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Named-entity recognition (NER) is one of the primary components in various natural language processing tasks such as relation extraction, information retrieval, question answering, etc. The majority of the research work deals with flat entities. However, it was observed that the entities were often embedded within other entities. Most of the current state-of-the-art models deal with the problem of embedded/nested entity recognition with very complex neural network architectures. In this research work, we proposed to solve the problem of nested named-entity recognition using the transfer-learning approach. For this purpose, different variants of fine-tuned, pretrained, BERT-based language models were used for the problem using the joint-labeling modeling technique. Two nested named-entity-recognition datasets, i.e., GENIA and GermEval 2014, were used for the experiment, with four and two levels of annotation, respectively. Also, the experiments were performed on the JNLPBA dataset, which has flat annotation. The performance of the above models was measured using F1-score metrics, commonly used as the standard metrics to evaluate the performance of named-entity-recognition models. In addition, the performance of the proposed approach was compared with the conditional random field and the Bi-LSTM-CRF model. It was found that the fine-tuned, pretrained, BERT-based models outperformed the other models significantly without requiring any external resources or feature extraction. The results of the proposed models were compared with various other existing approaches. The best-performing BERT-based model achieved F1-scores of 74.38, 85.29, and 80.68 for the GENIA, GermEval 2014, and JNLPBA datasets, respectively. It was found that the transfer learning (i.e., pretrained BERT models after fine-tuning) based approach for the nested named-entity-recognition task could perform well and is a more generalized approach in comparison to many of the existing approaches.

Modern day supply chain has gone through many changes in order to satisfy the ever increasing needs of customers. One of such change is origin of omnichannel in supply chain which believes in not only satisfying customer’s needs but also delighting them. Implementation of omnichannel in practice comes with lots of challenges. These challenges are summarized as major research areas in omnichannel and being explored in this paper through literature. Further, distribution system in omnichannel demands flexibility in distribution which results in restructuring and reconfiguring the practices of traditional supply chain. For this purpose, a mathematical model has been developed under omni environment which provides the facility of flexible distribution for customers. This proposed model is bi-objective in nature which considers sustainability along with minimization of total supply chain cost. To check the feasibility of the proposed model, numerical illustrations have been carried out for two different scenarios. In first scenario, the proposed model is solved for single product and single time period while in second scenario similar conditions are considered for conventional Supply Chain Network (SCN) and is being compared with proposed model. The proposed MILP model is coded in GAMS and solved using CPLEX solver. The results obtained indicate that proposed model possess superiority over conventional SCN in many aspects. The findings from the proposed model ensure that customers are free to avail product and services from their desired channel as per their choice.