alemiaschi/clic-it_corpus huggingface dataset:
A Preliminary Comparison of State-of-the-art Dependency Parsers on the Italian Stanford Dependency Treebank
Authors: Alberto Lavelli (Fondazione Bruno Kessler, 2014)
Topic: Syntax and Dependency Treebanks

Abstract
This paper reports the efforts in-volved in applying several state-of-the-art dependency parsers on the Italian Stanford Dependency Treebank (ISDT). The aim of such efforts is twofold: first, to compare the performance and choose the parser to participate in the EVALITA 2014 task on dependency parsing; second, to investigate  how simple it is to apply freely available state-of-the-art dependency parsers to a new language/treebank.

Introduction
Recently, there has been an increasing interest in dependency parsing, witnessed by the organisation of a number of shared tasks, e.g. Buchholz and Marsi (2006), Nivre et al. (2007). Concerning Italian, there have been tasks on dependency parsing in all the editions of the EVALITA evaluation campaign (Bosco et al., 2008; Bosco et al., 2009; Bosco and Mazzei, 2011; Bosco et al., 2014). In the 2014 edition, the task on dependency parsing exploits the Italian Stanford Dependency Treebank (ISDT), a new treebank featuring an annotation based on Stanford Dependencies (de Marneffe and Manning, 2008). This paper reports the efforts involved in applying several state-of-the-art dependency parsers on ISDT. There are at least two motivations for such efforts. First, to compare the results and choose the parsers to participate in the EVALITA 2014 task on dependency parsing. Second, to investigate how simple it is to apply freely available state-of-the-art dependency parsers to a new language/ treebank following the instructions available together with the code and possibly having a few interactions with the developers. As in many other NLP fields, there are very few comparative articles when the performance of different parsers is compared. Most of the papers simply present the results of a newly proposed approach and compare them with the results reported in previous articles. In other cases, the papers are devoted to the application of the same tool to different languages/treebanks. It is important to stress that the comparison concerns tools used more or less out of the box and that the results cannot be used to compare specific characteristics like: parsing algorithms, learning systems, . . .

Conclusion
In the paper we have reported on work in progress on the comparison between several state-of-the-art dependency parsers on the Italian Stanford Dependency Treebank (ISDT). In the near future, we plan to widen the scope of the comparison including more parsers. Finally, we will perform an analysis of the results obtained by the different parsers considering not only their performance but also their behaviour in terms of speed, CPU load at training and parsing time, ease of use, licence agreement, . . .
On the lexical coverage of some resources on Italian cooking recipes
Authors: Alessandro Mazzei (Università di Torino, 2014)
Topic: Textual Genres & Literature Linguistics

Abstract
We describe an experiment de-signed to measure the lexical coverage of some resources over the Italian cooking recipes genre. First, we have built a small cooking recipe dataset second, we have done a qualitative morpho-syntactic analysis of the dataset and third we have done a quantitative analysis of the lexical coverage of the dataset.

Introduction
The study reported in this paper is part of an applicative project in the field of nutrition. We are designing a software service for Diet Management (Fig. 1) that by using a smartphone allows one to retrieve, analyze and store the nutrition information about the courses. In our hypothetical scenario the interaction between the man and the food is mediated by an intelligent recommendation system that on the basis of various factors encourages or discourages the user to eat that specific course. The main factors that the system needs to account for are: (1) the diet that you intend to follow, (2) the food that have been eaten in the last days and, (3) the nutritional values of the ingredients of the course and its specific recipe. Crucially, in order to extract the complete salient nutrition information from a recipe, we need to analyze the sentences of the recipe. To allow the execution of this information extraction task we intend to use a syntactic parser together with a semantic interpreter. However, we intend to use both a deep interpreter, which have showed to have good performances in restricted domains (Fundel et al., 2007; Lesmo et al., 2013), as well as a shallow interpreter, that are most widely used in practical applications (Manning et al., 2008). In order to optimize the information extraction task we need to evaluate the specific features of the applicative domain (Fisher, 2001; Jurafsky, 2014). In the next Sections we present a preliminary study towards the realization of our NLP system, i.e. a linguistic analysis of the Italian recipes domain.

