## 1 Introduction  
Knowledge Base Question Answering (KBQA) is a Natural Language Processing (NLP) task which is aimed at answering natural language questions automatically with facts in a Knowledge Base (KB). In a KBQA task, subject recognition (SR) is usually the prerequisite to obtain the answer to both single-relation questions and complex questions. In general, a general flat Named-Entity Recognition (NER) model is usually trained and employed in the SR task to recognize the entity in the question as the subject. However, sometimes, the golden entity recognized by the flat NER model is different from the golden subject in the subject recognition task of a similar question; examples are shown in Table 1.  
In these examples, the golden subject in the KB may be different from the golden entity recognized from the question by a flat NER model because of spelling errors, missed or unexpected words, characters, or spaces, unknown subject aliases, etc. In these cases, the golden subject could not be strictly matched to all n-grams, which could be generated from the question [1], so it is difficult to obtain the golden subject even though the golden entity is recognized. Further, if the recognized entity is not the golden one, it would be more difficult to obtain the golden subject. As a result, a flat NER model is not effective in these cases. Unfortunately, in practical applications, these cases would occur frequently: a question may be fed to a KBQA system after multiple processes of transmission, transformation, or translation, and the actual input question to a KBQA system may contain various errors caused by users or noise in these processes. In addition, it is impractical for a KB to contain all possible aliases to an entity, and for a NER model to achieve an accuracy of 100%. As a result, it is necessary to propose a model which is effective and robust to these questions. Before describing our proposed model, we first describe the general flat NER model under SR tasks, which is shown in Figure 1a. To the example question \"where is mission san buenaventura located\" with the golden subject \"mission san buenaventura\" (bold font), several successive tokens (gray background) in the question is selected as the recognized entity in a flat NER model (e.g., BERT-CRF). Then, the recognized entity is supposed to be strictly matched to a certain subject in the KB and the subject is outputted as the recognized subject. Obviously, if the recognized entity could not be strictly matched to any subject in the KB, this model would fail. In this case, an approximate matching strategy would be employed, as shown in Figure 1b. In this strategy, as \"mission san\" is the recognized entity, all possible tokens started with \"mission\" or ended with \"san\" in the question are approximately matched to all subjects in the KB. The best-matched one is outputted as the recognized subject. Further, entity tokens, start tokens, and end tokens could be considered as different types of entities in a NER model. As a token could belong to multiple types of entities, a nested NER model would be employed. As shown in Figure 1c, besides the recognized entity \"mission san\" (gray background), \"mission\" is recognized as the start token (green background) and \"located\" is recognized as the end token (blue background). Then, tokens starting with \"mission\" or ending with \"located\" in the question are approximately matched to subjects in the KB to find the best-matched one. In addition, as different NER models could recognize different entities to the same question, an integrated method could also be employed as shown in Figure 1d, which is our proposed model. In this model, a flat NER model (Model A) is employed to find the strictly matched subject first. If it fails, another nested NER model (Model B) will be employed to find the approximately matched subject. In this paper, we propose an Improved Nested NER Model (INNM) for subject recognition tasks under KBQA. In our model, a nested NER model is employed to label each question token.