Language resources for Italian: towards the development of a corpus of annotated Italian multiword expressions
Authors: Shiva Taslimipoor, Anna Desantis, Manuela Cherchi, Ruslan Mitkov, Johanna Monti (University of Wolverhampton, Università di Sassari, Università di Napoli L'Orientale, 2016)
Topic: Lexical and Semantic Resources and Analysis

Abstract
This paper describes the first resource annotated for multiword expressions (MWEs) in Italian. Two versions of this dataset have been prepared: the first with a fast markup %consisting of a list of out-of-context MWEs, and the second with an in-context annotation, where the MWEs are entered with their contexts. The paper also discusses annotation issues and reports the inter-annotator agreement for both types of annotations. Finally, the results of the first exploitation of the new resource, namely the automatic extraction of Italian MWEs, are presented. \\ \\ Questo contributo descrive la prima risorsa italiana annotatata con polirematiche. Sono state preparate due versioni del dataset: la prima con una lista di polirematiche senza contesto, e la seconda con annotazione in contesto. Il contributo discute le problematiche emerse durante l'annotazione e riporta il grado di accordo tra annotatori per entrambi i tipi di annotazione. Infine vengono presentati i risultati del primo impiego della nuova risorsa, ovvero l'estrazione automatica di polirematiche per l'italiano.

Introduction
Multiword expressions (MWEs) are a pervasive phenomenon in language with their computational treatment being crucial for users and NLP applications alike (Baldwin and Kim, 2010; Granger and Meunier, 2008; Monti et al., 2013; Monti and Todirascu, 2015; Seretan and Wehrli, 2013). However, despite being desiderata for linguistic analysis and language learning, as well as for training and evaluation of NLP tasks such as term extraction (and Machine Translation in multilingual scenarios), resources annotated with MWEs are a scarce commodity (Schneider et al., 2014b). The need for such types of resources is even greater for Italian, which does not benefit from the variety and volume of resources as does English. This paper outlines the development of a new language resource for Italian, namely a corpus annotated with Italian MWEs of a particular class: verb-noun expressions such as fare riferimento, dare luogo and prendere atto. Such collocations are reported to be the most frequent class of MWEs and of high practical importance both for automatic translation and language learning. To the best of our knowledge, this is the first resource of this kind in Italian. The development of this corpus is part of a multilingual project addressing the challenge of computational treatment of MWEs. It covers English, Spanish, Italian, and French and its goal is to develop a knowledge-poor methodology for automatically identifying MWEs and retrieving their translations (Taslimipoor et al., 2016) for any pair of languages. The developed methodology will be used for Machine Translation and multilingual dictionary compilation, and also in computer-aided tools to support the work of language learners and translators. Two versions of the above resource have been produced. The first version consists of lists of MWEs annotated out-of-context with a view to performing fast evaluation of the developed methodology (out-of-context mark-up). The second version consists of annotated MWEs along with their concordances (in-context annotation). The latter type of annotation is time-consuming, but provides the contexts for the MWEs annotated.

Conclusion
In this paper, we outline our work towards a gold-standard dataset which is tagged with Italian verb-noun MWEs along with their contexts. We show the reliability of this dataset by its considerable inter-annotator agreement compared to the moderate inter-annotator agreement on annotated verb-noun expressions presented without context. We also report the results of automatic extraction of MWEs using this dataset as a gold-standard. One of the advantages of this dataset is that it includes both 0-tagged and 1-tagged tokens of expressions and it can be used for classification and other statistical NLP approaches. In future work, we are interested in extracting context features from concordances in this resource to automatically recognise and classify the expressions that are MWEs in some contexts but not MWEs in others.