| dc.description.abstract | In Computational Linguistics there is an essential dichotomy between Linguists
and Computer Scientists. The rst ones, with a strong knowledge of
language structures, have not engineering skills. The second ones, contrariwise,
expert in computer and mathematics skills, do not assign values to basic
mechanisms and structures of language. Moreover, this discrepancy, especially
in the last decades, has increased due to the growth of computational
resources and to the gradual computerization of the world; the use of Machine
Learning technologies in Arti cial Intelligence problems solving, which
allows for example the machines to learn , starting from manually generated
examples, has been more and more often used in Computational Linguistics
in order to overcome the obstacle represented by language structures and its
formal representation.
The dichotomy has resulted in the birth of two main approaches to Computational
Linguistics that respectively prefers:
rule-based methods, that try to imitate the way in which man uses and
understands the language, reproducing syntactic structures on which
the understanding process is based on, building lexical resources as electronic
dictionaries, taxonomies or ontologies;
statistic-based methods that, conversely, treat language as a group of
elements, quantifying words in a mathematical way and trying to extract
information without identifying syntactic structures or, in some
algorithms, trying to confer to the machine the ability to learn these
structures.
One of the main problems is the lack of communication between these two
di erent approaches, due to substantial di erences characterizing them: on
the one hand there is a strong focus on how language works and on language
characteristics, there is a tendency to analytical and manual work. From other
hand, engineering perspective nds in language an obstacle, and recognizes in
the algorithms the fastest way to overcome this problem.
However, the lack of communication is not only an incompatibility: following
Harris, the best way to approach natural language, could result by taking the
best of both.
At the moment, there is a large number of open-source tools that perform
text analysis and Natural Language Processing. A great part of these tools are
based on statistical models and consist on separated modules which could be
combined in order to create a pipeline for the processing of the text. Many of these resources consist in code packages which have not a GUI (Graphical User
Interface) and they result impossible to use for users without programming
skills. Furthermore, the vast majority of these open-source tools support only
English language and, when Italian language is included, the performances
of the tools decrease signi cantly. On the other hand, open source tools for
Italian language are very few.
In this work we want to ll this gap by present a new hybrid framework
for the analysis of Italian texts. It must not be intended as a commercial tool,
but the purpose for which it was built is to help linguists and other scholars to
perform rapid text analysis and to produce linguistic data. The framework,
that performs both statistical and rule-based analysis, is called LG-Starship.
The idea is to built a modular software that includes, in the beginning, the
basic algorithms to perform di erent kind of analysis. Modules will perform
the following tasks:
Preprocessing Module: a module with which it is possible to charge a
text, normalize it or delete stop-words. As output, the module presents
the list of tokens and letters which compose the texts with respective
occurrences count and the processed text.
Mr. Ling Module: a module with which POS tagging and Lemmatization
are performed. The module also returns the table of lemmas
with the count of occurrences and the table with the quanti cation of
grammatical tags.
Statistic Module: with which it is possible to calculate Term Frequency
and TF-IDF of tokens or lemmas, extract bi-grams and tri-grams units
and export results as tables.
Semantic Module: which use The Hyperspace Analogue to Language
algorithm to calculate semantic similarity between words. The module
returns similarity matrices of words per word which can be exported
and analyzed.
SyntacticModule: which analyze syntax structures of a selected sentence
and tag the verbs and its arguments with semantic labels.
The objective of the Framework is to build an all-in-one platform for NLP
which allows any kind of users to perform basic and advanced text analysis.
With the purpose of make the Framework accessible to users who have not
speci c computer science and programming language skills, the modules have
been provided with an intuitive GUI. The framework can be considered hybrid in a double sense: as explained
in the previous lines, it uses both statistical and rule/based methods, by relying
on standard statistical algorithms or techniques, and, at the same time,
on Lexicon-Grammar syntactic theory. In addition, it has been written in
both Java and Python programming languages. LG-Starship Framework has
a simple Graphic User Interface but will be also released as separated modules
which may be included in any NLP pipelines independently.
There are many resources of this kind, but the large majority works for English.
There are very few free resources for Italian language and this work tries
to cover this need by proposing a tool which can be used both by linguists
or other scientist interested in language and text analysis who have no idea
about programming languages, as by computer scientists, who can use free
modules in their own code or in combination with di erent NLP algorithms.
The Framework takes the start from a text or corpus written directly by
the user or charged from an external resource. The LG-Starship Framework
work ow is described in the owchart shown in g. 1. The pipeline shows that the Pre-Processing Module is applied on original
imported or generated text in order to produce a clean and normalized preprocessed
text. This module includes a function for text splitting, a stop-word
list and a tokenization method. On the text preprocessed the Statistic Module
or the Mr. Ling Module can be applied. The rst one, which includes basic statistics algorithm as Term Frequency, tf-idf and n-grams extraction, produces
as output databases of lexical and numerical data which can be used to
produce charts or perform more external analysis; the second one, is divided
in two main task: a Pos tagger, based on the Averaged Perceptron Tagger [?]
and trained on the Paisà Corpus [Lyding et al., 2014], perform the Part-Of-
Speech Tagging and produce an annotated text. A lemmatization method,
which relies on a set of electronic dictionaries developed at the University of
Salerno [Elia, 1995, Elia et al., 2010], take as input the Postagged text and
produces a new lemmatized version of original text with information about
syntactic and semantic properties.
This lemmatized text, which can also be processed with the Statistic Module,
serves as input for two deeper level of text analysis carried out by both
the Syntactic Module and the Semantic Module.
The rst one lays on the Lexicon Grammar Theory [Gross, 1971, 1975] and
use a database of Predicate structures in development at the Department of
Political, Social and Communication Science. Its objective is to produce a
Dependency Graph of the sentences that compose the text.
The Semantic Module uses the Hyperspace Analogue to Language distributional
semantics algorithm [Lund and Burgess, 1996] trained on the Paisà
Corpus to produce a semantic network of the words of the text.
These work ow has been included in two di erent experiments in which
two User Generated Corpora have been involved.
The rst experiment represent a statistical study of the language of Rap
Music in Italy through the analysis of a great corpus of Rap Song lyrics downloaded
from on line databases of user generated lyrics.
The second experiment is a Feature-Based Sentiment Analysis project performed
on user product reviews. For this project we integrated a large domain
database of linguistic resources for Sentiment Analysis, developed in the past
years by the Department of Political, Social and Communication Science of
the University of Salerno, which consists of polarized dictionaries of Verbs,
Adjectives, Adverbs and Nouns.
These two experiment underline how the linguistic framework can be applied
to di erent level of analysis and to produce both Qualitative data and Quantitative
data.
For what concern the obtained results, the Framework, which is only at
a Beta Version, obtain discrete results both in terms of processing time that
in terms of precision. Nevertheless, the work is far from being considered
complete. More algorithms will be added to the Statistic Module and the
Syntactic Module will be completed. The GUI will be improved and made more attractive and modern and, in addiction, an open-source on-line version
of the modules will be published. [edited by author] | it_IT |
