Svetla Koeva


2022

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Introducing the CURLICAT Corpora: Seven-language Domain Specific Annotated Corpora from Curated Sources
Tamás Váradi | Bence Nyéki | Svetla Koeva | Marko Tadić | Vanja Štefanec | Maciej Ogrodniczuk | Bartłomiej Nitoń | Piotr Pęzik | Verginica Barbu Mititelu | Elena Irimia | Maria Mitrofan | Dan Tufiș | Radovan Garabík | Simon Krek | Andraž Repar
Proceedings of the Thirteenth Language Resources and Evaluation Conference

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.

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Multilingual Image Corpus – Towards a Multimodal and Multilingual Dataset
Svetla Koeva | Ivelina Stoyanova | Jordan Kralev
Proceedings of the Thirteenth Language Resources and Evaluation Conference

One of the processing tasks for large multimodal data streams is automatic image description (image classification, object segmentation and classification). Although the number and the diversity of image datasets is constantly expanding, still there is a huge demand for more datasets in terms of variety of domains and object classes covered. The goal of the project Multilingual Image Corpus (MIC 21) is to provide a large image dataset with annotated objects and object descriptions in 24 languages. The Multilingual Image Corpus consists of an Ontology of visual objects (based on WordNet) and a collection of thematically related images whose objects are annotated with segmentation masks and labels describing the ontology classes. The dataset is designed both for image classification and object detection and for semantic segmentation. The main contributions of our work are: a) the provision of large collection of high quality copyright-free images; b) the formulation of the Ontology of visual objects based on WordNet noun hierarchies; c) the precise manual correction of automatic object segmentation within the images and the annotation of object classes; and d) the association of objects and images with extended multilingual descriptions based on WordNet inner- and interlingual relations. The dataset can be used also for multilingual image caption generation, image-to-text alignment and automatic question answering for images and videos.

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Curated Multilingual Language Resources for CEF AT (CURLICAT): overall view
Tamás Váradi | Marko Tadić | Svetla Koeva | Maciej Ogrodniczuk | Dan Tufiş | Radovan Garabík | Simon Krek | Andraž Repar
Proceedings of the 23rd Annual Conference of the European Association for Machine Translation

The work in progress on the CEF Action CURLICA T is presented. The general aim of the Action is to compile curated datasets in seven languages of the con- sortium in domains of relevance to Euro- pean Digital Service Infrastructures (DSIs) in order to enhance the eTransla- tion services.

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Ontology of Visual Objects
Svetla Koeva
Proceedings of the 5th International Conference on Computational Linguistics in Bulgaria (CLIB 2022)

The focus of the paper is the Ontology of Visual Objects based on WordNet noun hierarchies. In particular, we present a methodology for bidirectional ontology engineering, which integrates the pre-existing knowledge resources and the selection of visual objects within the images representing particular thematic domains. The Ontology of Visual Objects organizes concepts labeled by corresponding classes (dominant classes, classes that are attributes to dominant classes, and classes that serve only as parents to dominant classes), relations between concepts and axioms defining the properties of the relations. The Ontology contains 851 classes (706 dominant and attribute classes), 15 relations and a number of axioms built upon them. The definition of relations between dominant and attribute classes and formulations of axioms based on the properties of the relations offers a reliable means for automatic object or image classification and description.

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Image Models for large-scale Object Detection and Classification
Jordan Kralev | Svetla Koeva
Proceedings of the 5th International Conference on Computational Linguistics in Bulgaria (CLIB 2022)

Recent developments in computer vision applications that are based on machine learning models allow real-time object detection, segmentation and captioning in image or video streams. The paper presents the development of an extension of the 80 COCO categories into a novel ontology with more than 700 classes covering 130 thematic subdomains related to Sport, Transport, Arts and Security. The development of an image dataset of object segmentation was accelerated by machine learning for automatic generation of objects’ boundaries and classes. The Multilingual image dataset contains over 20,000 images and 200,000 annotations. It was used to pre-train 130 models for object detection and classification. We show the established approach for the development of the new models and their integration into an application and evaluation framework.

