Analisi Linguistica e Stilostatistica - Uno Studio Predittivo sul Campo
Authors: Rodolfo Delmonte (Università Ca' Foscari Venezia, 2014)
Topic: Text Simplification

Abstract
In this work we present a field study to define a precise evaluation scheme for text styling that has been used to establish a graduation of various documents based on their persuasion skills and readability. The study concerns the documents of the political programs published on a public forum by candidates to the University of Ca‚Äô Foscari ‚Äì Venice. The documents were analyzed by our system and a graduation was created on the basis of scores associated with eleven parameters. After the vote, we created the graduation and we discovered that the system had predicted the name of the real winner in advance. The results were published in a local newspaper.

Introduction
The analysis begins with the idea that the style of a programming document is composed of quantitative elements at the word level, of elements derived from the frequent use of certain synthetic structures and from unquisitely semantic and pragmatic characteristics such as the use of words and concepts that inspire positivity. I conducted the analysis starting from the texts available on the web or received from the candidates, using a series of parameters that I created for the analysis of the political speech in the Italian newspapers during the last and last government crisis. The results are published in some work nationally and internationally that I have listed in a brief bibliography. The analysis uses classical quantitative data such as the types/tokens ratio and then introduces information derived from the GETARUNS system that makes a complete parsing of texts from a syntax, semantic and pragmatic point of view. The data listed in the tables are derived from the system output files. The system produces a file for each phrase, a comprehensive file for the semantic analysis of the text and a file with the verticalized version of the text analyzed where each word is accompanied by a synthetic-semanticapragmatic classification. The system consists of a parser to transition networks increased by subcategory information, which first builds the chunks and then cascades the structures to higher complex constituents up to that of phrase. This representation is translated to another view that works on islands, starting from each verbal complex, corresponding to the verbal constituent. The parser to islands identifies the predicated-argumental structure, including the additions on the basis of the information contained in a subcategory for Italian built in previous projects, containing approximately 50,000 verbal and objective entries at different levels of depth. It is also used a list of selection preferences for verbs, names and acts derived from Italian treebanks available and containing approximately 30,000 income. In Table 1, we report the data in absolute numbers.

Conclusion
If you want to do a comprehensive graduation, you can consider that each parameter may have a positive or negative value. If it is positive, the person with the greater amount will be attributed as a reward the value 5 and the others to scale a value below one point, up to the value 1. In the event that the parameter has a negative value, the candidate with the greater amount will receive the score below 1 and the others to scale a value above one point to 5. The overall graduation will then be shaped making the sum of all the individual points obtained. The attribution of polarity to each parameter follows linguistic and stylistic criteria, and is as follows: 1. NullSubject - positive: The major number of zero subjects indicates the will to create a very coherent text and not to overload the reference to the same entity with repeated or core reference forms. The two. Subjective Props ‚Äì negative: The majority of proposals that express a subjective content indicates the subject‚Äôs tendency to expose his own ideas in a non-objective way. and 3. Negative Props - negative: The major use of negative propositions, i.e. with the use of denial or negative warnings, is a stylistic trait that is not propositive but tends to contradict what said or done by others. and 4. Non-factive Props - negative: The use of non-factive propositions indicates the stylistic tendency to expose their own ideas using unrealistic verbal times and ways - conjunctive, conditional, future and indefinite times. 5 of 5. Props / Sents - negative: The ratio that indicates the number of propositions per phrase is considered negative to mean that the higher the greater is the complexity of the style. 6 of 6. Negative Ws - negative: The number of negative words used in proportion to the total number of words has a negative value. 7 of 7. Positive Ws - positive: The number of positive words used in proportion to the total number of words has a positive value. 8 of 8. Passive Diath - negative: The number of passive forms used is considered negative as dark the agent of the action described. 9 of 9. Token / Sents - negative: The number of tokens in relation to the expressed phrases is treated as a negative factor again in relation to the problem of the induced complexity. 10 of 10. Vr - Rw - negative: This measure considers vocabulary wealth based on the so-called RareWords, or the total number of Hapax/Dis/Tris Legomena in the Rank List. The more unique or less frequent words the more complex the style is. 11 of 11. Vr - Tt - negative: As above, this time considering the total number of Types. The award of the score on the basis of the criteria indicated defines the following final graduation: Bugliesi 47 LiCalzi 36 Brugiavini 28 Cardinaletti 27 Bertinetti 27 Table 2. Final graduation based on the 11 parameters (see Tab. 2.1 in Appendix 2) Wishing to include also the points relating to the use of PERSONALE and its names we will have this overall result: Bulliesi 53 LiCalzi 44 Brugiavini 37 Cardinaletti 31 Bertinetti 30 *Table 3. Final graduation based on the 13 parameters (see Tab. 3.1 in Appendix 2) Using the parameters as judgment elements to classify the style of the candidates and assigning a word assessment, the two subsequent judgments are obtained. 1 of 1. Bugliesi has won perch≈Ω has used a more coherent style, with a simpler vocabulary, of simple and direct synthetic structures, expressing the contents in a concrete and fatal way, speaking at all levels of stakeholders, teachers and non- teachers. He also used fewer negative expressions and phrases and more positive expressions. The data also tells us that the Bugliesi program is in strong correlation with that of LiCalzi but not with that of the other candidates. Cardinaletti has written a program that uses a uncohered style, with a somewhat elaborate vocabulary, with quite more complex synthetic structures, expressing the contents in a much less concrete and much less fatal way, speaking to all levels of stakeholders, teachers and non- teachers. He also used few negative expressions and phrases and relatively few positive expressions. Finally, the Cardinaletti program is in good correlation with the Brugiavini program.