ABSTRACT In this paper, we study cross-domain relation extraction. Since new data mapping to feature spaces always differs from the previously seen data due to a domain shift, few-shot relation extraction often perform poorly. To solve the problems caused by cross-domain, we propose a method for combining the pure entity, relation labels and adversarial (PERLA). We first use entities and complete sentences for separate encoding to obtain context-independent entity features. Then, we combine relation labels which are useful for relation extraction to mitigate context noise. We combine adversarial to reduce the noise caused by cross-domain. We conducted experiments on the publicly available cross-domain relation extraction dataset Fewrel 2.0[ 1 ] ① , and the results show that our approach improves accuracy and has better transferability for better adaptation to cross-domain tasks.

ABSTRACT Few-shot learning has been proposed and rapidly emerging as a viable means for completing various tasks. Recently, few-shot models have been used for Named Entity Recognition (NER). Prototypical network shows high efficiency on few-shot NER. However, existing prototypical methods only consider the similarity of tokens in query sets and support sets and ignore the semantic similarity among the sentences which contain these entities. We present a novel model, Few-shot Named Entity Recognition with J oint T oken and S entence A wareness (JTSA) , to address the issue. The sentence awareness is introduced to probe the semantic similarity among the sentences. The Token awareness is used to explore the similarity of the tokens. To further improve the robustness and results of the model, we adopt the joint learning scheme on the few-shot NER. Experimental results demonstrate that our model outperforms state-of-the-art models on two standard Few-shot NER datasets.

ABSTRACT Predicting interactions between drugs and target proteins has become an essential task in the drug discovery process. Although the method of validation via wet-lab experiments has become available, experimental methods for drug-target interaction (DTI) identification remain either time consuming or heavily dependent on domain expertise. Therefore, various computational models have been proposed to predict possible interactions between drugs and target proteins. However, most prediction methods do not consider the topological structures characteristics of the relationship. In this paper, we propose a relational topology-based heterogeneous network embedding method to predict drug-target interactions, abbreviated as RTHNE_ DTI. We first construct a heterogeneous information network based on the interaction between different types of nodes, to enhance the ability of association discovery by fully considering the topology of the network. Then drug and target protein nodes can be represented by the other types of nodes. According to the different topological structure of the relationship between the nodes, we divide the relationship in the heterogeneous network into two categories and model them separately. Extensive experiments on the real-world drug datasets, RTHNE_DTI produces high efficiency and outperforms other state-of-the-art methods. RTHNE_DTI can be further used to predict the interaction between unknown interaction drug-target pairs.

Few-shot learning has been proposed and rapidly emerging as a viable means for completing various tasks. Many few-shot models have been widely used for relation learning tasks. However, each of these models has a shortage of capturing a certain aspect of semantic features, for example, CNN on long-range dependencies part, Transformer on local features. It is difficult for a single model to adapt to various relation learning, which results in a high variance problem. Ensemble strategy could be competitive in improving the accuracy of few-shot relation extraction and mitigating high variance risks. This paper explores an ensemble approach to reduce the variance and introduces fine-tuning and feature attention strategies to calibrate relation-level features. Results on several few-shot relation learning tasks show that our model significantly outperforms the previous state-of-the-art models.