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Ontology Supported Frame Classification
Svetla Koeva | Emil Doychev
Proceedings of the 5th International Conference on Computational Linguistics in Bulgaria (CLIB 2022)

We present BulFrame – a web-based system designed for creating, editing, validating and viewing conceptual frames. A unified theoretical model for the formal presentation of Conceptual frames is offered, which predetermines the architecture of the system with which the data is processed. A Conceptual frame defines a unique set of syntagmatic relations between verb synsets representing the frame and noun synsets expressing the frame elements. Thereby, the notion of Conceptual frame combines semantic knowledge presented in WordNet and FrameNet and builds upon it. The main difference with FrameNet semantic frames is the definition of the sets of nouns that can be combined with a given verb. This is achieved by an ontological representation of noun semantic classes. The framework is built and evaluated with Conceptual frames for Bulgarian verbs.

2021

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Multilingual Image Corpus: Annotation Protocol
Svetla Koeva
Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2021)

In this paper, we present work in progress aimed at the development of a new image dataset with annotated objects. The Multilingual Image Corpus consists of an ontology of visual objects (based on WordNet) and a collection of thematically related images annotated with segmentation masks and object classes. We identified 277 dominant classes and 1,037 parent and attribute classes, and grouped them into 10 thematic domains such as sport, medicine, education, food, security, etc. For the selected classes a large-scale web image search is being conducted in order to compile a substantial collection of high-quality copyright free images. The focus of the paper is the annotation protocol which we established to facilitate the annotation process: the Ontology of visual objects and the conventions for image selection and for object segmentation. The dataset is designed both for image classification and object detection and for semantic segmentation. In addition, the object annotations will be supplied with multilingual descriptions by using freely available wordnets.

2020

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Categorisation of Bulgarian Legislative Documents
Nikola Obreshkov | Martin Yalamov | Svetla Koeva
Proceedings of the 4th International Conference on Computational Linguistics in Bulgaria (CLIB 2020)

The paper presents the categorisation of Bulgarian MARCELL corpus in toplevel EuroVoc domains. The Bulgarian MARCELL corpus is part of a recently developed multilingual corpus representing the national legislation in seven European countries. We performed several experiments with JEX Indexer, with neural networks and with a basic method measuring the domain-specific terms in documents annotated in advance with IATE terms and EuroVoc descriptors (combined with grouping of a primary document and its satellites, term extraction and parsing of the titles of the documents). The evaluation shows slight overweight of the basic method, which makes it appropriate as the categorisation should be a module of a NLP Pipeline for Bulgarian that is continuously feeding and annotating the Bulgarian MARCELL corpus with newly issued legislative documents.

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The European Language Technology Landscape in 2020: Language-Centric and Human-Centric AI for Cross-Cultural Communication in Multilingual Europe
Georg Rehm | Katrin Marheinecke | Stefanie Hegele | Stelios Piperidis | Kalina Bontcheva | Jan Hajič | Khalid Choukri | Andrejs Vasiļjevs | Gerhard Backfried | Christoph Prinz | José Manuel Gómez-Pérez | Luc Meertens | Paul Lukowicz | Josef van Genabith | Andrea Lösch | Philipp Slusallek | Morten Irgens | Patrick Gatellier | Joachim Köhler | Laure Le Bars | Dimitra Anastasiou | Albina Auksoriūtė | Núria Bel | António Branco | Gerhard Budin | Walter Daelemans | Koenraad De Smedt | Radovan Garabík | Maria Gavriilidou | Dagmar Gromann | Svetla Koeva | Simon Krek | Cvetana Krstev | Krister Lindén | Bernardo Magnini | Jan Odijk | Maciej Ogrodniczuk | Eiríkur Rögnvaldsson | Mike Rosner | Bolette Pedersen | Inguna Skadiņa | Marko Tadić | Dan Tufiș | Tamás Váradi | Kadri Vider | Andy Way | François Yvon
Proceedings of the Twelfth Language Resources and Evaluation Conference

