Open AccessKeyphrase Extraction And Source Code Similarity Detection-A Survey
Author(s) -
Nakul Sharma,
Prasanth Yalla
Publication year - 2021
Publication title -
iop conference series. materials science and engineering
Language(s) - English
Resource type - Journals
eISSN - 1757-899X
pISSN - 1757-8981
DOI - 10.1088/1757-899x/1074/1/012027
Subject(s) - computer science , similarity (geometry) , domain (mathematical analysis) , code (set theory) , field (mathematics) , natural language processing , source code , information retrieval , feature extraction , artificial intelligence , situated , feature (linguistics) , linguistics , set (abstract data type) , mathematics , programming language , mathematical analysis , philosophy , pure mathematics , image (mathematics)
Keyphrase extraction is the starting phase of feature extraction and many other NLP tasks. Hence, keyphrase extraction ensures the separation of essential and relevant information from the rest of the document or corpus. A similarity score is used for detecting similarities between two or more documents. In this paper, a summary of existing research conducted in the field of keyphrase extraction and source code similarity detection is presented. The literature focuses on more concept-based research instead of going towards the domain level. There exist a promising potential for the creation of recommendation systems by employing similarly situated research techniques.