## 1 Introduction  
With the rapid development of the Internet, short text in the Web increased rapidly. These short texts are the main constituent forms of many Web applications, such as search queries, news titles, and social media comments [1]. Therefore, short texts have become a prominent part of natural language processing. However, short texts contain many mentions whose meanings are often ambiguous and cannot be effectively linked to the knowledge base. Fortunately, entity linking as a critical step in many natural language processing tasks can solve this problem. The entity-linking task aims at linking a given mention in a piece of text to the correct entity in a specific knowledge base [2]. Hence, entity linking for short texts plays an increasingly important role in a variety of industrial applications. This paper focuses on entity linking for short texts, which usually consist of only a few or a few tens of terms, while long texts typically consist of a single document. Traditional entity linking are primarily for long texts [3, 4, 5], which contain rich contextual information and background knowledge that can facilitate disambiguation. Nevertheless, there are fewer studies on entity linking for short texts because short texts have the following challenges compared to long texts. To begin, short texts have insufficient contextual information and sparse semantic expression that are hardly distinguishable by contextual semantics [6]. Next, the short texts contain some informal expressions, which increase the semantic ambiguity of the short texts. Therefore, entity linking in short texts can be more complicated due to the above challenges. Recently, research entity linking for Chinese short text has gradually increased. The obvious difference between Chinese and English is the lack of space separators in Chinese sentences, so it can be more challenging to understand the contextual semantics in Chinese than in English. Moreover, the current entity linking for Chinese short-text methods does not consider the anisotropy problem of the sentence vector output by the pre-trained language model. This anisotropy refers to the fact that word embeddings occupy a narrow cone in the vector space, which leads to semantic information not being further fully utilized. In addition, the current entity linking for Chinese short-text methods ignore the interaction between labels and original texts when classifying unlinkable entities. Owing to these challenges and problems, traditional entity-linking methods cannot handle Chinese short text well, and how to effectively represent semantics and make full use of the interaction between labels and texts remains a challenging problem for entity linking in Chinese short text. To more effectively address the challenge of Chinese short text in entity linking, we propose an entity-linking model based on RoBERTa sentence vector normalization with label embedding to fully utilize semantic information. First, context semantic information is adequately captured in sentence vectors by the RoBERTa model. In the next step, to better characterize the semantics, the sentence vectors are input to a post-processing model to correct the anisotropy. Specifically, the RoBERTa output sentence vectors are fed into a flow model and the standard Gaussian distribution is utilized to transform the sentence vectors into a smooth, isotropic spatial distribution to better describe the semantics. Furthermore, entity typing is applied to unlinkable mentions to enhance the performance of entity linking, and the interaction between label embedding and text embedding is further adapted to enhance the multi-classification of entity types. The main contributions of this paper are detailed below:  
(1) We design a model based on sentence vector normalization and label embedding, which can sufficiently capture the semantic information in Chinese short texts and effectively utilize the original label information.