## 1 Introduction  
In recent years, natural-language-processing techniques have been increasingly used to study bio-entity-based interactions from the literature for a wide range of tasks, such as protein-protein interaction (PPI) [1], chemical-disease relation [2], and chemical-protein relation [3]. Protein-based interactions are the pinnacle of biological mechanisms in all organisms, and therefore the protein-protein interaction is one of the most exhaustively researched topics in the biomedical field. Over the years, multiple PPI corpora have been deployed, and each one has a varied level of bio-entity information [4]; for example, BioInfer includes gene- and RNA-based protein-association information annotated as per binding, directed-edge, and negative interactions. Similarly, HPRD50 primarily comprises human protein-gene-based interactions. Therefore, the types of interacting partners and nature of interactions vary within the literature. While some instances contain one-to-one entity-pair association, e.g., \"oxytocin \(\rightarrow\) insulin\" in \"Subcutaneous injections of oxytocin increased insulin, glucagon and glucose levels significantly\", others exhibit complex pairings, with one-to-many or many-to-many associations, e.g., \"UCP2 \(\rightarrow\) leptin\" and \"UCP3 \(\rightarrow\) leptin\" in \"UCP2, UCP3 and leptin gene expression: modulation by food restriction and leptin\". Many of the earlier studies developed tree-kernel (TK) classifiers for PPI association-detection tasks [1, 5, 6]. These models have successfully obtained a state-of-the-art (SOTA) performance in PPI tasks for both cross-corpus and within-corpus settings. Recently studiesbased on deep-learning architectures have also generated interesting results, particularly for larger corpuses such as AIMed and BioInfer. They employ enriched embedding developed with features such as shortest dependency path (sdpCNN), continuous bag of words (CBOW) embedding (MCNN), and dependency-tree embedding (McDeepCNN) [7; 8; 9]. The syntactic relevant representation generated from these embeddings helps improve the performance, using convolution neural networks (CNNs). These approaches draw a contrast to the feature-engineering techniques used by TK-based PPI detection studies. In separate studies conducted by [PERSON] et al. [5] and [PERSON] et al. [6], enriched syntax and semantic features were used with tree kernels to achieve improved PPI detection. In this paper, we introduce gradient-boosted learning on semantically optimized linguistic-pattern representation. The eXtreme Gradient Boosting (XGboost) is a gradient-boosting decision tree that can be used for classification or regression problems. Gradient boosting tries to correct the residuals of all the weak learners by adding a new weak learner. Eventually, multiple learners are added together for the final prediction, and the accuracy is higher than the single one. Since the effectiveness of XGBoost, it has been validated on a real-life, large-scale, and imbalanced dataset and solves many data-science problems in a fast and accurate way. Therefore, we developed our model based on XGBoost in this research. The feature-optimization and -representation studies were developed from our previous work, using unsupervised learning to extract semantically invariant linguistic features. These invariant linguistic patterns generate a pattern-based representation, which is adapted with XGBoost for gradient-boosted tree learning. The invariant lexical patterns contribute to context-based representation for each token, in contrast to long vector embeddings based on word co-distribution. We evaluated our work on five benchmark PPI corpora, i.e., HPRD50, LLL, IEPA, AIMed, and BioInfer [10], and a new PPI corpus AIdea, [[https://aidea-web.tw/file/d0](https://aidea-web.tw/file/d0) ce130-b15d-4c4c-b7e8-bf95a0c3]([https://aidea-web.tw/file/d0](https://aidea-web.tw/file/d0) ce130-b15d-4c4c-b7e8-bf95a0c3) 4 dd8-553658198_train_test_dataset_1_Sample_data.7z (accessed on 13 May 2022).