src/Gargantext/Text/Terms.hs

   1 {-|
   2 Module      : Gargantext.Text.Ngrams
   3 Description : Ngrams definition and tools
   4 Copyright   : (c) CNRS, 2017 - present
   5 License     : AGPL + CECILL v3
   6 Maintainer  : team@gargantext.org
   7 Stability   : experimental
   8 Portability : POSIX
   9
  10 An @n-gram@ is a contiguous sequence of n items from a given sample of
  11 text. In Gargantext application the items are words, n is a non negative
  12 integer.
  13
  14 Using Latin numerical prefixes, an n-gram of size 1 is referred to as a
  15 "unigram"; size 2 is a "bigram" (or, less commonly, a "digram"); size
  16 3 is a "trigram". English cardinal numbers are sometimes used, e.g.,
  17 "four-gram", "five-gram", and so on.
  18
  19 Source: https://en.wikipedia.org/wiki/Ngrams
  20
  21 TODO
  22 group Ngrams -> Tree
  23 compute occ by node of Tree
  24 group occs according groups
  25
  26 compute cooccurrences
  27 compute graph
  28
  29 -}
  30
  31 {-# LANGUAGE NoImplicitPrelude #-}
  32 {-# LANGUAGE TemplateHaskell   #-}
  33
  34 module Gargantext.Text.Terms
  35   where
  36
  37 import Control.Lens
  38 import Data.Text (Text)
  39 import Data.Traversable
  40 import GHC.Base (String)
  41
  42 import Gargantext.Prelude
  43 import Gargantext.Core
  44 import Gargantext.Core.Types
  45 import Gargantext.Text.Terms.Multi (multiterms)
  46 import Gargantext.Text.Terms.Mono  (monoTerms)
  47
  48 import qualified Data.List as List
  49 import qualified Data.Text as Text
  50 import Gargantext.Text (sentences)
  51 import Gargantext.Text.Terms.Mono.Token.En (tokenize)
  52 import Gargantext.Text.Eleve (testEleve)
  53
  54 data TermType lang
  55   = Mono      { _tt_lang :: lang }
  56   | Multi     { _tt_lang :: lang }
  57   | MonoMulti { _tt_lang :: lang }
  58
  59 makeLenses ''TermType
  60
  61 --group :: [Text] -> [Text]
  62 --group = undefined
  63
  64 -- remove Stop Words
  65 -- map (filter (\t -> not . elem t)) $
  66 ------------------------------------------------------------------------
  67 -- | Sugar to extract terms from text (hiddeng mapM from end user).
  68 --extractTerms :: Traversable t => TermType Lang -> t Text -> IO (t [Terms])
  69 extractTerms :: TermType Lang -> [Text] -> IO [[Terms]]
  70 extractTerms termTypeLang = mapM (terms termTypeLang)
  71 ------------------------------------------------------------------------
  72 -- | Terms from Text
  73 -- Mono : mono terms
  74 -- Multi : multi terms
  75 -- MonoMulti : mono and multi
  76 -- TODO : multi terms should exclude mono (intersection is not empty yet)
  77 terms :: TermType Lang -> Text -> IO [Terms]
  78 terms (Mono      lang) txt = pure $ monoTerms lang txt
  79 terms (Multi     lang) txt = multiterms lang txt
  80 terms (MonoMulti lang) txt = terms (Multi lang) txt
  81 -- terms (WithList  list) txt = pure . concat $ extractTermsWithList list txt
  82 ------------------------------------------------------------------------
  83
  84 isPunctuation :: Text -> Bool
  85 isPunctuation x = List.elem x $  (Text.pack . pure)
  86                              <$> ("!?(),;." :: String)
  87
  88 -- | Unsupervised ngrams extraction
  89 -- language agnostic extraction
  90 -- TODO: remove IO
  91 -- TODO: BlockText
  92 extractTermsUnsupervised :: Int -> Text -> IO [[Text]]
  93 extractTermsUnsupervised n =
  94                fmap List.nub
  95              . fmap (List.filter (\l -> List.length l > 1))
  96              . testEleve n
  97              . map (map Text.toLower)
  98              . map (List.filter (not . isPunctuation))
  99              . map tokenize
 100              . sentences
 101