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Chris Karageorg Kaneen edited this page Aug 10, 2018 · 6 revisions

Unit - RespAs Associations Extraction

For the purpose of detecting and extracting rough Unit of PAOrgs - RespAs pairs (as outlined in the Description) a set of RespA Classifiers were formulated and trained for (for detection) and a set of custom Extraction Methods (that exploit the former) were implemented for finding specific Unit-RespA sections in raw text.

  1. Issue and Article Classifiers

    These clfs were trained with data contained within RespA and non-RespA related and Articles, GG PAOrg Presidential Decree texts and irrelevant ones, respectively.

    The features that were chosen were are the most commonly occurring phrases phrases found within those types of texts, specifically:

    In both Issue and Article cases, the number of occurrences of each of the following phrase classes found in the list of word bigrams of the given text:

       'common_bigram_pairs':
   [
      ("αρμόδι", "για"),
      ("ευθύνη", "για"),
      ("εύθυν", "για"),									                           
      ("αρμοδιότητ", "ακόλουθ"),
      ("αρμοδιότητ", "μεταξύ"),								                           
      ("ρμοδιότητες", "τ")
   ]

    In conjunction with a corresponding set of phrase classes found in the list of word quadgrams of the given
    text:

       'common_quadgram_pairs':  
   [  
      ("αρμοδιότητ", "έχει"),  
      ("αρμοδιότητ", "εξής"),     																			                                   
      ("αρμοδιότητ", "είναι")  
   ]

    Thus, for example, the training data vector extracted from a RespA related PAOrg Pres. Decree Issue
    may be:

    [5, 1, 2, 0, 0, 10, 27, 81, 0, 1]

    Whereas, the corresponding vector for a non-RespA related PAOrg Pres. Decree Article may be:

    [0, 0, 1, 0, 0, 0, 0, 0, 0, 0]

    Where in both cases elements [1:9] are the feature values
    ( [1:6] : 'common_bigram_pairs' key phrase occurrences, [7:9] : 'common_quadgram_pairs' key phrase occurrences)
    and the last value is the target value (always 1 for RespA or 0 for non-RespA)

  2. Paragraph Classifier

    This clf was trained with data contained within RespA and non-RespA related Paragraphs, GG PAOrg
    Presidential Decree texts and irrelevant ones, from which two big dictionaries were made, as training data: A RespA and a non-RespA dictionary, both containing a dictionary of all unigram occurrences and a dictionary of all bigram occurrences of the given text.

    Which look like:

        {
      ...,
      'βασικές': 1, 'διοικητής': 2, 'στόχους': 5, 'οπτικοαουστικού': 1, 'παντός': 2, 'πρόσληψη': 3,                                 
      'αναγκαίων': 10,
      ...
    }
        {
      ...,
      ('συνεργασία', 'τμήμα'): 6, ('εκπαίδευση', 'μαθητές'): 1, ('πολιτικοοικονομική', 'κατάσταση'): 2,            
      ('έργων', 'οδοποιίας'): 1, ('επικουρεί', 'τεχνικά'): 1, ('θεσμικών', 'μεταβολών'): 1, ('τήρηση', 
      'μητρώου'): 4, ('αγορών', 'υπερτοπικό'): 1, ('υλικών', 'κεραμικών'): 1, ('προέλευσης',            
      'αντικειμένων'): 1, ('ανάπτυξη', 'προγραμμάτων'): 1, ('χρηματικού', 'υλικών'): 2,
      ...
    }

    For prediction, a unigram and a bigram dictionary is created for the given text and each is compared, using a cosine similarity metric, to the two corresponding big dictionaries of the RespA and non-RespA training data and a dual result is returned signifying the similarity to each RespA or non-RespA dictionary.

    For example, the return value:

    (True, False)

    would mean that according to the unigram training data similarity, the given text is RespA related, but according to the binary training data similarity, it is not.

    For the sake of extracting the required associations, some semi-manual methods for detecting different variants of Unit-RespA occurrences were also formulated.

  • Extraction Methods

    After manually analyzing different GG PAOrg Pres. Decree Issues methods for extracting the required associations were formulated based on the semi-manual methods mentioned above.

    Examples of the three most common Unit-RespAs occurrences:

,

and

On which specific heuristics are used to roughly find any lists containing RespAs and to disentangle Unit sections from them into OrderedDict, JSON or XML data which can then be exported.

Metadata Extraction

To accommodate a more general extraction scheme, simple regex-based methods were implemented to extract Decision Contents and Summaries, Numbers, Prerequisites, Bodies, Signing info and general Issue Articles, Numbers, Publication Dates, Categories, Types, Mentioned Issues, Serial Numbers.

