Learning entity-oriented representation for biomedical relation extraction.

Biomedical Relation Extraction (BioRE) aims to automatically extract semantic relations for given entity pairs and is of great significance in biomedical research. Current popular methods often utilize pretrained language models to extract semantic features from individual input instances, which frequently suffer from overlapping semantics. Overlapping semantics refers to the situation in which a sentence contains multiple entity pairs that share the same context, leading to highly similar information between these entity pairs. In this study, we propose a model for learning Entity-oriented Representation (EoR) that aims to improve the performance of the model by enhancing the discriminability between entity pairs. It contains three modules: sentence representation, entity-oriented representation, and output. The first module learns the global semantic information of the input instance; the second module focuses on extracting the semantic information of the sentence from the target entities; and the third module enhances distinguishability among entity pairs and classifies the relation type. We evaluated our approach on four BioRE tasks with eight datasets, and the experiments showed that our EoR achieved state-of-the-art performance for PPI, DDI, CPI, and DPI tasks. Further analysis demonstrated the benefits of entity-oriented semantic information in handling multiple entity pairs in the BioRE task.

Protein-protein interaction relation extraction based on multigranularity semantic fusion.

Extracting semantic relationships about biomedical entities in a sentence is a typical task in biomedical information extraction. Because a sentence usually contains several named entities, it is important to learn global semantics of a sentence to support relation extraction. In related works, many strategies have been proposed to encode a sentence representation relevant to considered named entities. Despite the current success, according to the characteristic of languages, semantics of words are expressed on multigranular levels which also heavily depends on local semantic of a sentence. In this paper, we propose a multigranularity semantic fusion method to support biomedical relation extraction. In this method, Transformer is adopted for embedding words of a sentence into distributed representations, which is effective to encode global semantic of a sentence. Meanwhile, a multichannel strategy is applied to encode local semantics of words, which enables the same word to have different representations in a sentence. Both global and local semantic representations are fused to enhance the discriminability of the neural network. To evaluate our method, experiments are conducted on five standard PPI corpora (AImed, BioInfer, IEPA, HPRD50, and LLL), which achieve F1-scores of 83.4%, 89.9%, 81.2%, 84.5%, and 92.5%, respectively. The results show that multigranular semantic fusion is helpful to support the protein-protein interaction relationship extraction.

Structure enhanced deep clustering network via a weighted neighbourhood auto-encoder.

Structural deep clustering involves the use of neural networks for fusing semantic and structural representations for clustering tasks, and it has been receiving increasing attention. In some pioneering works, auto-encoder (AE)-specific representations were integrated with a graph convolutional network (GCN)-specific representation by delivering semantic information to the GCN module layer-by-layer. Although promising performance has been achieved in various applications, we observed that a vital aspect was overlooked in these works: the structural information may vanish in the learning process because of the over-smoothing problem of the GCN module, leading to non-representative features and, thus, deteriorating clustering performance. In this study, we address this issue by proposing a structure enhanced deep clustering network. The GCN-specific structural data representation is enhanced and supervised by its structural information. Specifically, the GCN-specific structural data representation is strengthened during the learning process by combining it with a structure enhanced semantic (SES) representation. A novel structure enhanced AE, named the weighted neighbourhood AE (wNAE), is employed to learn the SES representation for each data sample. Finally, we design a joint supervision strategy to uniformly guide the simultaneous learning of the wNAE and GCN modules and the clustering assignment. Experimental results for different datasets empirically validate the importance of semantic and neighbour-wise structure learning.

A neuralized feature engineering method for entity relation extraction.

Making full use of semantic and structure information in a sentence is critical to support entity relation extraction. Neural networks use stacked neural layers to perform designated feature transformations and can automatically extract high-order abstract feature representations from raw inputs. However, because a sentence usually contains several pairs of named entities, the networks are weak when encoding semantic and structure information of a relation instance. In this paper, we propose a neuralized feature engineering approach for entity relation extraction. This approach enhances the neural network by manually designed features, which have the advantage of using prior knowledge and experience developed in feature-based models. Neuralized feature engineering encodes manually designed features into distributed representations to increase the discriminability of a neural network. Experiments show that this approach considerably improves the performance compared to that of neural networks or feature-based models alone, exceeding state-of-the-art performance by more than 8% and 16.5% in terms of F1-score on the ACE corpus and the Chinese literature text corpus, respectively.