Maria Mitrofan


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Improving Romanian BioNER Using a Biologically Inspired System
Maria Mitrofan | Vasile Pais
Proceedings of the 21st Workshop on Biomedical Language Processing

Recognition of named entities present in text is an important step towards information extraction and natural language understanding. This work presents a named entity recognition system for the Romanian biomedical domain. The system makes use of a new and extended version of SiMoNERo corpus, that is open sourced. Also, the best system is available for direct usage in the RELATE platform.


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Named Entity Recognition in the Romanian Legal Domain
Vasile Pais | Maria Mitrofan | Carol Luca Gasan | Vlad Coneschi | Alexandru Ianov
Proceedings of the Natural Legal Language Processing Workshop 2021

Recognition of named entities present in text is an important step towards information extraction and natural language understanding. This work presents a named entity recognition system for the Romanian legal domain. The system makes use of the gold annotated LegalNERo corpus. Furthermore, the system combines multiple distributional representations of words, including word embeddings trained on a large legal domain corpus. All the resources, including the corpus, model and word embeddings are open sourced. Finally, the best system is available for direct usage in the RELATE platform.

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Assessing multiple word embeddings for named entity recognition of professions and occupations in health-related social media
Vasile Pais | Maria Mitrofan
Proceedings of the Sixth Social Media Mining for Health (#SMM4H) Workshop and Shared Task

This paper presents our contribution to the ProfNER shared task. Our work focused on evaluating different pre-trained word embedding representations suitable for the task. We further explored combinations of embeddings in order to improve the overall results.


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Collection and Annotation of the Romanian Legal Corpus
Dan Tufiș | Maria Mitrofan | Vasile Păiș | Radu Ion | Andrei Coman
Proceedings of the 12th Language Resources and Evaluation Conference

We present the Romanian legislative corpus which is a valuable linguistic asset for the development of machine translation systems, especially for under-resourced languages. The knowledge that can be extracted from this resource is necessary for a deeper understanding of how law terminology is used and how it can be made more consistent. At this moment the corpus contains more than 140k documents representing the legislative body of Romania. This corpus is processed and annotated at different levels: linguistically (tokenized, lemmatized and pos-tagged), dependency parsed, chunked, named entities identified and labeled with IATE terms and EUROVOC descriptors. Each annotated document has a CONLL-U Plus format consisting in 14 columns, in addition to the standard 10-column format, four other types of annotations were added. Moreover the repository will be periodically updated as new legislative texts are published. These will be automatically collected and transmitted to the processing and annotation pipeline. The access to the corpus will be done through ELRC infrastructure.

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The MARCELL Legislative Corpus
Tamás Váradi | Svetla Koeva | Martin Yamalov | Marko Tadić | Bálint Sass | Bartłomiej Nitoń | Maciej Ogrodniczuk | Piotr Pęzik | Verginica Barbu Mititelu | Radu Ion | Elena Irimia | Maria Mitrofan | Vasile Păiș | Dan Tufiș | Radovan Garabík | Simon Krek | Andraz Repar | Matjaž Rihtar | Janez Brank
Proceedings of the 12th Language Resources and Evaluation Conference

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.


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Leaving No Stone Unturned When Identifying and Classifying Verbal Multiword Expressions in the Romanian Wordnet
Verginica Mititelu | Maria Mitrofan
Proceedings of the 10th Global Wordnet Conference

We present here the enhancement of the Romanian wordnet with a new type of information, very useful in language processing, namely types of verbal multi-word expressions. All verb literals made of two or more words are attached a label specific to the type of verbal multi-word expression they correspond to. These labels were created in the PARSEME Cost Action and were used in the version 1.1 of the shared task they organized. The results of this annotation are compared to those obtained in the annotation of a Romanian news corpus with the same labels. Given the alignment of the Romanian wordnet to the Princeton WordNet, this type of annotation can be further used for drawing comparisons between equivalent verbal literals in various languages, provided that such information is annotated in the wordnets of the respective languages and their wordnets are aligned to Princeton WordNet, and thus to the Romanian wordnet.

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Evaluating the Wordnet and CoRoLa-based Word Embedding Vectors for Romanian as Resources in the Task of Microworlds Lexicon Expansion
Elena Irimia | Maria Mitrofan | Verginica Mititelu
Proceedings of the 10th Global Wordnet Conference

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.

