TY  - JOUR
TI  - Effect of Dynamic Stoplist on Keyword Prediction in RAKE
AU  - Avinash Bhat
AU  -  Chirag Satish
AU  -  Nihal D’Souza
AU  -  Nikhil Kashyap
JO  - International Journal of Scientific Research in Computer Science, Engineering and Information Technology
PB  - Technoscience Academy
DA  - 2018/05/08
PY  - 2018
DO  - https://doi.org/10.32628/IJSRCSEIT
UR  - https://ijsrcseit.com/CSEIT184650
VL  - 4
IS  - 6
SP  - 259	
EP  - 264
AB  - Keywords which we define as a sequence of words that provide a condensed representation of the document in question. These keywords are vital in numerous applications from web search engines to abstractive text summarization. Rapid Automatic Keyword Extraction (RAKE) [1] is an unsupervised, domain and language independent method for extracting keywords from documents. RAKE is based on the simple observation that keywords seldom contain stop words – such as and, of and the. RAKE uses a list of stop words to split the document text into candidate keywords. The list of stop words or stoplist is static. In this paper, we make the stoplist dynamic, in that, stop words, that do not currently belong to the stoplist but are identified as potential stop words for the given document are added to the stoplist. Consequently, every document has a unique stoplist. We compare the performance of our implementation to the standard RAKE implementation on Wikipedia articles.