alemiaschi/clic-it_corpus huggingface dataset:
Initial Explorations in Kazakh to English Statistical Machine Translation
Authors: Zhenisbek Assylbekov, Assulan Nurkas (Nazarbayev University, 2014)
Topic: Machine Translation

Abstract
This paper presents preliminary results of developing a statistical machine translation system from Kazakh to English. Starting with a baseline model trained on 1.3K and then on 20K aligned sentences, we tried to cope with the complex morphology of Kazakh by applying different schemes of morphological word segmentation to the training and test data. Morphological segmentation appears to benefit our system: our best segmentation scheme achieved a 28% reduction of out-of-vocabulary rate and 2.7 point BLEU improvement above the baseline.

Introduction
The availability of considerable amounts of parallel texts in Kazakh and English has motivated us to apply statistical machine translation (SMT) paradigm for building a Kazakh-to-English machine translation system using publicly available data and open-source tools. The main ideas of SMT were introduced by researchers at IBM’s Thomas J. Watson Research Center (Brown et al., 1993). This paradigm implies that translations are generated on the basis of statistical models whose parameters are derived from the analysis of bilingual text corpora. We show how one can compile a Kazakh-English parallel corpus from publicly available resources in Section 2. It is well known that challenges arise in statistical machine translation when we deal with languages with complex morphology, e.g. Kazakh. However recently there were attempts to tackle such challenges for similar languages by morphological pre-processing of the source text (Bisazza and Federico, 2009; Habash and Sadat, 2006; Mermer, 2010). We apply morphological preprocessing techniques to Kazakh side of our corpus and show how they improve translation performance in Sections 5 and 6.

Conclusion
The experiments have shown that a selective morphological segmentation improves the performance of an SMT system. One can see that in contrast to Bisazza and Federico’s results (2009), in our case MS11 downgrades the translation performance. One of the reasons for this might be that Bisazza and Federico considered translation of spokenl anguage in which sentences weres horter on average than in our corpora. In this work we mainly focused on nominal suffixation. In our future work we are planning to: increase the dictionary of morphological transducer – currently it covers 93.3% of our larger corpus; improve morphological disambiguation using e.g. perceptron algorithm (Saket al., 2007); develop more segmentation rules for verbs and other parts of speech; mine more mono- and bilingual data using officia lwebsites of Kazakhstan’s public authorities.
Adapting Linguistic Tools for the Analysis of Italian Medical Records
Authors: Giuseppe Attardi, Vittoria Cozza, Daniele Sartiano (Università di Pisa, 2014)
Topic: In-domain IR and IE

Abstract
We address the problem of recognition of medical entities in clinical records written in Italian. We report on experiments performed on medical data in English provided in the shared tasks at CLEF-ER 2013 and SemEval 2014. This allowed us to refine Named Entity recognition techniques to deal with the specifics of medical and clinical language in particular. We present two approaches for transferring the techniques to Italian. One solution relies on the creation of an Italian corpus of annotated clinical records and the other on adapting existing linguistic tools to the medical domain.

Introduction
One of the objectives of the RIS project (RIS 2014) is to develop tools and techniques to help identifying patients at risk of evolving their disease into a chronic condition. The study relies on a sample of patient data consisting of both medical test reports and clinical records. We are interested in verifying whether text analytics, i.e. information extracted from natural language texts, can supplement or improve information extracted from the more structured data available in the medical test records. Clinical records are expressed as plain text in natural language and contain mentions of diseases or symptoms affecting a patient, whose accurate identification is crucial for any further text mining process. Our task in the project is to provide a set of NLP tools for extracting automatically information from medical reports in Italian. We are facing the double challenge of adapting NLP tools to the medical domain and of handling documents in a language (Italian) for which there are few available linguistic resources. Our approach to information extraction exploits both supervised machine-learning tools, which require annotated training corpora, and unsupervised deep learning techniques, in order to leverage unlabeled data. For dealing with the lack of annotated Italian resources for the bio-medical domain, we attempted to create a silver corpus with a semi-automatic approach that uses both machine translation and dictionary based techniques. The corpus will be validated through crowdsourcing.

