src/Gargantext/Core/Text/Terms/Multi/RAKE.hs

   1 {-|
   2 Module      : Gargantext.Core.Text.Terms.Multi.RAKE
   3 Description : Rapid automatic keyword extraction (RAKE)
   4 Copyright   : (c) CNRS, 2017
   5 License     : AGPL + CECILL v3
   6 Maintainer  : team@gargantext.org
   7 Stability   : experimental
   8 Portability : POSIX
   9
  10 Personal notes for the integration of RAKE in Gargantext.
  11
  12 RAKE algorithm is a simple, rapid and effective algorithm to extract
  13 keywords that is very sensitive to the quality of the stop word list.
  14
  15 Indeed, the very first step starts from the stop words list to cut the
  16 text towards keywords extraction. The conTexT is the sentence level to
  17 compute the coccurrences and occurrences which are divided to compute
  18 the metric of one word. Multi-words metrics is equal to the sum of the
  19 metrics of each word.
  20
  21 Finally The metrics highlight longer keywords which highly depends of
  22 quality of the cut which depends on the quality of the stop word list.
  23
  24 As a consequence, to improve the effectiveness of RAKE algorithm, I am
  25 wondering if some bayesian features could be added to increase stop word
  26 list quality in time.
  27
  28 -}
  29
  30
  31 module Gargantext.Core.Text.Terms.Multi.RAKE (multiterms_rake, select, hardStopList)
  32   where
  33
  34 import Data.Text (Text)
  35 import NLP.RAKE.Text
  36
  37 import Gargantext.Core.Text.Samples.EN (stopList)
  38 import Gargantext.Prelude
  39
  40 select :: Double -> [a] -> [a]
  41 select part ns = take n ns
  42   where
  43     n = round $ part * (fromIntegral $ length ns)
  44
  45
  46 multiterms_rake :: Text -> [WordScore]
  47 multiterms_rake = candidates hardStopList
  48                         defaultNosplit
  49                         defaultNolist   . pSplitter
  50
  51 -- | StopList
  52 hardStopList :: StopwordsMap
  53 hardStopList =  mkStopwordsStr stopList