & 0 & 29.2 & 0.0 \\ \hline Guiraud Index & 29.23 & 15.02 & 119.75 & 51.73 & 20.96 & 14.95 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Statistical features for datasets D4, D5, and D6. datasets, and the dictionary after the Hapax removal is smaller with respect to the other datasets. Among the datasets we have also included the Reuters collection, as it presents differences in data distributions with respect to the other datasets. The Reuters collection is characterized by a medium length and a not-too-high lexical index, since the average frequency of the terms is the highest (i.e., the documents are characterized by a medium length with terms repeated several times). For this reason, the lexical richness is the lowest of all corpora.