Conclusion
We presented a series of experiments on biomedical texts from both medical literature and clinical records, in multiple languages, that helped us to refine the techniques of NE recognition and to adapt them to Italian. We explored supervised techniques as well as unsupervised ones, in the form of word embeddings or word clusters. We also developed a Deep Learning NE tagger that exploits embeddings. The best results were achieved by using a MEMM sequence labeler using clusters as features improved in an ensemble combination with other NE taggers. As an further contribution of our work, we produced, by exploiting semi-automated techniques, an Italian corpus of medical records, annotated with mentions of medical terms.
L'integrazione di informazioni contestuali e linguistiche nel riconoscimento automatico dell'ironia
Authors: Lorenzo Bernardini, Irina Prodanof (Università di Pavia, 2014)
Topic: Sentiment, Emotion, Irony, Hate

Abstract
Verbal is a highly complex rhetorical figure that belongs to the pragmatic level of the language. So far, however, all computing attempts aimed at the automatic recognition of irony have limited themselves to searching for linguistic indications that could indicate their presence without considering pragmatic and contextual factors. In this work, we tried to evaluate the possibility of integrating simple computing contextual factors with language-type information in order to improve the effectiveness of automatic irony recognition systems in the comments of online newspapers.

Introduction
Verbal irony is a very complex rhetoric figure that is placed at the pragmatic level of the language. As far as an ironist can use phonological, prosodial, morphological, lessical, synthetic and semantic elements to produce irony, this latter is not an internal property of the enunciated itself and is not determined by its formal characteristics. The irony is rather an interpretative phenomenon linked to the expectations that a listener develops regarding the author‚Äôs intentions of an enunciated product in a specific context starting from an extinct set of encyclopedic and contextual information.

Conclusion
This work has been presented with the possibility of using contextual information to automatically identify irony in the comments of the usual readers of online newspapers. For this purpose, a possible computing approach was proposed to identify the more ironic commentators of a community, suggesting a different treatment of the linguistic material between them and the other commentators. The integration of contextual information and language information could have a positive impact on the effectiveness of automatic irony recognition systems, which would have an important role in the field of Sentiment Analysis. Currently we are expanding research by evaluating the influence of information such as the type of newspaper, the topic of the news and the length of the comment on a broader and built on multiple newspapers comment corpus. Obviously, an integration of contextual information as basic would not completely solve the problem of how to automatically identify irony in online texts. However, this work reflects the firm belief that these progressive attempts to integrate simple and computable contextual information with linguistic information are today the best way to go to attempt to automatically address phenomena of pragmatic nature as ‚Äúcomplex and disfaced‚Äù as irony.
CorEA: Italian News Corpus with Emotions and Agreement
Authors: Fabio Celli, Giuseppe Riccardi (Università di Trento, 2014)
Topic: Sentiment, Emotion, Irony, Hate

Abstract
In this paper, we describe an Italian corpus of news blogs, including bloggers’ emotion tags, and annotations of agreement relations amongst blogger-comment pairs. The main contributions of this work are: the formalization of the agreement relation, the design of guide-lines for its annotation, the quantitative analysis of the annotators’ agreement.

