# Classification of Full Text Biomedical Documents: Sections Importance Assessment
## 3 Material and Methods
### Experiments  
We prepare a study to determine the accuracy of the SVM classifier when using certain sections of the documents against the traditional processing (Title-Abstract).  
To conduct the experiments, we have applied 43 different preprocessing parameters using combinations of sections with the weights shown in Table 2. The Title-Abstract classical approach is represented by the combination \(00\), while the full text corresponds to the combination \(27\).  
Figure 1 shows the results obtained in the classification of the OHSUMED corpora applying the weighing combinations shown in the previous table. Specifically, for each weighing combination, we show the number of corpus in which that combination achieved a Kappa value close to the best one in that corpus. For example, the weighing combination \(15\) [TA0,2-IM0,6-RC0,2] reaches a Kappa value close to the best Kappa value in \(5\) of the analyzed corpus.  
For clarity, the Figure 1 only includes the corpus where a Kappa greater than \(0.6\) (moderate or substantial agreement) is reached for some combination of weighing.  
Figure 1: For each weighing combination, number of OHSUMED corpus with Kappa values close to the best Kappa value.  
As shown in the Figure 1, Title-Abstract alone -combination 00- reaches values close to the best Kappa value in three of the analyzed corpus, whereas if the full text is processed -combination 27- good Kappa values are reached in ten of the analyzed corpus. Consequently, we conclude that the accuracy improves when we process the full text with respect to Title-Abstract. This fact is corroborated by other authors, as noted in Section 1.1.  
\begin{table}
\begin{tabular}{l c c c c c c} \hline \hline
**WEIGHING COMBINATION** & **(T)** & **(A)** & **(I)** & **(M)** & **(R)** & **(C)** \\ \hline
00. [TA1,0] & 1.0 & 0 & 0 & 0 & 0 & 0 \\ \hline
01. [T1,0] & 1.0 & 0 & 0 & 0 & 0 & 0 \\ \hline
02. [A1,0] & 0 & 1.0 & 0 & 0 & 0 & 0 \\ \hline
03. [I1,0] & 0 & 0 & 1.0 & 0 & 0 & 0 \\ \hline
04. [M1,0] & 0 & 0 & 0 & 1.0 & 0 & 0 \\ \hline
05. [R1,0] & 0 & 0 & 0 & 0 & 1.0 & 0 \\ \hline
06. [C1,0] & 0 & 0 & 0 & 0 & 0 & 1.0 \\ \hline
07. [RC1,0] & 0 & 0 & 0 & 0 & 1.0 & \\ \hline
08. [IM0,2-RC0,8] & 0 & 0 & 0.2 & & 0.8 & \\ \hline
09. [IM0,4-RC0,6] & 0 & 0 & 0.4 & & 0.6 & \\ \hline
10. [IM0,6-RC0,4] & 0 & 0 & 0.6 & & 0.4 & \\ \hline
11. [IM0,8-RC0,2] & 0 & 0 & 0.8 & & 0.2 & \\ \hline
12. [IM1,0] & 0 & 0 & 1.0 & 0 & 0 \\ \hline
13. [TA0,2-RC0,8] & 0.2 & 0 & 0 & 0,8 & \\ \hline  
& 0.2 & 0.8 & 0 & 0 & 0 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Weighing of sections: Title (T), Abstract (A), Introduction (I), Methods (M), Results (R), and Conclusions (C).  
In addition, it is possible to analyze how the weighing of the sections affects the accuracy of the model. For instance, in the Table 3 we can observe that the weighing combination 32 [T0,2-A0,2-I0,2-M0,2-C0,2] (that is to say, regardless the Results section) reaches better values than full-text (combination 27) in c04 corpus (0.895 versus 0.894), c08 (0.726 versus 0.724), c11 (0.714 versus 0.713), c12 (0.796 versus 0.789), c13 (0.700 versus a Kappa value less than 60) and c19 (0.711 versus 0.703).  
There are also differences in the results obtained in the classification according to the weight assigned to each of the sections. For example, when the weighing combination 17 [TA0,2-IM0,8] is applied (that is, 80% of the weight is given to the terms that appear in the Introduction and Methods sections, and 20% of weight to the terms that appear in the Title and Abstract sections) only a Kappa value above 0.6 is achieved for 2 of the 26 corpus analyzed. On the contrary, if the attributes of Title and Abstract are given more importance following the combination 21 [TA0,4-IM0,6], then the accuracy grows substantially, being even better than the full-text.