Text Rank for Domain Specific Using Field Association Words

Omnia G. El Barbary, El Sayed Atlam
2020 Journal of Computer and Communications  
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
more » ... 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%. Communications text summaries, text categorization, retrieving data, etc. The maximum is the collection of significant and topical sentences from either a text or subject corpus. However, the major issue is far from being solved: state-of-the-art accomplishment in the extraction of keyphrases was much significantly smaller than many core NLP tasks [5] . The similitude of extracting keyphrases from either a document or a field is that they're using similar algorithms. Given the fact that Term Frequency and Inverse Document Frequency (TFIDF) is used to measure the domain weighted in a strengthened Text Rank system for Keyphrase extracting in prior gallate [6] [7], it is badly performed when retrieving domain-specific keyphrases. The difficulty comes throughout the particular field. Keyphrase excavation requires so many regions related to key knowledge whereas the extraction of data file keyphrases hardly concerns the topic of a single document. The primary objective of the whole article is to study how to use field association words to strengthen domain-specific keyphrase extraction based on Text Rank using field association words. The extracting of Domain-specific Keyphrase requires 3 stages. First, we defined a framework of the domain corpus. Secondly, extricate a list of different domain words or phrases using field association words algorithm, and obtain great and semi-perfect FA words. Keyphrases can annotate a domain's main meaning and are normally nouns, adjectives and verbs, but shouldn't be worthless words such as stop words. Thereby
doi:10.4236/jcc.2020.811005 fatcat:whz77j6n6bdhvahvdo2d7hgfau