Introduction
Online news media, such as journals and blogs, allow people to comment news articles, to express their own opinions and to debate about a wide va-riety of different topics, from politics to gossips. In this scenario, commenters express approval and dislike about topics, other users and articles, ei-ther in a linguistic form and/or using like pre-coded actions (e.g. like buttons). Corriere is one of the most visited Italian news websites, attract-ing over 1.6 million readers everyday1. The pe-culiarity of corriere.it with respect to most news websites, is that it contains metadata on emotions expressed by the readers about the articles. The emotions (amused, satisfied, sad, preoccupied and 1source ’http://en.wikipedia.org/wiki/Corriere della Sera’ retrieved in Jan 2014.indignated) are annotated directly by the readers on a voluntary basis. They can express one emo-tion per article. In this paper, we describe the col-lection of a corpus from corriere.it, that combines emotions and agreement/disagreement. The paper is structured as follows: in section 2 we will provide an overview of related work, in sections 3 and 4 we will define the agree-ment/disagreement relation, describe the corpus, comparing it to related work, and provide the an-notation guidelines. In section 5 we will draw some conclusions.

Conclusion
We presented the CorEA corpus, a resource that combines agreement/disagreement at message level and emotions at participant level. We are not aware of any other resource of this type for Ital-ian. We found that the best way to annotate agree-ment/disagreement is with binary classes, filtering out “NA” and neutral cases. In the future, we would like to annotate CorEA at topic level and develop classifiers for agree-ment/disagreement. We plan to make available the corpus at the end of the project.
Polysemy alternations extraction using the PAROLE SIMPLE CLIPS Italian lexicon
Authors: Francesca Frontini, Valeria Quochi, Monica Monachini (CNR-ILC, 2014)
Topic: Lexical and Semantic Resources and Analysis

Abstract
This paper presents the results of an experiment of polysemy alternations induction from a lexicon (Utt and Padó, 2011 Frontini et al., 2014), discussing the results and proposing an amendment in the original algorithm.

Introduction
The various different senses of polysemic words do not always stand to each other in the same way. Some senses group together along certain dimensions of meaning while others stand clearly apart. Machine readable dictionaries have in the past used coarse grained sense distinctions but often without any explicit indication as to whether these senses were related or not. Most significantly, few machine readable dictionaries explicitly encode systematic alternations. In Utt and Pad´o (2011) a methodology is described for deriving systematic alternations of senses from WordNet. In Frontini et al. (2014) the work was carried out for Italian using the PAROLE SIMPLE CLIPS lexicon (PSC) (Lenci et al., 2000), a lexical resource that contains a rich set of explicit lexical and semantic relations. The purpose of the latter work was to test the methodology of the former work against the inventory of regular polysemy relations already encoded in the PSC semantic layer. It is important to notice that this was not possible in the original experiment, as WordNet does not contain such information. The result of the work done on PSC shows how the original methodology can be useful in testing the consistency of encoded polysemies and in finding gaps in individual lexical entries. At the same time the methodology is not infallible especially in distinguishing type alternations that frequently occur in the lexicon due to systematic polysemy from other alternations that are produced by metaphoric extensions, derivation or other non systematic sense shifting phenomena. In this paper we shall briefly outline the problem of lexical ambiguity; then describe the procedure of type induction carried out in the previous experiments, discussing the most problematic results; finally we will propose a change in the original methodology that seems more promising in capturing the essence of systematic polysemy.

Conclusion
To conclude, such preliminary results actually seem to confirm the hypothesis that measuring the association strength between types, rather than the frequency of their cooccurrence, is useful to capture the systematicity of an alternation. In future work it may be interesting to test ranking by other association measures (such as Log Likelihood) and with different filternigs. Finally, the original experiment may be repeated on both Italian and English WordNets in order to evaluate the new method on the original lexical resource.
Rappresentazione dei concetti azionali attraverso prototipi e accordo nella categorizzazione dei verbi generali. Una validazione statistica
Authors: Gloria Gagliardi (Università di Firenze, 2014)
Topic: Lexical and Semantic Resources and Analysis

Abstract
The article presents the results of a study aimed at assessing the consistency of the ca-tegorization of the business space operated by mother-language noteers for a set of semantically cohesive verbs of the IMA-GACT database (Ogirare‚Äôs semantic area). The statistical value-daction, articulated into three tests, is based on the calculation of the inter-tagger agreement in tasks of disambiguation of re-sented concepts using prototypes for images.

