Access to jurisprudence is of paramount importance for both law professionals (judges, lawyers, law students) and for the larger public. In Romania, the Superior Council of Magistracy holds a large database of jurisprudence from different courts in the country, which is updated daily. However, granting public access requires its anonymization. This paper presents the efforts behind building a corpus for the anonymization process. We present the annotation scheme, the manual annotation methods, and the platform used.

This paper will attempt to determine experimentally if POS tagging of unseen words produces comparable performance, in terms of accuracy, as for words that were rarely seen in the training set (i.e. frequency less than 5), or more frequently seen (i.e. frequency greater than 10). To compare accuracies objectively, we will use the odds ratio statistic and its confidence interval testing to show that odds of being correct on unseen words are close to odds of being correct on rarely seen words. For the training of the POS taggers, we use different Romanian BERT models that are freely available on HuggingFace.

This article presents a work-in-progress project, which aims to build and utilize a corpus of Romanian texts written or spoken by non-native students of different nationalities, who learn Romanian as a foreign language in the one-year, intensive academic program organized by the University of Bucharest. This corpus, called LECOR – Learner Corpus for Romanian – is made up of pairs of texts: a version of the student and a corrected one of the teacher. Each version is automatically annotated with lemma and POS-tag, and the two versions are then compared, and the differences are marked as errors at this stage. The corpus also contains metadata file sets about students and their samples. In this article, the conceptual framework for building and utilization of the corpus is presented, including the acquisition and organization phases of the primary material, the annotation process, and the first attempts to adapt the NoSketch Engine query interface to the project’s objectives. The article concludes by outlining the next steps in the development of the corpus aimed at quantitative accumulation and the development of the error correction process and the complex error annotation.

In this paper we present the standardization of the Romanian Wordnet by means of conversion to the Linked Open Data format. We describe the vocabularies used to encode data and metadata of this resource. The decisions made are in accordance with the characteristics of the Romanian Wordnet, which are the outcome of the development method, enrichment strategies and resources used for its creations. By interlinking with other resources, words in the Romanian Wordnet have now the pronunciation associated, as well as syntagmatic information, in the form of contexts of occurrences.

In this paper, we report on (i) the conversion of Romanian language resources to the Linked Open Data specifications and requirements, on (ii) their publication and (iii) interlinking with other language resources (for Romanian or for other languages). The pool of converted resources is made up of the Romanian Wordnet, the morphosyntactic and phonemic lexicon RoLEX, four treebanks, one for the general language (the Romanian Reference Treebank) and others for specialised domains (SiMoNERo for medicine, LegalNERo for the legal domain, PARSEME-Ro for verbal multiword expressions), frequency information on lemmas and tokens and word embeddings as extracted from the reference corpus for contemporary Romanian (CoRoLa) and a bi-modal (text and speech) corpus. We also present the limitations coming from the representation of the resources in Linked Data format. The metadata of LOD resources have been published in the LOD Cloud. The resources are available for download on our website and a SPARQL endpoint is also available for querying them.

Following the successful creation of a national representative corpus of contemporary Romanian language, we turned our attention to the social media text, as present in micro-blogging platforms. In this paper, we present the current activities as well as the challenges faced when trying to apply existing tools (for both annotation and indexing) to a Romanian language micro-blogging corpus. These challenges are encountered at all annotation levels, including tokenization, and at the indexing stage. We consider that existing tools for Romanian language processing must be adapted to recognize features such as emoticons, emojis, hashtags, unusual abbreviations, elongated words (commonly used for emphasis in micro-blogging), multiple words joined together (within oroutside hashtags), and code-mixed text.

This article presents the current outcomes of the CURLICAT CEF Telecom project, which aims to collect and deeply annotate a set of large corpora from selected domains. The CURLICAT corpus includes 7 monolingual corpora (Bulgarian, Croatian, Hungarian, Polish, Romanian, Slovak and Slovenian) containing selected samples from respective national corpora. These corpora are automatically tokenized, lemmatized and morphologically analysed and the named entities annotated. The annotations are uniformly provided for each language specific corpus while the common metadata schema is harmonised across the languages. Additionally, the corpora are annotated for IATE terms in all languages. The file format is CoNLL-U Plus format, containing the ten columns specific to the CoNLL-U format and three extra columns specific to our corpora as defined by Varádi et al. (2020). The CURLICAT corpora represent a rich and valuable source not just for training NMT models, but also for further studies and developments in machine learning, cross-lingual terminological data extraction and classification.