  • For Decision Issues

Contents and Summaries:

Numbers:

Prerequisites:

Bodies:

Signing info:

  • For general Issues

Articles:

Numbers:

Publication Dates:

Categories:

Types:

Mentioned Issues:

Serial Numbers:

Extras

Moreover, you can also get an approximative list of PAOrgs from any type of GG Issue in the form of, e.g.:

[{'Κανονισμός Λειτουργίας Ενιαίας Ανεξάρτητης Αρχής Δημοσίων Συμβάσεων': ['ΕΝΙΑΙΑ ΑΝΕΞΑΡΤΗΤΗ ΑΡΧΗ ΔΗΜΟΣΙΩΝ ΣΥΜΒΑΣΕΩΝ ']}, {'Εθνικό Tυπογραφείο': ['ΕΘΝΙΚΟ ΤΥΠΟΓΡΑΦΕΙΟ']}, {'Υπουργού Ανάπτυξης Ανταγωνιστικότητας': ['ΥΠΟΥΡΓΕΙΟ ΑΝΑΠΤΥΞΗΣ ΚΑΙ ΑΝΤΑΓΩΝΙΣΤΙΚΟΤΗΤΑΣ']}, {'Τον ΚΑΔ': ['ΕΤΟΣ ΚΟΑ']}, {'Οργανισμός Ενιαίας Ανεξάρτητης Αρχής Δημοσίων Συμβάσεων': ['ΕΝΙΑΙΑ ΑΝΕΞΑΡΤΗΤΗ ΑΡΧΗ ΔΗΜΟΣΙΩΝ ΣΥΜΒΑΣΕΩΝ ']}, {'Υπουργείο Διοικητικής Ανασυγκρότησης': ['ΥΠΟΥΡΓΕΙΟ ΔΙΟΙΚΗΤΙΚΗΣ ΑΝΑΣΥΓΚΡΟΤΗΣΗΣ', 'ΥΠΟΥΡΓΕΙΟ ΕΣΩΤΕΡΙΚΩΝ ΚΑΙ ΔΙΟΙΚΗΤΙΚΗΣ ΑΝΑΣΥΓΚΡΟΤΗΣΗΣ']}, {'Ενιαία Ανεξάρτητη Αρχή Δημοσίων Συμβάσεων': ['ΕΝΙΑΙΑ ΑΝΕΞΑΡΤΗΤΗ ΑΡΧΗ ΔΗΜΟΣΙΩΝ ΣΥΜΒΑΣΕΩΝ ']}, {'Υπουργού Οικονομίας Ανάπτυξης': ['ΥΠΟΥΡΓΕΙΟ ΟΙΚΟΝΟΜΙΑΣ ΚΑΙ ΑΝΑΠΤΥΞΗΣ', 'ΥΠΟΥΡΓΕΙΟ ΟΙΚΟΝΟΜΙΑΣ, ΑΝΑΠΤΥΞΗΣ ΚΑΙ ΤΟΥΡΙΣΜΟΥ']}, {'Υπουργού Οικονομίας': ['ΥΠΟΥΡΓΕΙΟ ΟΙΚΟΝΟΜΙΚΩΝ']}, {'Υπουργών Οικονομίας': ['ΥΠΟΥΡΓΕΙΟ ΟΙΚΟΝΟΜΙΚΩΝ']}, {'Το Εθνικό Τυπογραφείο': ['ΕΘΝΙΚΟ ΤΥΠΟΓΡΑΦΕΙΟ']}, {'ΕΝΙΑΙΑ ΑΝΕΞΑΡΤΗΤΗ ΑΡΧΗ ΔΗΜΟΣΙΩΝ ΣΥΜΒΑΣΕΩΝ Έχοντας': ['ΕΝΙΑΙΑ ΑΝΕΞΑΡΤΗΤΗ ΑΡΧΗ ΔΗΜΟΣΙΩΝ ΣΥΜΒΑΣΕΩΝ ']}, {'Δημοσίων Συμβάσεων': ['ΔΗΜΟΣ ΣΥΜΗΣ', 'ΔΗΜΟΣ ΣΕΡΡΩΝ']}, {'Ενιαίας Ανεξάρτητης Αρχής Δημοσίων Συμβάσεων': ['ΕΝΙΑΙΑ ΑΝΕΞΑΡΤΗΤΗ ΑΡΧΗ ΔΗΜΟΣΙΩΝ ΣΥΜΒΑΣΕΩΝ ']}]

Through spacy's Greek Support fetching an spacy.nlp instance is also available for performing various NLP tasks.

In addition, you can easily fetch text analysis data through a method that makes an API request to text-analysis's web app: http://nlp.wordgames.gr/

For more information on the specifics of the aforementioned functionalities or how to use them and more, please visit: Use or API.

Since these methods are not perfect, any Contribution is more than welcome.

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