Introduction
IMAGACT is an interlinguistic enthology that makes the spectrum of pragmatic variation-as associated with action predicates at average and high frequency in Italian and English (Moneglia et al., 2014). The classes of action that identify the reference entities of the language concepts, referred to in this lessical resource in the form of prototype scenes (Rosch, 1978), were in-docked by the body of spoken by madrelin-gua linguists, through a bottom-up procedure: the mate-rials linguistic were subjected to an arctic-lat procedure of notification described extensively in previous works (Moneglia et al., 2012; Frontini et al., 2012). The article illustrates the results of three tests aimed at assessing the consistency of the categorization of the action space proposed by the noteers for a short but semanticly consistent set of resource verbes: such choice was determined by the will to study at a high level of detail the problems related to the typization of the variation of the preached on events. The predisposition of this case-study is also favorable to the creation of a standard procedure, extended in a second time to statistically significant portions of the oncology for its complete validation. Paragraph 2 shall present the statist coefficients adopted, and Paragraph 3 shall describe the methodology and results of the tests carried out.

Conclusion
It is notorious that semantic note tasks, and in particular those dedicated to verbal lessic (Fellbaum, 1998; Fellbaum et al., 2001), record low levels of I.TA.5 In this case the possibility of obtaining high values, even with non-expert noteers, is likely due to the exclusively action and physical nature of the classes used for the categorization. Following the validation it was possible to use the data in applications of a psycholinguistic type (Gagliardi, 2014): the sample of verbs of oncology, broad and at the same time formally controlled, if fully validated could represent an unprecedented source of semantic data for cognitive sciences. For this purpose, as well as for a full educational and computing exploitation of the resource,6 in the near future the illustrated methodology will be extended to a quantitatively and statistically significant portion of the database.
Correcting OCR errors for German in Fraktur font
Authors: Michel Généreux, Egon W. Stemle, Lionel Nicolas, Verena Lyding (EURAC, 2014)
Topic: Lexical and Semantic Resources and Analysis

Abstract
In this paper, we present on-going experiments for correcting OCR er-rors on German newspapers in Fraktur font. Our approach borrows from techniques for spelling correction in context using a prob-abilistic edit-operation error model and lexical resources. We highlight conditions in which high error reduction rates can be obtained and where the approach currently stands with real data.

Introduction
The OPATCH project (Open Platform for Access to and Analysis of Textual Documents of Cul-tural Heritage) aims at creating an advanced online search infrastructure for research in an historical newspapers archive. The search experience is en-hanced by allowing for dedicated searches on per-son and place names as well as in defined subsec-tions of the newspapers. For implementing this, OPATCH builds on computational linguistic (CL) methods for structural parsing, word class tagging and named entity recognition (Poesio et al., 2011). The newspaper archive contains ten newspapers in German language from the South Tyrolean region for the time period around the First World War. Dating between 1910 and 1920, the newspapers are typed in the blackletter Fraktur font and paper quality is derogated due to age. Unfortunately, such material is challenging for optical character recognition (OCR), the process of transcribing printed text into computer readable text, which is the first necessary pre-processing step for any further CL processing. Hence, in OPATCH we are starting from majorly error-prone OCR-ed text, in quantities that cannot realistically be corrected manually. In this paper we present attempts to automate the procedure for correcting faulty OCR-ed text.