Multilingualism is a cultural cornerstone of Europe and firmly anchored in the European treaties including full language equality. However, language barriers impacting business, cross-lingual and cross-cultural communication are still omnipresent. Language Technologies (LTs) are a powerful means to break down these barriers. While the last decade has seen various initiatives that created a multitude of approaches and technologies tailored to Europe’s specific needs, there is still an immense level of fragmentation. At the same time, AI has become an increasingly important concept in the European Information and Communication Technology area. For a few years now, AI – including many opportunities, synergies but also misconceptions – has been overshadowing every other topic. We present an overview of the European LT landscape, describing funding programmes, activities, actions and challenges in the different countries with regard to LT, including the current state of play in industry and the LT market. We present a brief overview of the main LT-related activities on the EU level in the last ten years and develop strategic guidance with regard to four key dimensions.

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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 Twelfth 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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Natural Language Processing Pipeline to Annotate Bulgarian Legislative Documents
Svetla Koeva | Nikola Obreshkov | Martin Yalamov
Proceedings of the Twelfth Language Resources and Evaluation Conference

The paper presents the Bulgarian MARCELL corpus, part of a recently developed multilingual corpus representing the national legislation in seven European countries and the NLP pipeline that turns the web crawled data into structured, linguistically annotated dataset. The Bulgarian data is web crawled, extracted from the original HTML format, filtered by document type, tokenised, sentence split, tagged and lemmatised with a fine-grained version of the Bulgarian Language Processing Chain, dependency parsed with NLP- Cube, annotated with named entities (persons, locations, organisations and others), noun phrases, IATE terms and EuroVoc descriptors. An orchestrator process has been developed to control the NLP pipeline performing an end-to-end data processing and annotation starting from the documents identification and ending in the generation of statistical reports. The Bulgarian MARCELL corpus consists of 25,283 documents (at the beginning of November 2019), which are classified into eleven types.

2018

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Mapping WordNet Concepts with CPA Ontology
Svetla Koeva | Cvetana Dimitrova | Valentina Stefanova | Dimitar Hristov
Proceedings of the 9th Global Wordnet Conference

The paper discusses the enrichment of WordNet data through merging of WordNet concepts and Corpus Pattern Analysis (CPA) semantic types. The 253 CPA semantic types are mapped to the respective WordNet concepts. As a result of mapping, the hyponyms of a synset to which a CPA semantic type is mapped inherit not only the respective WordNet semantic primitive but also the CPA semantic type.

2016

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Automatic Prediction of Morphosemantic Relations
Svetla Koeva | Svetlozara Leseva | Ivelina Stoyanova | Tsvetana Dimitrova | Maria Todorova
Proceedings of the 8th Global WordNet Conference (GWC)

This paper presents a machine learning method for automatic identification and classification of morphosemantic relations (MSRs) between verb and noun synset pairs in the Bulgarian WordNet (BulNet). The core training data comprise 6,641 morphosemantically related verb–noun literal pairs from BulNet. The core dataset were preprocessed quality-wise by applying validation and reorganisation procedures. Further, the data were supplemented with negative examples of literal pairs not linked by an MSR. The designed supervised machine learning method uses the RandomTree algorithm and is implemented in Java with the Weka package. A set of experiments were performed to test various approaches to the task. Future work on improving the classifier includes adding more training data, employing more features, and fine-tuning. Apart from the language specific information about derivational processes, the proposed method is language independent.

2015

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Automatic Classification of WordNet Morphosemantic Relations
Svetlozara Leseva | Ivelina Stoyanova | Maria Todorova | Tsvetana Dimitrova | Borislav Rizov | Svetla Koeva
The 5th Workshop on Balto-Slavic Natural Language Processing

