Relation extraction has evolved from supervised relation extraction to zero-shot setting due to the continuous emergence of newly generated relations. Some pioneering works handle zero-shot relation extraction by reformulating it into proxy tasks, such as reading comprehension and textual entailment. Nonetheless, the divergence in proxy task formulations from relation extraction hinders the acquisition of informative semantic representations, leading to subpar performance. Therefore, in this paper, we take a data-driven view to handle zero-shot relation extraction under a three-step paradigm, including encoder training, relation clustering, and summarization. Specifically, to train a discriminative relational encoder, we propose a novel selective contrastive learning framework, namely, SCL, where selective importance scores are assigned to distinguish the importance of different negative contrastive instances. During testing, the prompt-based encoder is employed to map test samples into representation vectors, which are then clustered into several groups. Typical samples closest to the cluster centroid are selected for summarization to generate the predicted relation for all samples in the cluster. Moreover, we design a simple non-parametric threshold plugin to reduce false-positive errors in inference on unseen relation representations. Our experiments demonstrate that SCL outperforms the current state-of-the-art method by over 3% across all metrics.

Entity linking, as one of the fundamental tasks in natural language processing, is crucial to knowledge fusion, knowledge base construction and update. Nevertheless, in contrast to the research on entity linking for English text, which undergoes continuous development, the Chinese counterpart is still in its infancy. One prominent issue lies in publicly available annotated datasets and evaluation benchmarks, which are lacking and deficient. In specific, existing Chinese corpora for entity linking were mainly constructed from noisy short texts, such as microblogs and news headings, where long texts were largely overlooked, which yet constitute a wider spectrum of real-life scenarios. To address the issue, in this work, we build CLEEK, a Chinese corpus of multi-domain long text for entity linking, in order to encourage advancement of entity linking in languages besides English. The corpus consists of 100 documents from diverse domains, and is publicly accessible. Moreover, we devise a measure to evaluate the difficulty of documents with respect to entity linking, which is then used to characterize the corpus. Additionally, the results of two baselines and seven state-of-the-art solutions on CLEEK are reported and compared. The empirical results validate the usefulness of CLEEK and the effectiveness of proposed difficulty measure.