Conclusion
In this paper we presented a preliminary study on cooking recipes in Italian. The qualitative analysis emphatizes the importance of the sentence splitter and of the PoS tagger for a correct morpho-syntactic analysis. From the quantitative lexical coverage analysis we can draw a number of speculations. First, there is a great linguistic variation among cookbooks. Second, general lexical resources outperform domain specific resources with respect to lexical coverage. Third, the lemmatization can improve the recall of the algorithm with respect to the lexical resource.
ALCIDE: An online platform for the Analysis of Language and Content In a Digital Environment
Authors: Giovanni Moretti, Sara Tonelli, Stefano Menini, Rachele Sprugnoli (Fondazione Bruno Kessler, Università di Trento, 2014)
Topic: Lexical and Semantic Resources and Analysis

Abstract
This work presents ALCIDE (Analysis of Language and Content In a Digital Environment), a new platform for Historical Content Analysis. Our aim is to improve Digital Humanities studies in- tegrating methodologies taken from hu- man language technology and an easily understandable data structure representa- tion. ALCIDE provides a wide collection of tools that go beyond simple metadata indexing, implementing functions of tex- tual analysis such as named entity recog- nition, key-concept extraction, lemma and string-based search and geo-tagging.

Introduction
In this paper we present ALCIDE (Analysis of Language and Content In a Digital Environment), a new platform for Historical Content Analysis. Our aim is to improve Digital Humanities stud- ies implementing both methodologies taken from both methodologies taken from human language technology and an easily under- standable data structure representation. ALCIDE provides a wide collection of tools that go be- yond text indexing, implementing functions of textual analysis such as: named entities recogni- tion (e.g. identification of names of persons and locations within texts, key-concept extraction, tex- tual search and geotagging). Every function and information provided by ALCIDE is time bounded and all query functions are related to this feature; the leitmotif of the portal can be summarized as: “All I want to know related to a time period”. Our work aims at providing a flexible tool com- bining automatic semantic analysis and manual annotation tailored to the temporal dimension of documents. The ALCIDE platform currently sup- ports corpus analysis of English and Italian docu- ments.

Conclusion
In this paper we described the general workflow and specific characteristics of the ALCIDE platform. In the future, we aim to improve the efficiency of current functionalities and to add new ones such as (i) identification of temporal expressions and events (and the extraction of relations between them), (ii) distributional semantic analysis (i.e. quantification and categorization of semantic similarities between linguistic elements) and (iii) sentiment analysis on statements and key-concepts. ALCIDE is already online but it is password protected. When the implementation stage will be more advanced, we will make it freely accessible and users will be allowed to upload their corpora in Excel, XML or TEI format and explore them with the platform. For the moment a video of ALCIDE demo is available at http://dh.fbk.eu/projects/alcideanalysis- language-and-content-digital-enviroment.
The Importance of Being sum. Network Analysis of a Latin Dependency Treebank
Authors: Marco Carlo Passarotti (Università Cattolica del Sacro Cuore, 2014)
Topic: Lexical and Semantic Resources and Analysis

Abstract
Network theory provides a suitable framework to model the structure of language as a complex system. Based on a network built from a Latin dependency treebank, this paper applies methods for network analysis to show the key role of the verb sum (to be) in the overall structure of the network.