The paper presents an open-domain Question Answering system for Romanian, answering COVID-19 related questions. The QA system pipeline involves automatic question processing, automatic query generation, web searching for the top 10 most relevant documents and answer extraction using a fine-tuned BERT model for Extractive QA, trained on a COVID-19 data set that we have manually created. The paper will present the QA system and its integration with the Romanian language technologies portal RELATE, the COVID-19 data set and different evaluations of the QA performance.

This paper introduces a manually annotated dataset for named entity recognition (NER) in micro-blogging text for Romanian language. It contains gold annotations for 9 entity classes and expressions: persons, locations, organizations, time expressions, legal references, disorders, chemicals, medical devices and anatomical parts. Furthermore, word embeddings models computed on a larger micro-blogging corpus are made available. Finally, several NER models are trained and their performance is evaluated against the newly introduced corpus.

This article presents the current outcomes of the MARCELL CEF Telecom project aiming to collect and deeply annotate a large comparable corpus of legal documents. The MARCELL corpus includes 7 monolingual sub-corpora (Bulgarian, Croatian, Hungarian, Polish, Romanian, Slovak and Slovenian) containing the total body of respective national legislative documents. These sub-corpora are automatically sentence split, tokenized, lemmatized and morphologically and syntactically annotated. The monolingual sub-corpora are complemented by a thematically related parallel corpus (Croatian-English). The metadata and the annotations are uniformly provided for each language specific sub-corpus. Besides the standard morphosyntactic analysis plus named entity and dependency annotation, the corpus is enriched with the IATE and EUROVOC labels. The file format is CoNLL-U Plus Format, containing the ten columns specific to the CoNLL-U format and four extra columns specific to our corpora. The MARCELL corpora represents a rich and valuable source for further studies and developments in machine learning, cross-lingual terminological data extraction and classification.

Within a larger frame of facilitating human-robot interaction, we present here the creation of a core vocabulary to be learned by a robot. It is extracted from two tokenised and lemmatized scenarios pertaining to two imagined microworlds in which the robot is supposed to play an assistive role. We also evaluate two resources for their utility for expanding this vocabulary so as to better cope with the robot’s communication needs. The language under study is Romanian and the resources used are the Romanian wordnet and word embedding vectors extracted from the large representative corpus of contemporary Romanian, CoRoLa. The evaluation is made for two situations: one in which the words are not semantically disambiguated before expanding the lexicon, and another one in which they are disambiguated with senses from the Romanian wordnet. The appropriateness of each resource is discussed.

The article describes the current status of a large national project, CoRoLa, aiming at building a reference corpus for the contemporary Romanian language. Unlike many other national corpora, CoRoLa contains only - IPR cleared texts and speech data, obtained from some of the country’s most representative publishing houses, broadcasting agencies, editorial offices, newspapers and popular bloggers. For the written component 500 million tokens are targeted and for the oral one 300 hours of recordings. The choice of texts is done according to their functional style, domain and subdomain, also with an eye to the international practice. A metadata file (following the CMDI model) is associated to each text file. Collected texts are cleaned and transformed in a format compatible with the tools for automatic processing (segmentation, tokenization, lemmatization, part-of-speech tagging). The paper also presents up-to-date statistics about the structure of the corpus almost two years before its official launching. The corpus will be freely available for searching. Users will be able to download the results of their searches and those original files when not against stipulations in the protocols we have with text providers.

This paper describes the Romanian treebank annotated according to the Universal Dependency principles. We present the types of texts included in the treebank, their processing phases and the tools used for doing it, as well as the levels of annotation, with a focus on the syntactic level. We briefly present the syntactic formalism used, the principles followed and the set of relations. The perspective we adopted is the linguist’s who searches the treebank for information with relevance for the study of Romanian. (S)He can interpret the statistics based on the corpus and can also query the treebank for finding examples to support a theory, for testing hypothesis or for discovering new tendencies. We use here the passive constructions in Romanian as a case study for showing how statistical data help understanding this linguistic phenomenon. We also discuss the kinds of linguistic information retrievable and non-retrievable form the treebank, based on the annotation principles.

