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Nested named entity recognition (NNER) has become a research hotspot in information extraction because of its increasingly important practical significance. However, due to the shortage of corpus resources, limited exhaustive windows, missing span features, etc., NNER research in vertical field has made slow progress and there are problems of entity recognition errors or omissions. To solve these problems, a vertical field NNER model based on mineralogy and corpus awareness dictionary is proposed. Firstly, the point mutual information, word frequency inverse text frequency algorithm and attention mechanism are combined to automatically integrate the corpus awareness dictionary, and the anchor text knowledge is used to improve the training accuracy of the model. Secondly, from the shared perspective, three multi-modal information fusion strategies are designed to train the encoder to learn the extended vector representation of character, glyph and vocabulary. Through triple product operation and slicing attention mechanism, the private representations captured by the multi-layer perceptron are screened and integrated to narrow the spatial gap of heterogeneous features. Thirdly, the context association between spans is determined by a bottom-up hierarchical architecture, and the proposed span set is generated. The characteristics of target span and adjacent span, target span internal characterization, target span boundary, etc. are obtained by double affine mechanism and linear classifier. Finally, the corresponding entity type label is assigned to the target span. Experimental results on six datasets show that compared with baseline model, the proposed method achieves significant performance improvement and can effectively improve the NNER task effect in low-resource scenarios.

The correct extraction of equipment name, parameter standard, fault location, fault type and other entities in the monitoring text of mine electromechanical equipment can assist experts to find abnormal equipment as soon as possible and improve the efficiency and accuracy of equipment fault analysis. In view of the fact that most entities in the field of mine electromechanical equipment are nested entities with long characters and strong contextual relevance, an entity recognition method combining multi-granularity features is proposed in this paper. The long sequence nested entity boundary is initially determined by the machine reading comprehension framework, and the context association representation between entities is deeply explored by BiLSTM neural network integrating attention mechanism. The experimental results show that this method has a good recognition effect on the entities in the mine electromechanical equipment monitoring text, and improves the effectiveness of other named entity recognition tasks in low resource scenarios. [ABSTRACT FROM AUTHOR]

Simultaneous conversion of CO2 and biomass into value-added chemicals through solar-driven catalysis holds tremendous importance for fostering a sustainable circular economy. Herein, plasmonic Bi quantum dots were immobilized on phosphoric acid modified attapulgite (P-ATP) nanorod using an in-situ reduction–deposition method, and were employed for photocatalytic reduction of CO2 coupled with oxidation of biomass-derived benzyl alcohol. Results revealed that Bi atoms successfully integrated into the basal structure of P-ATP, forming chemically coordinated Bi–O–Si bonds that served as efficient transportation channels for electrons. The incorporation of high-density monodispersed Bi quantum dots induced a surface plasmon resonance (SPR) effect, expanding the light absorption range into the near-infrared region. As a consequence, the photo-thermal transformation was significantly accelerated, leading to enhanced reaction kinetics. Notably, 50% Bi/P-ATP nanocomposite exhibited the highest plasmon-mediated photocatalytic CH4 generation (115.7 µmol·g−1·h−1) and CO generation (44.9 µmol·g−1·h−1), along with remarkable benzaldehyde generation rate of 79.5 µmol·g−1·h−1 in the photo-redox coupling system under solar light irradiation. The hydrogen protons released from the oxidation of benzyl alcohol facilitated the incorporation of more hydrogen protons into CO2 to form key CH3O− intermediates. This work demonstrates the synergistic solar-driven valorization of CO2 and biomass using natural mineral based catalyst. [ABSTRACT FROM AUTHOR]