## 3 Materials and Methods
### Results and Discussion  
In order to understand the significance of use of linguistic features with a decision-tree classifier, we studied the performance of XGBoost with other known lexical/semantic features, as shown in Table 3; we also visualized their performance in Figure 3. The comparative analysis of the HPRD50 corpus indicates that the use of linguistic features improves decision-tree-based classification by approximately 29%. Table 3 illustrates a comparative analysis of the results from our experiments. We developed a Naive Bayes (NB) baseline and included SOTA models from PPI studies based on tree kernel and deep learning for this analysis. For an effective comparison, we limited our analysis to the studies that explore semantic features for input representation. Among the tree-kernel studies, graph-based feature kernel by [PERSON] et al. [11] and [PERSON] et al. [5] and linguistic-pattern-tree representations by [PERSON] et al. [14] and [PERSON] et al. [6] were referenced to draw a semantic-feature-based comparative analysis. The analysis shows a 3.64% improvement in the F\({}_{1}\)-score when compared with the highest tree-kernel performer, i.e., DSTK [5]. Figure 3: _Cont._We also compared the performance of our model with semantically enriched embeddings used with deep-learning models. Both [PERSON] et al. [7] and [PERSON] et al. [8] used semantically enriched embeddings with CNN. [PERSON] et al. [9] and [PERSON] et al. [21] studied CNN and Bi-LSTM architecture, using selective dependency graph features with embedding input. LpGBoost shows 2% and 2.4% improvement in the F\({}_{1}\)-score for AIMed and BioInfer, respectively, when compared with the MCCNN, the highest performing CNN-based model. We also developed a cross-corpus study to further examine the adaptability of our linguistic-feature representation into other cross-domain entity-association studies. As shown in Table 4 we compared the best cross-corpus reported performances from each tree-kernel and deep-learning-based study. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Features** & **Precision** & **Recall** & **F\({}_{1}\)-Score** \\ \hline Bag of words (BOW) & 62.4 & 50.9 & 56.0 \\ Term frequency–inverse-document & 62.9 & 49.0 & 55.1 \\ frequency (Tf–IDF) & 63.0 & 50.3 & 55.9 \\ Hashing features (Hf) & 86.1 & 84.0 & 85.0 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: XGBoost-based comparative feature analysis on HPRD50 corpus. Figure 3: Comparative analysis on precision, recall, and F\({}_{1}\)-score. Our discussion primarily focuses on results from AIMed and BioInfer, as these have been extensively studied in several related works. The comparative analysis of the SOTA tree-kernel model (Table 5) shows a 4.55% increase in F\({}_{1}\)-average. Our approach outperforms semantically enriched CNN models; however, we do observe a 3.65% drop in F\({}_{1}\)-average when compared to sdpLSTM. We also developed a new baseline for Aldea corpus, where we achieved 86.2% on the F\({}_{1}\)-score. The performance showcases a 53.4% increase when compared to Naive Bayes. LpGBoost exploits sparse semantic patterns (linguistic patterns) to develop decision features for gradient-boosted tree. The original instance is reduced to an entity-pair selective sematic representation; for example, \"Activation of lacZ upon interaction of p85 with IR beta (delta C-43) was 4-fold less as compared to IR beta\" is reduced to {[IN, NN, IN]} \(\rightarrow\) [NN, IN, TRIGGERPRI] \(\rightarrow\) [PROTEINT, IN, TRIGGERPRI] \(\rightarrow\) [PROTEINT, VBD, RB]] representation. Derived from it, the candidate features are optimized over the lead feature matrix to generate the classification vector. Table 6 shows the candidate features for the given example. The above features combine aspects of both convolved and sequential representations, making it a robust descriptor for a tree-based decision model.