src/Gargantext/Text.hs

   1 {-|
   2 Module      : Gargantext.Text
   3 Description : Ngrams tools
   4 Copyright   : (c) CNRS, 2018
   5 License     : AGPL + CECILL v3
   6 Maintainer  : team@gargantext.org
   7 Stability   : experimental
   8 Portability : POSIX
   9
  10 Text gathers terms in unit of contexts.
  11
  12 -}
  13
  14
  15 module Gargantext.Text
  16   where
  17
  18 import Data.Text (Text, split)
  19 import Gargantext.Prelude hiding (filter)
  20 import NLP.FullStop (segment)
  21 import qualified Data.Text as DT
  22
  23 -----------------------------------------------------------------
  24
  25 class HasText h
  26   where
  27     hasText :: h -> [Text]
  28
  29 -----------------------------------------------------------------
  30 -- French words to distinguish contexts
  31 newtype Texte      = Texte      Text
  32 newtype Paragraphe = Paragraphe Text
  33 newtype Phrase     = Phrase     Text
  34 newtype MultiTerme = MultiTerme Text
  35 newtype Mot        = Mot        Text
  36 newtype Lettre     = Lettre     Text
  37
  38 -- | Type syn seems obvious
  39 type    Titre      = Phrase
  40
  41 -----------------------------------------------------------------
  42
  43 instance Show Texte where
  44   show (Texte t) = show t
  45
  46 instance Show Paragraphe where
  47   show (Paragraphe p) = show p
  48
  49 instance Show Phrase where
  50   show (Phrase p) = show p
  51
  52 instance Show MultiTerme where
  53   show (MultiTerme mt) = show mt
  54
  55 instance Show Mot where
  56   show (Mot t) = show t
  57
  58 instance Show Lettre where
  59   show (Lettre l) = show l
  60
  61 -----------------------------------------------------------------
  62
  63 class Collage sup inf where
  64   dec ::  sup  -> [inf]
  65   inc :: [inf] -> sup
  66
  67 instance Collage Texte Paragraphe where
  68   dec (Texte t) = map Paragraphe $ DT.splitOn "\n" t
  69   inc           = Texte . DT.intercalate "\n" . map (\(Paragraphe t) -> t)
  70
  71 instance Collage Paragraphe Phrase where
  72   dec (Paragraphe t) = map Phrase $ sentences t
  73   inc                = Paragraphe . DT.unwords . map (\(Phrase p) -> p)
  74
  75 instance Collage Phrase MultiTerme where
  76   dec (Phrase t) = map MultiTerme $ DT.words t
  77   inc            = Phrase . DT.unwords . map (\(MultiTerme p) -> p)
  78
  79 instance Collage MultiTerme Mot where
  80   dec (MultiTerme mt) = map Mot $ DT.words mt
  81   inc                 = MultiTerme . DT.intercalate " " . map (\(Mot m) -> m)
  82
  83 -------------------------------------------------------------------
  84 -- Contexts of text
  85 sentences :: Text -> [Text]
  86 sentences txt = map DT.pack $ segment $ DT.unpack txt
  87
  88 sentences' :: Text -> [Text]
  89 sentences' txt = split isCharStop txt
  90
  91 isCharStop :: Char -> Bool
  92 isCharStop c = c `elem` ['.','?','!']
  93
  94 unsentences :: [Text] -> Text
  95 unsentences txts = DT.intercalate " " txts
  96
  97 -- | https://en.wikipedia.org/wiki/Text_mining
  98 testText_en :: Text
  99 testText_en = DT.pack "Text mining, also referred to as text data mining, roughly equivalent to text analytics, is the process of deriving high-quality information from text. High-quality information is typically derived through the devising of patterns and trends through means such as statistical pattern learning. Text mining usually involves the process of structuring the input text (usually parsing, along with the addition of some derived linguistic features and the removal of others, and subsequent insertion into a database), deriving patterns within the structured data, and finally evaluation and interpretation of the output. 'High quality' in text mining usually refers to some combination of relevance, novelty, and interestingness. Typical text mining tasks include text categorization, text clustering, concept/entity extraction, production of granular taxonomies, sentiment analysis, document summarization, and entity relation modeling (i.e., learning relations between named entities). Text analysis involves information retrieval, lexical analysis to study word frequency distributions, pattern recognition, tagging/annotation, information extraction, data mining techniques including link and association analysis, visualization, and predictive analytics. The overarching goal is, essentially, to turn text into data for analysis, via application of natural language processing (NLP) and analytical methods. A typical application is to scan a set of documents written in a natural language and either model the document set for predictive classification purposes or populate a database or search index with the information extracted."
