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 import Gargantext.Text.Terms.Mono.Stem (stem)
  48
  49 import qualified Data.Set  as Set
  50 import qualified Data.List as List
  51 import qualified Data.Text as Text
  52 import Gargantext.Text (sentences)
  53 import Gargantext.Text.Terms.Mono.Token.En (tokenize)
  54 import Gargantext.Text.Terms.Eleve (mainEleveWith, Tries, Token, buildTries, toToken)
  55
  56 data TermType lang
  57   = Mono      { _tt_lang :: lang }
  58   | Multi     { _tt_lang :: lang }
  59   | MonoMulti { _tt_lang :: lang }
  60   | Unsupervised { _tt_lang  :: lang
  61                  , _tt_size  :: Int
  62                  , _tt_model :: Maybe (Tries Token ())
  63   }
  64 makeLenses ''TermType
  65
  66 --group :: [Text] -> [Text]
  67 --group = undefined
  68
  69 -- remove Stop Words
  70 -- map (filter (\t -> not . elem t)) $
  71 ------------------------------------------------------------------------
  72 -- | Sugar to extract terms from text (hiddeng mapM from end user).
  73 --extractTerms :: Traversable t => TermType Lang -> t Text -> IO (t [Terms])
  74 extractTerms :: TermType Lang -> [Text] -> IO [[Terms]]
  75
  76 extractTerms (Unsupervised l n m) xs = mapM (terms (Unsupervised l n (Just m'))) xs
  77   where
  78     m' = case m of
  79       Just m''-> m''
  80       Nothing -> newTries n (Text.intercalate " " xs)
  81
  82 extractTerms termTypeLang xs = mapM (terms termTypeLang) xs
  83
  84
  85
  86 ------------------------------------------------------------------------
  87 -- | Terms from Text
  88 -- Mono : mono terms
  89 -- Multi : multi terms
  90 -- MonoMulti : mono and multi
  91 -- TODO : multi terms should exclude mono (intersection is not empty yet)
  92 terms :: TermType Lang -> Text -> IO [Terms]
  93 terms (Mono      lang) txt = pure $ monoTerms lang txt
  94 terms (Multi     lang) txt = multiterms lang txt
  95 terms (MonoMulti lang) txt = terms (Multi lang) txt
  96 terms (Unsupervised lang n m) txt = termsUnsupervised m' n lang txt
  97   where
  98     m' = maybe (newTries n txt) identity m
  99 -- terms (WithList  list) txt = pure . concat $ extractTermsWithList list txt
 100 ------------------------------------------------------------------------
 101
 102 text2term :: Lang -> [Text] -> Terms
 103 text2term _ [] = Terms [] Set.empty
 104 text2term lang txt = Terms txt (Set.fromList $ map (stem lang) txt)
 105
 106 isPunctuation :: Text -> Bool
 107 isPunctuation x = List.elem x $  (Text.pack . pure)
 108                              <$> ("!?(),;." :: String)
 109
 110 -- | Unsupervised ngrams extraction
 111 -- language agnostic extraction
 112 -- TODO: remove IO
 113 -- TODO: newtype BlockText
 114 termsUnsupervised :: Tries Token () -> Int -> Lang -> Text -> IO [Terms]
 115 termsUnsupervised m n l =
 116                pure
 117              . map (text2term l)
 118              . List.nub
 119              . (List.filter (\l' -> List.length l' > 1))
 120              . List.concat
 121              . mainEleveWith m n
 122              . uniText
 123
 124 newTries :: Int -> Text -> Tries Token ()
 125 newTries n t = buildTries n (fmap toToken $ uniText t)
 126
 127 uniText :: Text -> [[Text]]
 128 uniText = map (List.filter (not . isPunctuation))
 129         . map tokenize
 130         . sentences   -- | TODO get sentences according to lang
 131
 132