Tint 2.0: an All-inclusive Suite for NLP in Italian
Authors: Alessio Palmero Aprosio, Giovanni Moretti (Fondazione Bruno Kessler, 2018)
Topic: Lexical and Semantic Resources and Analysis

Abstract
In this we paper present Tint 2.0, English. In this we paper present Tint 2.0, an open-source, fast and extendable Natural Language Processing suite for Italian based on Stanford CoreNLP. The new release includes some improvements of the existing NLP modules, and a set of new text processing components for ﬁnegrained linguistic analysis that were not available so far, including multi-word expression recognition, afﬁx analysis, readability and classiﬁcation of complex verb tenses.

Introduction
In recent years, Natural Language Processing (NLP) technologies have become fundamental to deal with complex tasks requiring text analysis, such as Question Answering, Topic Classiﬁcation, Text Simpliﬁcation, etc. Both research institutions and companies require accurate and reliable software for free and efﬁcient linguistic analysis, allowing programmers to focus on the core of their business or research. While most of the opensource NLP tools freely available on the web (such as Stanford CoreNLP1 and OpenNLP2) are designed for English and sometimes adapted to other languages, there is a lack of this kind of resources for Italian. In this paper, we present a novel, extended release of Tint (Palmero Aprosio and Moretti, 2016), a suite of ready-to-use modules for Italian NLP. It is free to use, open source, and can be downloaded and used out-of-the-box (see Section 6). Compared to the previous version, the suite has been enriched with several modules for ﬁne-grained linguistic analysis that were not available for Italian before. 2 Related work There are plenty of linguistic pipelines available for download. Most of them (such as Stanford CoreNLP and OpenNLP) are language independent and, even if they are not available in Italian out-of-the-box, they could be trained in every existing language. A notable example in this direction is UDpipe (Straka and Strakov´a, 2017), a trainable pipeline which performs most of the common NLP tasks and is available in more than 50 languages, and Freeling (Padr´o and Stanilovsky, 2012), a C++ library providing language analysis functionalities for a variety of languages. There are also some pipelines for Italian, such as TextPro (Emanuele Pianta and Zanoli, 2008), T2K (Dell’Orletta et al., 2014), and TaNL, but none of them are released as open source (and only TextPro can be downloaded and used for free for research purposes). Other single components are unfortunately available only upon request to the authors, for example the AnIta morphological analyser (Tamburini and Melandri, 2012). In this respect, Tint represents an exception because not only it includes standard NLP modules, for example Named Entity Recognition and Lemmatization, but it also provides within a single framework additional components that are usually available as separate tools, such as the identiﬁcation of multi-word expressions, the estimation of text complexity and the detection of text reuse. Multi-word expression identiﬁcation is a well studied problem, but most of the tools are available or optimized only for English. One of them, jMWE,3 is written in Java and provides a parallel project4 that adds compatibility to CoreNLP (Kulkarni and Finlayson, 2011). The mwetoolkit5 is written in Python and uses a CRF classiﬁer (Ramisch et al., 2010). The word2phrase module of word2vec attempts to learn phrases in a document of any language (Mikolov et al., 2013), but it is more a statistical tool for phrase extraction than for multi-word detection. As for the assessment of text complexity, READ-IT (Dell’Orletta et al., 2011) is the only existing tool that gathers readability information for an Italian text. However, while the online demo can be used for free without registration, the tool is not available for ofﬂine use. As for text reuse detection, i.e. when an author quotes (or borrows) another earlier or contemporary author, in the last years it has become easier thanks to new algorithms and high availability of texts (Mullen, 2016; Clough et al., 2002; Mihalcea et al., 2006). However, also in this case, no tools are available for Italian.

Conclusion
In this paper, we presented the new release of Tint, a simple, fast and accurate NLP pipeline for Italian, based on Stanford CoreNLP. In the new version, we have ﬁxed some bugs and improved some of the existing modules. We have also added a set of components for ﬁne-grained linguistics analysis that were not available so far. In the future, we plan to improve the suite and extend it with additional modules, also based on the feedback from the users through the github project page. We are currently working on new modules, in particular Word Sense Disambiguation (WSD) based on linguistic resources such as MultiWordNet (Pianta et al., 2002) and Semantic Role Labelling, by porting to Italian resources such as FrameNet (Baker et al., 1998), now available only in English. The Tint pipeline will also be integrated in PIKES (Corcoglioniti et al., 2016), a tool that extracts knowledge from English texts using NLP and outputs it in a queryable form (such RDF triples), so to extend it to Italian.