Resolving Ambiguities in Named Entity Recognition Using Machine Learning

Named entity recognition (NER) is a subtask of the information extraction. NER system reads the text and highlights the entities. NER will separate different entities according to the project. NER is the process of two steps. The steps are detection of names and classifications of them. The first step is further divided into the segmentation. The second step will consist to choose an ontology which will organize the things categorically. Document summarization is also called automatic summarization. It is a process in which the text document with the help of software will create a summary by selecting the important points of the original text. In this chapter, the authors explain how document summarization is performed using named entity recognition. They discuss about the different types of summarization techniques. They also discuss about how NER works and its applications. The libraries available for NER-based information extraction are explained. They finally explain how NER is applied into document summarization.

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Resolving Ambiguities in Named Entity Recognition Using Machine Learning

Cited by 2 publications

References 8 publications

Exploratory Analysis of the News in Easy Language (NiEL) Corpus to Identify Characteristic Patterns for Natural Language Processing

Exploratory Analysis of the News in Easy Language (NiEL) Corpus to Identify Characteristic Patterns for Natural Language Processing

Named Entity Recognition in Document Summarization

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