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has been cited by the following article:

• TITLE: Text Rank for Domain Specific Using Field Association Words

  AUTHORS: Omnia G. El Barbary, El Sayed Atlam

  KEYWORDS: Text Rank, Keyphrase Extraction, Field Association Words, Information Retrieval

  JOURNAL NAME: Journal of Computer and Communications, Vol.8 No.11, November 20, 2020

  ABSTRACT: Text Rank is a popular tool for obtaining words or phrases that are important for many Natural Language Processing (NLP) tasks. This paper presents a practical approach for Text Rank domain specific using Field Association (FA) words. We present the keyphrase separation technique not for a single document, although for a particular domain. The former builds a specific domain field. The second collects a list of ideal FA terms and compounds FA terms from the specific domain that are considered to be contender keyword phrases. Therefore, we combine two-word node weights and field tree relationships into a new approach to generate keyphrases from a particular domain. Studies using the changed approach to extract key phrases demonstrate that the latest techniques including FA terms are stronger than the others that use normal words and its precise words reach 90%.