Conclusion
The approach we presented to correct OCR er-rors considered four features of two types: edit-distance and n-grams frequencies. Results show that a simple scoring system can correct OCR-ed texts with very high accuracy under idealized con-ditions: no more than two edit operations and a perfect dictionary. Obviously, these conditions do not always hold in practice, thus an observed er-ror reduction rate drops to 10%. Nevertheless, we can expect to improve our dictionary coverage so that very noisy OCR-ed texts (i.e. 48% error with distance of at least three to target) can be corrected with accuracies up to 20%. OCR-ed texts with less challenging error patterns can be corrected with accuracies up to 61% (distance 2) and 86% (dis-tance 1).
Part-of-Speech tagging strategy for MIDIA: a diachronic corpus of the Italian language
Authors: Claudio Iacobini, Aurelio De Rosa, Giovanna Schirato (Università di Salerno, 2014)
Topic: Lexical and Semantic Resources and Analysis

Abstract
The realization of MIDIA (a bal-anced diachronic corpus of written Italian texts ranging from the XIII to the first half of the XX c.) has raised the issue of developing a strategy for PoS tagging able to properly analyze texts from different textual genres belonging to a broad span of the history of the Italian language. The paper briefly de-scribes the MIDIA corpus it focuses on the improvements to the contemporary Italian parameter file of the PoS tagging program Tree Tagger, made to adapt the software to the analysis of a textual basis characterized by strong morpho-syntactic and lexical variation; and, finally, it outlines the reasons and the advantages of the strategies adopted.

Introduction
The realization of MIDIA, a balanced diachronic corpus of Italian, raised the issue of the elaboration of a strategy of analysis of texts from different genres and time periods in the history of Italian. This temporal and textual diversity involves both a marked graphic, morphological and lexical variation in word forms, and differences in the ordering of the PoS. The program chosen for the PoS tagging is Tree Tagger (cf. Schmid 1994, 1995), and the parameter file, made of a lexicon and a training corpus, is the one developed by Baroni et al (2004) for contemporary Italian. The strategy we developed for the adjustement of the PoS tagging to different diachronic varieties has been to greatly increase the lexicon with a large amount of word forms belonging predominantly to Old Italian, and not to retrain the program with texts belonging to previous temporal stages. This solution turned out to be economical and effective: it has allowed a significant improvement of the correct assignment of PoS for texts both old and modern, with a success rate equal to or greater than 95% for the tested texts, and an optimal use of human resources.

Conclusion
The strategy we devised to develop MIDIA PoS tagging for the analysis of texts belonging to different time periods and textual genres than that for which it was originally trained has proved to be successful and economical. Human resources have been concentrated on enriching the lexicon and on the review of automatic lexeme and PoS assignment. Our results show that a larger lexicon improves the analysis also for words adjacent to those recognized by the matching with the word forms listed in the lexicon. This has some interesting consequences both on the strategies for text tagging and on the implementation of the program Tree Tagger for the analysis of texts with a great range of variation. We plan to further enrich MIDIA lexicon by adding word forms from the corpus not yet listed in the lexicon.
Publishing PAROLE SIMPLE CLIPS as Linguistic Linked Open Data
Authors: Fahad Khan, Francesca Frontini (CNR-ILC, 2014)
Topic: Lexical and Semantic Resources and Analysis

Abstract
This paper presents the ongo-ing project for the conversion and publi-cation of the Italian lexicon Parole Simple Clips in linked open data, illustrating the chosen model, with a particular focus on the translation of the syntactic and seman-tic information pertaining verbs and their predicates.

Introduction
1 Introduction The aim of the present paper is to describe the ongoing conversion of the semantic layer of the Parole Simple Clips (PSC) lexical resource into linked open data. We have previously presented the conversion of the nouns in PSC in (Del Gratta et al., 2013). In this paper we will continue this work by presenting the model we intend to use for converting the verbs. In the next section we shall give a general back-ground on the linguistic linked open data (LLOD) cloud and discuss the importance of putting lex-ical resources on the cloud. We also discuss the lemon model which we have chosen as the basis of the conversion of the PSC resource. In the fol-lowing section we discuss PSC itself and give a brief overview of its structure. Finally in the last section we will outline how we intend to proceed with the conversion of the PSC verbs, illustrating the proposed schema with an example.

Conclusion
In this paper we have presented our model for rep-resenting the PSC verbs using the lemon model. As we have stated above this is currently work in progress. In the final paper the link to the public dataset will be provided.