###### Abstract  
Named entity recognition (NER) is a natural language processing task to identify spans that mention named entities and to annotate them with predefined named entity classes. Although many NER models based on machine learning have been proposed, their performance in terms of processing fine-grained NER tasks was less than acceptable. This is because the training data of a fine-grained NER task is much more unbalanced than those of a coarse-grained NER task. To overcome the problem presented by unbalanced data, we propose a fine-grained NER model that compensates for the sparseness of fine-grained NEs by using the contextual information of coarse-grained NEs. From another viewpoint, many NER models have used different levels of features, such as part-of-speech tags and gazetteer look-up results, in a nonhierarchical manner. Unfortunately, these models experience the feature interference problem. Our solution to this problem is to adopt a multi-stacked feature fusion scheme, which accepts different levels of features as its input. The proposed model is based on multi-stacked long short-term memories (LSTMs) with a multi-stacked feature fusion layer for acquiring multilevel embeddings and a dual-stacked output layer for predicting fine-grained NEs based on the categorical information of coarse-grained NEs. Our experiments indicate that the proposed model is capable of state-of-the-art performance. The results show that the proposed model can effectively alleviate the unbalanced data problem that frequently occurs in a fine-grained NER task. In addition, the multi-stacked feature fusion layer contributes to the improvement of NER performance, confirming that the proposed model can alleviate the feature interference problem. Based on this experimental result, we conclude that the proposed model is well-designed to effectively perform NER tasks. fine-grained named entity recognition; \(k\)-stacked feature fusion; dual-stacked output; unbalanced data problem +
Footnote †: 2021: 11 November 2021  
with a large number of characters that do not use capitalization or word boundary by spacing (e.g., a spacing unit is not a word in Korean), the performance of these systems is unsatisfactory, that is, between 65% and 75% [1]. The main reason for the lower performance is that the training data for fine-grained NER are more unbalanced than those for coarse-grained NER. It is easy to find fine-grained NEs that seldom occur in training data. To alleviate this sparse data problem, we propose an NER model that compensates for the sparseness of fine-grained NEs with the contextual information of coarse-grained NEs that semantically include the fine-grained NEs. Table 1 presents examples of coarse-grained NE categories and their fine-grained NE categories. These examples show that the classes of coarse-grained NEs are supersets that are tightly associated with the classes of fine-grained NEs. If the fine-grained NE \"Seoul\" in Table 1 does not occur in the training data, the contextual information of the coarse-grained NE \"Washington, D.C.\" could be helpful in that it would enable the NE class \"City\" of \"Seoul\" to be inferred because they are both capital cities. Many NER systems actively use various linguistic and domain-specific features to improve their performance. For example, part-of-speech (POS) tags play an important role in detecting NE boundaries, and domain-specific gazetteers play a decisive role in determining NE categories. Previous NER models based on deep neural networks embedded various types of linguistic and domain-specific knowledge into vector spaces. Then, they used the embedded vectors as nonhierarchical features of input layers, although the embedded vectors imply different levels of knowledge (e.g., POS tags imply a grammatical level of linguistic knowledge, and entities in a gazetteer imply a semantic level of domain knowledge). In addition, some previous works have shown that different layers of deep RNNs encode different types of information [2].