Joint Extraction of Entities and Relations Based on a Novel Tagging Scheme

Joint extraction of entities and relations is an important task in information extraction. To tackle this problem, we firstly propose a novel tagging scheme that can convert the joint extraction task to a tagging problem. Then, based on our tagging scheme, we study different end-to-end models to extract entities and their relations directly, without identifying entities and relations separately. We conduct experiments on a public dataset produced by distant supervision method and the experimental results show that the tagging based methods are better than most of the existing pipelined and joint learning methods. What's more, the end-to-end model proposed in this paper, achieves the best results on the public dataset

Friend Ranking in Online Games via Pre-training Edge Transformers

Friend recall is an important way to improve Daily Active Users (DAU) in online games. The problem is to generate a proper lost friend ranking list essentially. Traditional friend recall methods focus on rules like friend intimacy or training a classifier for predicting lost players' return probability, but ignore feature information of (active) players and historical friend recall events. In this work, we treat friend recall as a link prediction problem and explore several link prediction methods which can use features of both active and lost players, as well as historical events. Furthermore, we propose a novel Edge Transformer model and pre-train the model via masked auto-encoders. Our method achieves state-of-the-art results in the offline experiments and online A/B Tests of three Tencent games.Comment: Accepted by the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR 2023

Deciphering the functional importance of comammox vs. canonical ammonia oxidisers in nitrification and N2O emissions in acidic agricultural soils

Acknowledgments This work was jointly supported by grants from the National Key Research and Development Program of China (2018YFD0800202), the National Key Research and Development Program of China (2017YFD0200707 & 2017YFD0200102), the Fundamental Research Funds for the Central Universities (226-2023-00077) and Zhejiang University-Julong Ecological Environment R&D Centre (2019-KYY-514106-0006).Peer reviewe