2014

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The Strategic Impact of META-NET on the Regional, National and International Level
Georg Rehm | Hans Uszkoreit | Sophia Ananiadou | Núria Bel | Audronė Bielevičienė | Lars Borin | António Branco | Gerhard Budin | Nicoletta Calzolari | Walter Daelemans | Radovan Garabík | Marko Grobelnik | Carmen García-Mateo | Josef van Genabith | Jan Hajič | Inma Hernáez | John Judge | Svetla Koeva | Simon Krek | Cvetana Krstev | Krister Lindén | Bernardo Magnini | Joseph Mariani | John McNaught | Maite Melero | Monica Monachini | Asunción Moreno | Jan Odijk | Maciej Ogrodniczuk | Piotr Pęzik | Stelios Piperidis | Adam Przepiórkowski | Eiríkur Rögnvaldsson | Michael Rosner | Bolette Pedersen | Inguna Skadiņa | Koenraad De Smedt | Marko Tadić | Paul Thompson | Dan Tufiş | Tamás Váradi | Andrejs Vasiļjevs | Kadri Vider | Jolanta Zabarskaite
Proceedings of the Ninth International Conference on Language Resources and Evaluation (LREC'14)

This article provides an overview of the dissemination work carried out in META-NET from 2010 until early 2014; we describe its impact on the regional, national and international level, mainly with regard to politics and the situation of funding for LT topics. This paper documents the initiative’s work throughout Europe in order to boost progress and innovation in our field.

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Proceedings of Workshop on Lexical and Grammatical Resources for Language Processing
Jorge Baptista | Pushpak Bhattacharyya | Christiane Fellbaum | Mikel Forcada | Chu-Ren Huang | Svetla Koeva | Cvetana Krstev | Eric Laporte
Proceedings of Workshop on Lexical and Grammatical Resources for Language Processing

2013

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Wordnet-Based Cross-Language Identification of Semantic Relations
Ivelina Stoyanova | Svetla Koeva | Svetlozara Leseva
Proceedings of the 4th Biennial International Workshop on Balto-Slavic Natural Language Processing

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Text Modification for Bulgarian Sign Language Users
Slavina Lozanova | Ivelina Stoyanova | Svetlozara Leseva | Svetla Koeva | Boian Savtchev
Proceedings of the Second Workshop on Predicting and Improving Text Readability for Target Reader Populations

2012

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Harnessing NLP Techniques in the Processes of Multilingual Content Management
Anelia Belogay | Diman Karagyozov | Svetla Koeva | Cristina Vertan | Adam Przepiórkowski | Dan Cristea | Plovios Raxis
Proceedings of the Demonstrations at the 13th Conference of the European Chapter of the Association for Computational Linguistics

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Bulgarian X-language Parallel Corpus
Svetla Koeva | Ivelina Stoyanova | Rositsa Dekova | Borislav Rizov | Angel Genov
Proceedings of the Eighth International Conference on Language Resources and Evaluation (LREC'12)

The paper presents the methodology and the outcome of the compilation and the processing of the Bulgarian X-language Parallel Corpus (Bul-X-Cor) which was integrated as part of the Bulgarian National Corpus (BulNC). We focus on building representative parallel corpora which include a diversity of domains and genres, reflect the relations between Bulgarian and other languages and are consistent in terms of compilation methodology, text representation, metadata description and annotation conventions. The approaches implemented in the construction of Bul-X-Cor include using readily available text collections on the web, manual compilation (by means of Internet browsing) and preferably automatic compilation (by means of web crawling ― general and focused). Certain levels of annotation applied to Bul-X-Cor are taken as obligatory (sentence segmentation and sentence alignment), while others depend on the availability of tools for a particular language (morpho-syntactic tagging, lemmatisation, syntactic parsing, named entity recognition, word sense disambiguation, etc.) or for a particular task (word and clause alignment). To achieve uniformity of the annotation we have either annotated raw data from scratch or transformed the already existing annotation to follow the conventions accepted for BulNC. Finally, actual uses of the corpora are presented and conclusions are drawn with respect to future work.

