## 3 Materials and Methods
### Results and Discussion
The results from our XGBoost-based feature-comparison study (Table 3) also highlight that the sparse semantically relevant representation performs better with gradient-boosted trees in comparison to elaborate vectors based on term frequency and hashing. A detailed analysis of the \begin{table}
\begin{tabular}{c c} \hline \hline
**Feature \#** & **Linguistic-Pattern-Based Candidate Features** \\ \hline _f1_ & [IN, NN, IN] \(\rightarrow\) [NN, IN, TRIGGERPRI] \\ _f2_ & [IN, NN, IN] \(\rightarrow\) [NN, IN, TRIGGERPRI] \(\rightarrow\) [PROTEINT, IN, TRIGGERPRI] \\ _f3_ & [IN, NN, IN] \(\rightarrow\) [NN, IN, TRIGGERPRI] \(\rightarrow\) [PROTEINT, UN, TRIGGERPRI] \(\rightarrow\) [PROTEINT, VBD, RB] \\ _f4_ & [PROTEINT, IN, TRIGGERPRI] \(\rightarrow\) [PROTEINT, VBD, RB] \\ _f5_ & [NN, IN, TRIGGERPRI] \(\rightarrow\) [PROTEINT, IN, TRIGGERPRI] \(\rightarrow\) [PROTEINT, VBD, RB] \\ _f6_ & [IN, NN, IN] \(\rightarrow\) [NN, IN, TRIGGERPRI] \(\rightarrow\) [PROTEINT, IN, TRIGGERPRI] \(\rightarrow\) [PROTEINT, VBD, RB] \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Candidate feature representation developed for classification vector. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multicolumn{2}{c}{**HPRD50**} & **LLL** & **IEPA** & **AIMed** & **BioInfer** & **Aldea** \\ \cline{2-6}  & & \multicolumn{4}{c}{**Precision/Recall/F\({}_{1}\)-Score (\%)**} \\ \hline NB & 49.7/50.9/50.3(*) & 47.2/37.1/41.6(*) & 61.4/70.4/65.6(*) & 22.3/41.6/29.0(*) & 28.4/28.4/28.4(*) & 27.2/41.1/32.8(*) \\ \hline GK & 64.3/65.8/63.4(*) & 72.5/87.2/76.8(*) & 69.6/82.7/75.1(*) & 52.9/61.8/56.4(*) & 56.7/67.2/61.3(*) & - \\ PIPE & 63.8/81.2/71.5(*) & 73.2/89.6/80.6(*) & 62.5/83.3/71.4(*) & 57.2/64.5/60.6(*) & 68.6/70.3/69.4(*) & - \\ LPTK & 72.7/62.6/67.1(*) & 78.9/72.1/75.3(*) & 74.8/66.1/70.2(*) & 83.6/75.0/79.1(*) & 83.0/65.7/74.4(*) & - \\ DSTK & 76.3/84.2/80.0(*) & 87.3/91.2/89.2(-) & 75.9/85.2/80.2(*) & 68.9/73.2/71.0(*) & 75.7/76.9/76.3(*) & - \\ DNN & 58.7/92.4/71.3(*) & 76.0/91.0/81.4(*) & 71.8/79.4/74.2(*) & 51.5/63.4/56.1(*) & 53.9/72.9/61.6(*) & - \\ McDepCNN & - & - & - & 67.3/60.1/63.5(*) & 62.7/68.2/65.3(*) & - \\ MCCNN & - & - & - & 76.4/69.0/72.4(*) & 81.3/78.1/79.6(*) & - \\ sdpCNN & - & - & - & 64.8/67.8/66.0(*) & 73.4/77.0/75.2(*) & - \\ sdpLSTM & - & - & - & 91.1/82.2/86.4(*) & 72.4/83.1/77.3(*) & - \\ LpGBoost & 86.1/84.0/78.5(*) & 84.9/89.6/87.2 & 94.6/79.4/86.3 & 61.8/93.6/74.4 & 74.7/90.9/82.0 & 78.4/95.8/86.2 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Ten-fold CV comparative performance evaluation on PPI corpora. A one-tailed \(t\)-test was applied to determine whether our proposed model (LpGBoost) significantly improves the performance of the F\({}_{1}\)-score of the comparisons, where * represents \(t\)-tests with alpha = 0.05.  
candidate features further revealed the consistency of features/_f_l and \(f\)4 in improving tree-based decision-making among all the test corpora. Moreover,_f_l and \(f\)4 represent sematic patterns immediately adjacent to the target-entity mention, which indicates that shorter sematic-context-based features are better descriptors in interaction detection studies over longer sequences. The results shown in Table 5 show that our model performs better than deep learning by 1.9% when studied for BioInfer, while the F\({}_{1}\)-score stagnates at 43.5% with AIMed testing. Our analysis suggests that selective labeling from interaction pattern trees used in PIPE can help improve the cross-corpus learning and general adaptability of linguistic pattern representation.