Tasks in Named Entity Recognition: Technologies and Tools

The task of named entity recognition (NER) is to identify and classify words and phrases denoting named entities (NEs), such as people, organizations, geographical names, dates, events, and terms from subject areas. While searching for the best solution, researchers conduct a wide range of experiments with different technologies and input data. A comparison of the results of these experiments shows a significant discrepancy in the quality of NER and poses the problem of determining the conditions and limitations for the application of the used technologies, as well as finding new solutions. An important part in answering these questions is the systematization and analysis of current research and the publication of relevant reviews. In the field of NE recognition, the authors of analytical articles primarily consider mathematical methods of identification and classification and do not pay attention to the specifics of the problem itself. In this survey, the field of NE recognition is considered from the point of view of individual task categories. The authors identify five categories: the classical task of NER, NER subtasks, NER in social media, NER in domain, and NER in natural language processing (NLP) tasks. For each category the authors discuss the quality of the solution, features of the methods, problems, and limitations. Information about current scientific works of each category is given in the form of a table for clarity. This review allows us to draw a number of conclusions. Deep learning methods are the leading methods among state-of-the-art technologies. The main problems are the lack of datasets in open access, strict requirements for computing resources, and the lack of error analysis. A promising area of research in NER is the development of methods based on unsupervised techniques or rule-based learning. Intensively developing language models in existing NLP tools can serve as a possible basis for text preprocessing for NER methods. The article ends with a description and results of experiments with NER tools for Russian-language texts.