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ATLAS - Human Language Technologies integrated within a Multilingual Web Content Management System
Svetla Koeva
Proceedings of the Joint Workshop on Exploiting Synergies between Information Retrieval and Machine Translation (ESIRMT) and Hybrid Approaches to Machine Translation (HyTra)

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Application of Clause Alignment for Statistical Machine Translation
Svetla Koeva | Svetlozara Leseva | Ivelina Stoyanova | Rositsa Dekova | Angel Genov | Borislav Rizov | Tsvetana Dimitrova | Ekaterina Tarpomanova | Hristina Kukova
Proceedings of the Sixth Workshop on Syntax, Semantics and Structure in Statistical Translation

2010

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Bulgarian National Corpus Project
Svetla Koeva | Diana Blagoeva | Siya Kolkovska
Proceedings of the Seventh International Conference on Language Resources and Evaluation (LREC'10)

The paper presents Bulgarian National Corpus project (BulNC) - a large-scale, representative, online available corpus of Bulgarian. The BulNC is also a monolingual general corpus, fully morpho-syntactically (and partially semantically) annotated, and manually provided with detailed meta-data descriptions. Presently the Bulgarian National corpus consists of about 320 000 000 graphical words and includes more than 10 000 samples. Briefly the corpus structure and the accepted criteria for representativeness and well-balancing are presented. The query language for advance search of collocations and concordances is demonstrated with some examples - it allows to retrieve word combinations, ordered queries, inflexionally and semantically related words, part-of-speech tags, utilising Boolean operations and grouping as well. The BulNC already plays a significant role in natural language processing of Bulgarian contributing to scientific advances in spelling and grammar checking, word sense disambiguation, speech recognition, text categorisation, topic extraction and machine translation. The BulNC can also be used in different investigations going beyond the linguistics: library studies, social sciences research, teaching methods studies, etc.

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Lexicon and Grammar in Bulgarian FrameNet
Svetla Koeva
Proceedings of the Seventh International Conference on Language Resources and Evaluation (LREC'10)

In this paper, we report on our attempt at assigning semantic information from the English FrameNet to lexical units in the Bulgarian valency lexicon. The paper briefly presents the model underlying the Bulgarian FrameNet (BulFrameNet): each lexical entry consists of a lexical unit; a semantic frame from the English FrameNet, expressing abstract semantic structure; a grammatical class, defining the inflexional paradigm; a valency frame describing (some of) the syntactic and lexical-semantic combinatory properties (an optional component); and (semantically and syntactically) annotated examples. The target is a corpus-based lexicon giving an exhaustive account of the semantic and syntactic combinatory properties of an extensive number of Bulgarian lexical units. The Bulgarian FrameNet database so far contains unique descriptions of over 3 000 Bulgarian lexical units, approx. one tenth of them aligned with appropriate semantic frames, supports XML import and export and will be accessible, i.e., displayed and queried via the web.

2009

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E-Connecting Balkan Languages
Cvetana Krstev | Ranka Stanković | Duško Vitas | Svetla Koeva
Proceedings of the Workshop Multilingual resources, technologies and evaluation for central and Eastern European languages

2008

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Chooser: a Multi-Task Annotation Tool
Svetla Koeva | Borislav Rizov | Svetlozara Leseva
Proceedings of the Sixth International Conference on Language Resources and Evaluation (LREC'08)

The paper presents a tool assisting manual annotation of linguistic data developed at the Department of Computational linguistics, IBL-BAS. Chooser is a general-purpose modular application for corpus annotation based on the principles of commonality and reusability of the created resources, language and theory independence, extendibility and user-friendliness. These features have been achieved through a powerful abstract architecture within the Model-View-Controller paradigm that is easily tailored to task-specific requirements and readily extendable to new applications. The tool is to a considerable extent independent of data format and representation and produces outputs that are largely consistent with existing standards. The annotated data are therefore reusable in tasks requiring different levels of annotation and are accessible to external applications. The tool incorporates edit functions, pass and arrangement strategies that facilitate annotators’ work. The relevant module produces tree-structured and graph-based representations in respective annotation modes. Another valuable feature of the application is concurrent access by multiple users and centralised storage of lexical resources underlying annotation schemata, as well as of annotations, including frequency of selection, updates in the lexical database, etc. Chooser has been successfully applied to a number of tasks: POS tagging, WS and syntactic annotation.

2007

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Multi-word Term Extraction for Bulgarian
Svetla Koeva
Proceedings of the Workshop on Balto-Slavonic Natural Language Processing

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