Introduction
Considering language as a complex system with deep relations between its components is a widespread approach in contemporary linguistics (Briscoe, 1998; Lamb, 1998; Steels, 2000; Hudson, 2007). Such a view implies that language features complex network structures at all its levels of analysis (phonetic, morphological, lexical, syntactic, semantic). Network theory provides a suitable framework to model the structure of linguistic systems from such a perspective. Network theory is the study of elements, called vertices or nodes, and their connections, called edges or links. A complex network is a (un)directed graph G(V, E) which is given by a set of vertices V and a set of edges E (Ferrer i Cancho, 2010). Vertices and edges can represent different things in networks. In a language network, the vertices can be different linguistic units (for instance, words), while the edges can represent different kinds of relations holding between these units (for instance, syntactic relations). So far, all the network-based studies in linguistics have concerned modern and living languages (Mehler, 2008a). However, times are mature enough for extending such approach also to the study of ancient languages. Indeed, the last years have seen a large growth of language resources for ancient languages. Among these resources are syntactically annotated corpora (treebanks), which provide essential information for building syntactic language networks.

Conclusion
While the most widespread tools for querying and analyzing treebanks give results in terms of lists of words or sequences of trees, network analysis permits a synoptic view of all the relations that hold between the words in a treebank. This makes network analysis a powerful method to fully exploit the structural information provided by a treebank, for a better understanding of the properties of language as a complex system with interconnnected elements.
Are Quantum Classifiers Promising?
Authors: Fabio Tamburini (Università di Bologna, 2014)
Topic: Multimodal

Abstract
This paper presents work in progress on the development of a new gen-eral purpose classifier based on Quantum Probability Theory. We will propose a kernel-based formulation of this classifier that is able to compete with a state-of-the-art machine learning methods when clas-sifying instances from two hard artificial problems and two real tasks taken from the speech processing domain.

Introduction
Quantum Mechanics Theory (QMT) is one of the most successful theory in modern science. Despite its ability to properly describe most natural phenomena in the physics realm, the attempts to prove its effectiveness in other domains remain quite limited. Only in recent years some scholars tried to embody principles derived from QMT into their specific fields. This connection has been actively studied, for example, by the Information Retrieval community (Zuccon et al., 2009; Melucci, van Rijsbergen, 2011; Gonz`alez, Caicedo, 2011) and in the domain of cognitive sciences and decision making (Busemeyer, Bruza, 2012). Also the NLP community started to look at QMT with interest and some studies using it have already been presented (Blacoe et al., 2013; Liu et al., 2013). This paper presents work in progress on the development of a new classifier based on Quantum Probability Theory. Starting from the work presented in (Liu et al., 2013) we will show all the limits of this simple quantum classifier and propose a new kernel-based formulation able to solve most of its problems and able to compete with a state-of-the-art classifier, namely Support Vector Machines, when classifying instances from two hard artificial problems and two real tasks taken from speech processing domain.

Conclusion
This paper presented a first attempt to produce a general purpose classifier based on Quantum Probability Theory. Considering the early experiments from (Liu et al., 2013), KQC is more powerful and gains better performance. The results obtained on our experiments are quite encouraging and we are tempted to answer ‘yes’ to the question presented in the paper title. This is a work in progress and the KQC is not free from problems. Despite its potential to outperform SVM using linear kernels, it is very complex to determine a tradeoff between the definition of decision boundaries with maximum margins and to maximise the classifier generalisation abilities. A long optimisation process on the training set maximise the margins between classes but could potentially lead to poor generalisations on new data. Making more experiments and evaluations in that directions is one of our future plans.
Il Corpus ICoN: una raccolta di elaborati di italiano L2 prodotti in ambito universitario
Authors: Mirko Tavosanis (Università di Pisa, 2014)
Topic: Learner Corpora and Language Acquisition

Abstract
The contribution presents the essential characteristics of the Corpus ICoN. The corpus collects processes carried out over 13 years by university students; the processes are divided into two subcorps.  The equivalents are dedicated respectively to students who know Italian as L1 and those who know it as L2/LS.