 100
 101
 102 testText_en_2 :: Text
 103 testText_en_2 = DT.pack "It is hard to detect important articles in a specific context. Information retrieval techniques based on full text search can be inaccurate to identify main topics and they are not able to provide an indication about the importance of the article. Generating a citation network is a good way to find most popular articles but this approach is not context aware. The text around a citation mark is generally a good summary of the referred article. So citation context analysis presents an opportunity to use the wisdom of crowd for detecting important articles in a context sensitive way. In this work, we analyze citation contexts to rank articles properly for a given topic. The model proposed uses citation contexts in order to create a directed and edge-labeled citation network based on the target topic. Then we apply common ranking algorithms in order to find important articles in this newly created network. We showed that this method successfully detects a good subset of most prominent articles in a given topic. The biggest contribution of this approach is that we are able to identify important articles for a given search term even though these articles do not contain this search term. This technique can be used in other linked documents including web pages, legal documents, and patents as well as scientific papers."
 104
 105
 106 -- | https://fr.wikipedia.org/wiki/Fouille_de_textes
 107 testText_fr :: Text
 108 testText_fr = DT.pack "La fouille de textes ou « l'extraction de connaissances » dans les textes est une spécialisation de la fouille de données et fait partie du domaine de l'intelligence artificielle. Cette technique est souvent désignée sous l'anglicisme text mining. Elle désigne un ensemble de traitements informatiques consistant à extraire des connaissances selon un critère de nouveauté ou de similarité dans des textes produits par des humains pour des humains. Dans la pratique, cela revient à mettre en algorithme un modèle simplifié des théories linguistiques dans des systèmes informatiques d'apprentissage et de statistiques. Les disciplines impliquées sont donc la linguistique calculatoire, l'ingénierie des langues, l'apprentissage artificiel, les statistiques et l'informatique."
 109
 110 termTests :: Text
 111 termTests = "It is hard to detect important articles in a specific context. Information retrieval techniques based on full text search can be inaccurate to identify main topics and they are not able to provide an indication about the importance of the article. Generating a citation network is a good way to find most popular articles but this approach is not context aware. The text around a citation mark is generally a good summary of the referred article. So citation context analysis presents an opportunity to use the wisdom of crowd for detecting important articles in a context sensitive way. In this work, we analyze citation contexts to rank articles properly for a given topic. The model proposed uses citation contexts in order to create a directed and edge-labeled citation network based on the target topic. Then we apply common ranking algorithms in order to find important articles in this newly created network. We showed that this method successfully detects a good subset of most prominent articles in a given topic. The biggest contribution of this approach is that we are able to identify important articles for a given search term even though these articles do not contain this search term. This technique can be used in other linked documents including web pages, legal documents, and patents as well as scientific papers."
 112
 113
 114 -- | Ngrams Test
 115 -- >> ngramsTest testText
 116 -- 248
 117 --ngramsTest :: Text -> Int
 118 --ngramsTest x =  length ws
 119 --  where
 120 --    --txt = concat <$> lines <$> clean <$> readFile filePath
 121 --    txt = clean x
 122 --    -- | Number of sentences
 123 --    --ls   = sentences $ txt
 124 --    -- | Number of monograms used in the full text
 125 --    ws   = ngrams    $ txt
 126 --    -- | stem ngrams
 127     -- TODO
 128     -- group ngrams
 129     --ocs  = occ       $ ws
 130