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Named entity recognition (NER) is a widely used natural language processing technique; it plays a key role in information extraction from sentences. To be able to test the correctness of NER systems is important, but it is expensive because an automated test oracle is normally unavailable. To address the oracle problem, this study proposes to apply metamorphic testing (MT). The authors conduct a case study with Litigant, an industrial NER system of the Ant Group, and show that MT can effectively detect real-life bugs in the absence of an ideal oracle. The authors further investigate the causes for a series of entity recognition failures detected. Outcomes of this research further justify the application of MT to the natural language processing domain as well as provide hints for practitioners to improve the quality process of their NER systems.

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As an important part of economic development, warehousing logistics also needs to be transformed and upgraded in order to adapt to the development of the new situation. The RFID reader records the related information of the goods to improve the efficiency of warehouse operation by identifying the RFID tags attached to the goods in batches. This paper also proposes an improved group-based anti-collision algorithm (GMQT) to solve the problem of tag collision in the process of Radio Frequency Identification (RFID) identification. The simulation results show that the GMQT algorithm improves the recognition efficiency of the system. The algorithm has the advantages of small data transmission and stable performance; in particular, the recognition efficiency is not affected by the number of tags.

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