Introduction
Text bodies made like L2 have long been an essential tool for the study of language learning. In the case of the Italian, however, although there are important and well-made products, the body number is still considered insufficient for many types of research (for a overview: Andorno and Rastelli 2009). The corpus in equipment described below aims to contribute in this sense. The work is placed within the activities of the PRIN Escripture brevi√ì project and the final product is expected to be used first by the Institute of Computing Linguistics of the CNR of Pisa for the development of automatic evaluation tools of the learning process. The work is currently ongoing. The completion of the activities is scheduled for the end of 2015, but the overall characteristics of the corpus are already well defined and thus make an articulated presentation possible.

Conclusion
The processing of the corpus is still ongoing. However, the trials carried out so far are very promising and make sure the project is useful. A particular value seems the possibility of comparing the texts produced by students who have or not the Italian as L1 in circumstances where communication purposes respond to a precise teaching reality.
On Mining Citations to Primary and Secondary Sources in Historiography
Authors: Giovanni Colavizza, Fréderic Kaplan (EPFL, 2015)
Topic: Lexical and Semantic Resources and Analysis

Abstract
We present preliminary results from the Linked Books project, which aims at analysing citations from the historiography on Venice. A preliminary goal is to extract and parse citations from any location in the text, especially footnotes, both to primary and secondary sources. We detail a pipeline for these tasks based on a set of classifiers, and test it on the Archivio Veneto, a journal in the domain.

Introduction
The Linked Books project is part of the Venice Time Machine.}, a joint effort to digitise and study the history of Venice by digital means. The project goal is to analyse the history of Venice through the lens of citations, by network analytic methods. Such research is interesting because it could unlock the potential of the rich semantics of the use of citations in humanities. A preliminary step is the extraction and normalization of citations, which is a challenge in itself. In this paper we present the first results on this last topic, over a corpus of journals and monographs on the history of Venice, digitised in partnership with the Ca' Foscari Humanities Library and the Marciana Library. Our contribution is three-fold. First, we address the problem of extracting citations in historiography, something rarely attempted before. Secondly, we extract citation from footnotes, with plain text as input. Lastly, we deal at the same time with two different kind of citations: to primary and to secondary sources. A primary source is a documentary evidence used to support a claim, a secondary source is a scholarly publication AUTHOR. In order to solve this problem, we propose a pipeline of classifiers dealing with citation detection, extraction and parsing. The paper is organised as follows: a state of the art in Section 2 is followed by a methodological section explaining the pipeline and applied computational tools. A section on experiments follows, conclusions and future steps close the paper.

Conclusion
We presented a pipeline for recognizing and parsing citations to primary and secondary sources from historiography on Venice, with a case study on the Archivio Veneto journal. A first filtering step allows us to detect text blocks likely to contain citations, usually footnotes, by a SVM classifier trained on a simple set of morphological features. We then detect citation boundaries and macro-categories (to primary and secondary sources) using more rich features and CRFs. The last step in our pipeline is the fine-grained parsing of each extracted citation, in order to prepare them for further processing and analysis. In the future we plan to design more advanced feature sets, first of all considering text format features. Secondly, we will implement the next package of our chain: an error-tolerant normalizer which will uniform all citations to the same primary or secondary source within a publication, as a means to minimise the impact of classification errors during previous steps.
Bolzano/Bozen Corpus: Coding Informationabout the Speaker in IMDI Metadata Structure
Authors: Marco Angster (Libera Università di Bolzano, 2015)
Topic: Lexical and Semantic Resources and Analysis

Abstract
The paper introduces a new collection of spoken data (the Bolzano/Bozen Corpus) available through The Language Archive of Max Planck Institute of Nijmegen. It shows an example of the issues encountered in accommodating information of an existent corpus into IMDI metadata structure. Finally, it provides preliminary reflections on CMDI: a component-based metadata format.

