Open AccessPrediction and Analysis of Extracting Relations using Spacy Model
Author(s) -
S. G.,
R Porkodi
Publication year - 2020
Publication title -
international journal of recent technology and engineering
Language(s) - English
Resource type - Journals
ISSN - 2277-3878
DOI - 10.35940/ijrte.f8524.038620
Subject(s) - automatic summarization , computer science , relationship extraction , natural language processing , information extraction , parsing , artificial intelligence , preprocessor , information retrieval , task (project management) , precision and recall , natural language , relation (database) , sentiment analysis , object (grammar) , data mining , management , economics
Text is an extremely rich resources of information. Each and every second, minutes, peoples are sending or receiving hundreds of millions of data. There are various tasks involved in NLP are machine learning, information extraction, information retrieval, automatic text summarization, question-answered system, parsing, sentiment analysis, natural language understanding and natural language generation. The information extraction is an important task which is used to find the structured information from unstructured or semi-structured text. The paper presents a methodology for extracting the relations of biomedical entities using spacy. The framework consists of following phases such as data creation, load and converting the data into spacy object, preprocessing, define the pattern and extract the relations. The dataset is downloaded from NCBI database which contains only the sentences. The created model evaluated with performance measures like precision, recall and f-measure. The model achieved 87% of accuracy in retrieving of entities relation.