Extracting Relational Triples Based on Graph Recursive Neural Network via Dynamic Feedback Forest Algorithm

Extracting relational triples (subject, predicate, object) from text enables the transformation of unstructured text data into structured knowledge. The named entity recognition (NER) and the relation extraction (RE) are two foundational subtasks in this knowledge generation pipeline. The integration of subtasks poses a considerable challenge due to their disparate nature. This paper presents a novel approach that converts the triple extraction task into a graph labeling problem, capitalizing on the structural information of dependency parsing and graph recursive neural networks (GRNNs). To integrate subtasks, this paper proposes a dynamic feedback forest algorithm that connects the representations of subtasks by inference operations during model training. Experimental results demonstrate the effectiveness of the proposed method

Geminin is partially localized to the centrosome and plays a role in proper centrosome duplication

Background information. Centrosome duplication normally parallels with DNA replication and is responsible for correct segregation of replicated DNA into the daughter cells. Although geminin interacts with Cdt1 to prevent loading of MCMs (minichromosome maintenance proteins) on to the replication origins, inactivation of geminin nevertheless causes centrosome over-duplication in addition to the re-replication of the genome, suggesting that geminin may play a role in centrosome duplication. However, the exact mechanism by which loss of geminin affects centrosomal duplication remains unclear and the possible direct interaction of geminin with centrosomal-localized proteins is still unidentified

Daughter-Specific Transcription Factors Regulate Cell Size Control in Budding Yeast

The asymmetric localization of cell fate determinants results in asymmetric cell cycle control in budding yeast

A Collaborative AI-enabled Pretrained Language Model for AIoT Domain Question Answering

Large-scale knowledge in the artificial intelligence of things (AIoT) field urgently needs effective models to understand human language and automatically answer questions. Pretrained language models achieve state-of-the-art performance on some question answering (QA) datasets, but few models can answer questions on AIoT domain knowledge. Currently, the AIoT domain lacks sufficient QA datasets and large-scale pretraining corpora. In this article, we propose RoBERTa_ AIoT to address the problem of the lack of high-quality large-scale labeled AIoT QA datasets. We construct an AIoT corpus to further pretrain RoBERTa and BERT. RoBERTa_ AIoT and BERT_ AIoT leverage unsupervised pretraining on a large corpus composed of AIoT-oriented Wikipedia webpages to learn more domain-specific context and improve performance on the AIoT QA tasks. To fine-tune and evaluate the model, we construct three AIoT QA datasets based on the community QA websites. We evaluate our approach on these datasets, and the experimental results demonstrate the significant improvements of our approach.Peer reviewe

Structural block driven enhanced convolutional neural representation for relation extraction

In this paper, we propose a novel lightweight relation extraction approach of structural block driven convolutional neural learning. Specifically, we detect the essential sequential tokens associated with entities through dependency analysis, named as a structural block, and only encode the block on a block-wise and an inter-block-wise representation, utilizing multi-scale Convolutional Neural Networks (CNNs). This is to (1) eliminate the noisy from irrelevant part of a sentence; meanwhile (2) enhance the relevant block representation with both block-wise and inter-block-wise semantically enriched representation. Our method has the advantage of being independent of long sentence context since we only encode the sequential tokens within a block boundary. Experiments on two datasets i.e., SemEval2010 and KBP37, demonstrate the significant advantages of our method. In particular, we achieve the new state-of-the-art performance on the KBP37 dataset; and comparable performance with the state-of-the-art on the SemEval2010 dataset