Introduction
Once a Language Resource (LR) exists it should be used, and this entails several problems. First of all it must be available to the public – which may be the academic community, but also industry or institutions – and, given that producing a LR is an expensive task, it would be ideal that a LR could be exploited beyond the originally intended public. The re-usability of a LR is possible provided that it is conceived following shared standards for formats, tagging and metadata. In this paper I focus on metadata structures, in particular I introduce a collection of spoken data (the Bolzano/Bozen Corpus) and I show the problems encountered in fitting the information available about the speakers sampled in the data in IMDI metadata structure. The paper aims at providing an example of how flexible are the considered metadata structures in accommodating information of existent collections of data and in adapting to the needs of the researcher in sociolinguistics.

Conclusion
In this paper, I have shown an example of the difficulty of using a metadata structure to accommodate information on speaker’s linguistic background. I have taken into account the case of Bolzano Bozen Corpus and two sociolinguistically oriented projects (KOMMA, Kontatto) hosted on The Language Archive. IMDI, the former standard of TLA, is now an outdated tool and is too rigid to adapt to specific purposes. The new standard CMDI provides huge possibilities to the research community to define metadata formats tailored on specific needs. However CMDI does not provide until now satisfactory profiles and components for sociolinguistic studies, especially as far as background information about the speaker is concerned. Furthermore, direct contribution to CMDI components is restricted to CLARIN centres and in some crucial cases even categories available in CMDI are unsatisfactory and must be proposed to the relevant (and closed) DCR. The case I have proposed shows on the one hand the possibilities of CMDI. However, on the other hand, the difficulty to contribute to CMDI profiles and components from outside CLARIN may lead to the uncomfortable condition of having huge amounts of data with unsatisfactory metadata, which have low possibilities to be re-used, failing one of the main objectives of a standardisation initiative.
SentIta and Doxa: Italian Databases and Tools for Sentiment Analysis Purposes
Authors: Serena Pelosi (Università di Salerno, 2015)
Topic: Sentiment, Emotion, Irony, Hate

Abstract
This reserach presents SentIta, a Sentiment lexicon for the Italian language, and Doxa, a prototype that, interacting with the lexical database, applies a set of linguistic rules for the Document-level Opinionated teXt Analysis. Details about the dictionary population, the semantic analysis of texts written in natural language and the evaluation of the tools will be provided in the paper.

Introduction
Through online customer review systems, Internet forums, discussion groups and blogs, consumers are allowed to share positive or negative information than can influence in different ways the purchase decisions and model the buyer expectations, above all with regard to experience goods (Nakayama et al., 2010); such as hotels (Ye et al., 2011), restaurants (Zhang et al., 2010), movies (Duan et al., 2008), books (Chevalier and Mayzlin, 2006) or videogames (Zhu and Zhang, 2006). The consumers, as Internet users, can freely share their thoughts with huge and geographically dispersed groups of people, competing, this way, with the traditional power of marketing and advertising channels. Differently from the traditional word-of-mouth, which is usually limited to private conversations, the user generated contents on Internet can be directly observed and described by the researchers. The present paper will provide in Section 2 a concise overview on the most popular techniques for both sentiment analysis and polarity lexicon propagation. Afterward, it will describe in Section 3 the method used to semi-automatically populate SentIta, our Italian sentiment lexicon and in Section 4 the rules exploited to put the words’ polarity in context . In the end, Sections 5 will describe our opinion analyzer Doxa, that performs documentlevel sentiment analysis, sentiment role labeling and feature-based opinion summarization.

Conclusion
In the present paper we underlined that the social and economic impact of the online customer opinions and the huge volume of raw data available on the web, concerning users point of views, offer new opportunities both to marketers and researchers. Indeed, sentiment analysis applications, able to go deep in the semantics of sentences and texts, can play a crucial role in tasks like web reputation monitoring, in social network analysis, in viral tracking campaigns, etc... Therefore, we presented SentIta, a semiautomatic Italian Lexicon for Sentiment Analysis, and Doxa, a Document-level Opinionated teXt Analyzer that exploits finite-state technologies to go through the subjective dimension of user generated contents.