Summary Short text similarity plays an important role in natural language processing (NLP). It has been applied in many fields. Due to the lack of sufficient context in the short text, it is difficult to measure the similarity. The use of semantics similarity to calculate textual similarity has attracted the attention of academia and industry and achieved better results. In this survey, we have conducted a comprehensive and systematic analysis of semantic similarity. We first propose three categories of semantic similarity: corpus‐based, knowledge‐based, and deep learning (DL)‐based. We analyze the pros and cons of representative and novel algorithms in each category. Our analysis also includes the applications of these similarity measurement methods in other areas of NLP. We then evaluate state‐of‐the‐art DL methods on four common datasets, which proved that DL‐based can better solve the challenges of the short text similarity, such as sparsity and complexity. Especially, bidirectional encoder representations from transformer model can fully employ scarce information of short texts and semantic information and obtain higher accuracy and F1 value. We finally put forward some future directions.

No abstract

Knowledge modeling is an important step in building knowledge-based applications. Understanding the processes of knowledge modeling and the techniques involved can help developers to grasp the knowledge

We suggest the C2 modeling method to develop a simulation model for training command groups which consist of commanders and staffs. By using C2 models in constructive simulation models, combat entities or units directly receive and execute orders from a command group without mediating human role players. We also compare combat results from suggested modeling method with the results of existing models by building and implementing a simulation model with C2 models. Our analysis by comparison demonstrates advantages of suggested method to model C2 for computer assisted exercises.

No abstract