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MoNERo: a Biomedical Gold Standard Corpus for the Romanian Language
Maria Mitrofan | Verginica Barbu Mititelu | Grigorina Mitrofan
Proceedings of the 18th BioNLP Workshop and Shared Task

In an era when large amounts of data are generated daily in various fields, the biomedical field among others, linguistic resources can be exploited for various tasks of Natural Language Processing. Moreover, increasing number of biomedical documents are available in languages other than English. To be able to extract information from natural language free text resources, methods and tools are needed for a variety of languages. This paper presents the creation of the MoNERo corpus, a gold standard biomedical corpus for Romanian, annotated with both part of speech tags and named entities. MoNERo comprises 154,825 morphologically annotated tokens and 23,188 entity annotations belonging to four entity semantic groups corresponding to UMLS Semantic Groups.

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Hear about Verbal Multiword Expressions in the Bulgarian and the Romanian Wordnets Straight from the Horse’s Mouth
Verginica Barbu Mititelu | Ivelina Stoyanova | Svetlozara Leseva | Maria Mitrofan | Tsvetana Dimitrova | Maria Todorova
Proceedings of the Joint Workshop on Multiword Expressions and WordNet (MWE-WN 2019)

In this paper we focus on verbal multiword expressions (VMWEs) in Bulgarian and Romanian as reflected in the wordnets of the two languages. The annotation of VMWEs relies on the classification defined within the PARSEME Cost Action. After outlining the properties of various types of VMWEs, a cross-language comparison is drawn, aimed to highlight the similarities and the differences between Bulgarian and Romanian with respect to the lexicalization and distribution of VMWEs. The contribution of this work is in outlining essential features of the description and classification of VMWEs and the cross-language comparison at the lexical level, which is essential for the understanding of the need for uniform annotation guidelines and a viable procedure for validation of the annotation.

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RACAI’s System at PharmaCoNER 2019
Radu Ion | Vasile Florian Păiș | Maria Mitrofan
Proceedings of The 5th Workshop on BioNLP Open Shared Tasks

This paper describes the Named Entity Recognition system of the Institute for Artificial Intelligence “Mihai Drăgănescu” of the Romanian Academy (RACAI for short). Our best F1 score of 0.84984 was achieved using an ensemble of two systems: a gazetteer-based baseline and a RNN-based NER system, developed specially for PharmaCoNER 2019. We will describe the individual systems and the ensemble algorithm, compare the final system to the current state of the art, as well as discuss our results with respect to the quality of the training data and its annotation strategy. The resulting NER system is language independent, provided that language-dependent resources and preprocessing tools exist, such as tokenizers and POS taggers.


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BioRo: The Biomedical Corpus for the Romanian Language
Maria Mitrofan | Dan Tufiş
Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018)


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Bootstrapping a Romanian Corpus for Medical Named Entity Recognition
Maria Mitrofan
Proceedings of the International Conference Recent Advances in Natural Language Processing, RANLP 2017

Named Entity Recognition (NER) is an important component of natural language processing (NLP), with applicability in biomedical domain, enabling knowledge-discovery from medical texts. Due to the fact that for the Romanian language there are only a few linguistic resources specific to the biomedical domain, it was created a sub-corpus specific to this domain. In this paper we present a newly developed Romanian sub-corpus for medical-domain NER, which is a valuable asset for the field of biomedical text processing. We provide a description of the sub-corpus, informative statistics about data-composition and we evaluate an automatic NER tool on the newly created resource.

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Adapting the TTL Romanian POS Tagger to the Biomedical Domain
Maria Mitrofan | Radu Ion
Proceedings of the Biomedical NLP Workshop associated with RANLP 2017

This paper presents the adaptation of the Hidden Markov Models-based TTL part-of-speech tagger to the biomedical domain. TTL is a text processing platform that performs sentence splitting, tokenization, POS tagging, chunking and Named Entity Recognition (NER) for a number of languages, including Romanian. The POS tagging accuracy obtained by the TTL POS tagger exceeds 97% when TTL’s baseline model is updated with training information from a Romanian biomedical corpus. This corpus is developed in the context of the CoRoLa (a reference corpus for the contemporary Romanian language) project. Informative description and statistics of the Romanian biomedical corpus are also provided.