CoreNLP-it: A UD Pipeline for Italian based on Stanford CoreNLP
Authors: Alessandro Bondielli, Lucia C. Passaro, Alessandro Lenci (Università di Firenze, Università di Pisa, 2018)
Topic: Lexical and Semantic Resources and Analysis

Abstract
This paper describes a collection of modules for Italian language processing based on CoreNLP and Universal Dependencies (UD). The software will be freely available for download under the GNU General Public License (GNU GPL). Given the ﬂexibility of the framework, it is easily adaptable to new languages provided with an UD Treebank.

Introduction
The fast-growing research ﬁeld of Text Mining and Natural Language Processing (NLP) has shown important advancements in recent years. NLP tools that provide basic linguistic annotation of raw texts are a crucial building block for further research and applications. Most of these tools, like NLTK (Bird et al., 2009) and Stanford CoreNLP (Manning et al., 2014), have been developed for able. For Italian, several tools have been developed during the years such as TextPro (Pianta et al., 2008) and the Tanl Pipeline (Attardi et al., 2010) but unfortunately they are either outdated or not open source. An exception is represented by Tint (Aprosio and Moretti, 2016), a standalone freely available and customizable software based on Stanford CoreNLP. The main drawback of this solution is that it is a resource highly tailored for Italian in which some of the modules have been completely re-implemented on new classes and data structures compared to the CoreNLP ones. In addition, like for the other existing resources, it does not provide an output that is fully compatible with the Universal Dependency (UD) framework,1 which is becoming the de facto standard especially for morpho-syntactic annotation, as well as for text annotation in general. In this paper, we present CoreNLP-it, a set of customizable classes for CoreNLP designed for Italian. Our system, despite being simpler than any of the above mentioned toolkits, both in scope and number of features, has the advantage of being easily integrated with the CoreNLP suite, since its development has been grounded on the principle that all data structures be natively supported by CoreNLP. The key properties of CoreNLP-it are: • UD based and compliant: The toolkit and models are based on UD and follow its guidelines for token and parsing representation. It can provide all annotation required in the UD framework, and produces a CoNLL-U formatted output at any level of annotation, as well as any other type of annotation provided in CoreNLP. • Multi-word token representation: Multiword tokens (e.g., enclitic constructions) are handled by providing separate tokens. Moreover, the CoNLL-U output can represent such information following the UD guidelines. • Hybrid tokenization: A fast and accurate hybrid tokenization and sentence splitting module replaces the original rule-based annotators for this task. • Integration with CoreNLP: Given the way it is built (including the exclusive usage of 1http://universaldependencies.org/ CoreNLP classiﬁers and data structures), the add-on can be seamlessly integrated with the latest available version (3.9.1) of CoreNLP, and is expected to work with upcoming versions as well. • Support for other languages: It provides out-of-the-box new capabilities of supporting basic annotations for other languages provided with a UD Treebank. This paper is organized as follows: in Section 2, we present the architecture of the toolkit, whereas its core components (annotators) are described in Section 3. The results on Italian are discussed in Section 3.5. Section 4 shows preliminary experiments for the adaptation of the software to two additional languages provided with a UD treebank, namely Spanish and French.

Conclusion
In this paper, we presented CoreNLP-it, a set of add-on modules for the Stanford CoreNLP language toolkit. Our system provides basic language annotations such as sentence splitting, tokenization, PoS tagging, lemmatization and dependency parsing, and can provide a UD-compliant output. Our rule based and statistical models achieve good performances for all tasks. In addition, since the framework has been implemented as an add-on to Stanford CoreNLP, it offers the possibility of adding other new annotators, including for example the Stanford NER (Finkel et al., 2005). Moreover, ﬁrst experiments on other languages have shown very good adaptation capability with very little effort. In the near future, we plan to reﬁne the core code by performing extensive tests to better deal with additional UD-supported languages and optimize their performances. We also plan to release the tool as well as the basic trained models for Italian. Moreover, we intend to perform data augmentation strategies to reﬁne our models and make them able to work properly also with ill-formed or substandard text input.