We present the project of creating CoRoLa, a reference corpus of contemporary Romanian (from 1945 onwards). In the international context, the project finds its place among the initiatives of gathering huge collections of texts, of pre-processing and annotating them at several levels, and also of documenting them with metadata (CMDI). Our project is a joined effort of two institutes of the Romanian Academy. We foresee a corpus of more than 500 million word forms, covering all functional styles of the language. Although the vast majority of texts will be in written form, we target about 300 hours of oral texts, too, obligatorily with associated transcripts. Most of the texts will be from books, while the rest will be harvested from newspapers, booklets, technical reports, etc. The pre-processing includes cleaning the data and harmonising the diacritics, sentence splitting and tokenization. Annotation will be done at a morphological level in a first stage, followed by lemmatization, with the possibility of adding syntactic, semantic and discourse annotation in a later stage. A core of CoRoLa is described in the article. The target users of our corpus will be researchers in linguistics and language processing, teachers of Romanian, students.

Romanian and Bulgarian are Balkan languages with rich derivational morphology that, if introduced into their respective wordnets, can aid broadening of the wordnet content and the possible NLP applications. In this paper we present a joint work on introducing derivation into the Bulgarian and the Romanian WordNets, BulNet and RoWordNet, respectively, by identifying and subsequently labelling the derivationally and semantically related noun-verb pairs. Our research aims at providing a framework for a comparative study on derivation in the two languages and offering training material for the automatic identification and assignment of derivational and morphosemantic relations needed in various applications.

This article describes the collecting, processing and validation of a large balanced corpus for Romanian. The annotation types and structure of the corpus are briefly reviewed. It was constructed at the Research Institute for Artificial Intelligence of the Romanian Academy in the context of an international project (METANET4U). The processing covers tokenization, POS-tagging, lemmatization and chunking. The corpus is in XML format generated by our in-house annotation tools; the corpus encoding schema is XCES compliant and the metadata specification is conformant to the METANET recommendations. To the best of our knowledge, this is the first large and richly annotated corpus for Romanian. ROMBAC is intended to be the foundation of a linguistic environment containing a reference corpus for contemporary Romanian and a comprehensive collection of interoperable processing tools.

It is known that POS tagging is not very accurate for unknown words (words which the POS tagger has not seen in the training corpora). Thus, a first step to improve the tagging accuracy would be to extend the coverage of the tagger’s learned lexicon. It turns out that, through the use of a simple procedure, one can extend this lexicon without using additional, hard to obtain, hand-validated training corpora. The basic idea consists of merely adding new words along with their (correct) POS tags to the lexicon and trying to estimate the lexical distribution of these words according to similar ambiguity classes already present in the lexicon. We present a method of automatically acquire high quality POS tagging lexicons based on morphologic analysis and generation. Currently, this procedure works on Romanian for which we have a required paradigmatic generation procedure but the architecture remains general in the sense that given the appropriate substitutes for the morphological generator and POS tagger, one should obtain similar results.

The paper briefly describes the RoCo project and, in details, one of its first outcomes, the RoCo-News corpus. RoCo-News is a middle-sized journalistic corpus of Romanian, abundant in proper names, numerals and named entities. The initially raw text was previously segmented with MtSeg segmenter, then POS annotated with TNT tagger. RoCo-News was further lemmatized and validated. Because of limited human resources, time constraints and the dimension of the corpus, hand validation of each individual token was out of question. The validation stage required a coherent methodology for automatically identifying as many POS annotation and lemmatization errors as possible. The hand validation process was focused on these automatically spotted possible errors. This methodology relied on three main techniques for automatic detection of potential errors: 1. when lemmatizing the corpus, we extracted all the triples that were not found in the word-form lexicon; 2. we checked the correctness of POS annotation for closed class lexical categories, technique described by (Dickinson & Meurers, 2003); 3. we exploited the hypothesis (Tufiº, 1999) according to which an accurately tagged text, re-tagged with the language model learnt from it (biased evaluation) should have more than 98% tokens identically tagged.