# A Message Passing Approach to Biomedical Relation Classification for Drug-Drug Interactions
###### Abstract
The proposed approach implemented a multi-channel CNN model and fused multiple versions of word embeddings that were trained on biomedical domain corpora. However, the systems performance depends greatly on the CNN's window size, leading to errors in long sentences where the relevant drug mentions are either very close or very far from each other. In an attempt to capture long distance dependencies, [PERSON] et al. [9] utilized syntactic features in the form of dependency parsing trees and word syntax-based embeddings in their proposed DCNN approach. Due to the small number of correctly parsed long sentences, a threshold was implemented where sentences with a length smaller than the threshold were classified by the DCNN, while the rest by a CNN. Similarly, [PERSON] et al. [10] utilized dependency features in combination with Part-of-Speech (PoS) and position embeddings with an auto-encoder to transfer sparse bag-of-words feature vectors to dense real value feature vectors. The proposed SCNN approach additionally implemented a rule-based negative instance filtering, leading to limited generalization ability. To alleviate the limitations of CNN-based approaches, various DDI extraction studies employed RNN-based networks that capture long sequences using an internal memory mechanism, such as Long Short Term Memory (LSTM) and Gated Recurrent Units (GRU) networks. Accordingly, [PERSON] et al. [11] presented a three channel bidirectional LSTM (BiLSTM) architecture to capture distance and dependency-based features with their DL-STM model. To account for the imbalanced class distribution of the DDI corpus, negative instance filtering and training set sampling were employed. However, the reported results indicate that the lengths of the instances continue to adversely affect the classification performance of the model. [PERSON] et al. [12] introduced 2 ATT-RNN, a GRU architecture that leverages multiple attention layers. A word-level attention layer extracts sentence representations in combination with a sentence-level attention layer that combines other sentences containing the same drug mentions. However, the inclusion of the negative class in the overall performance metric does not allow for a clear depiction of the effectiveness of the proposed method. [PERSON] et al. [13] divided the input sentence sequences into three parts according to the position of two drug entities, and applied a hierarchical BiLSTMs to integrate sentence sequences, shortest dependencies paths and attention mechanisms to classify DDIs. The experimental results show improvements over the previous approaches, but continue to underperform in cases where the two drug entities are mentioned over a long distance with each other. Similarly, [PERSON] et al. [14] utilized the attention mechanism in a BiLSTM-based architecture. To improve the efficiency of the attention mechanism, the proposed PM-BLSTM system utilizes an additional position embedding to generate the attention weights. The model takes advantage of multi-task learning by predicting whether or not two drugs interact with each other, further distinguishing the types of interactions jointly. The reported results show that the position-wise attention improves the performance but continues to misclassify instances that contain multiple drug mentions. [PERSON] et al. [15] proposed a straightforward LSTM and attention-based architecture and expanded the DDI extraction task to include sentiment-based severity prediction. The sentence-level polarity is extracted using an NLP toolkit and finally classified as either low, moderate or high level of severity for instances that contain at least on DDI. However, the Word2 Vec [16] generated word embeddings are context-independent and do not account for the word positions in the sentences. Furthermore, Word2 Vec learns word level embeddings, resulting in the same embedding for any learned word, independently of the surrounding context.