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 {-# LANGUAGE NoImplicitPrelude #-}
  15 {-# LANGUAGE OverloadedStrings #-}
  16
  17 module Gargantext.Text
  18   where
  19
  20 import Data.Text (Text, split)
  21 import qualified Data.Text as DT
  22
  23 import NLP.FullStop (segment)
  24 -----------------------------------------------------------------
  25 import Gargantext.Core
  26 import Gargantext.Prelude hiding (filter)
  27 -----------------------------------------------------------------
  28
  29 type Config  = Lang -> Context
  30 type Context = Text -> [Text]
  31 data Viz = Graph | Phylo | Chart
  32
  33 -----------------------------------------------------------------
  34 -------------------------------------------------------------------
  35 -- Contexts of text
  36 sentences :: Text -> [Text]
  37 sentences txt = map DT.pack $ segment $ DT.unpack txt
  38
  39 sentences' :: Text -> [Text]
  40 sentences' txt = split isStop txt
  41
  42 isStop :: Char -> Bool
  43 isStop c = c `elem` ['.','?','!']
  44
  45 unsentences :: [Text] -> Text
  46 unsentences txts = DT.intercalate " " txts
  47
  48 -- | https://en.wikipedia.org/wiki/Text_mining
  49 testText_en :: Text
  50 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."
  51
  52
  53 testText_en_2 :: Text
  54 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."
  55
  56
  57 -- | https://fr.wikipedia.org/wiki/Fouille_de_textes
  58 testText_fr :: Text
  59 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."
  60
  61 termTests :: Text
  62 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."
  63
  64
  65 -- | Ngrams Test
  66 -- >> ngramsTest testText
  67 -- 248
  68 --ngramsTest :: Text -> Int
  69 --ngramsTest x =  length ws
  70 --  where
  71 --    --txt = concat <$> lines <$> clean <$> readFile filePath
  72 --    txt = clean x
  73 --    -- | Number of sentences
  74 --    --ls   = sentences $ txt
  75 --    -- | Number of monograms used in the full text
  76 --    ws   = ngrams    $ txt
  77 --    -- | stem ngrams
  78     -- TODO
  79     -- group ngrams
  80     --ocs  = occ       $ ws
  81