Annotation of the Serbian ELTeC Collection

Stanković, Ranka; Krstev, Cvetana; Todorovic, B.; Škorić, Mihailo

doi:10.18485/infotheca.2021.21.2.3

Cited by 6 publications

(5 citation statements)

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“…Models trained using the spaCy library, downloaded for each language from the corresponding repository https://spacy.io/models, were used to label the named entities. For the Italian language, the it_core_news_sm-3.4.0, 8 model was downloaded, trained on an automatically created corpus for the recognition of named entities, WikiNER, 9 based on the text and structure of Wikipedia (Nothman et al 2013), while for the Serbian language, a model trained on the corpus was implemented of old Serbian novels SrpCNNER ( Šandrih Todorović et al 2021), downloaded from the European Language Grid (ELG) platform. 10 In addition to recognizing named entities, the goal of the application was also to link them with items in the open knowledge base Wikidata.…”

Section: Service Implementationmentioning

confidence: 99%

“…15 In addition, entity labels of the PERS class, which mark persons, were set as the basic tag into which corresponding labels from other models were mapped: PER (Italian), PRS (Swedish), PERSON (Macedonian), per-sNAME (Polish). The label NORP (nationalities or religious or political groups) of nationalities, political and religious groups from the Japanese and Finnish models, then NAT_REL_POL from the Romanian one, are mapped into the label of the class DEMO, which are marked with demonyms, ethnic relations (Stanković et al 2021). Since some language models have a much richer set of named entity classes, for example English has 18 classes, Romanian 16, a column with a list of ignored labels is defined in the configuration file.…”

Section: Service Implementationmentioning

confidence: 99%

See 1 more Smart Citation

It-Sr-NER: Web Services for Recognizing and Linking Named Entities in Text and Displaying Them on a Web Map

Perišić,

Stanković,

Ikonić Nešić

et al. 2023

Infotheca

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The paper will present the results of the project "It-Sr-NER: Web services for named entities recognition, linking and mapping," in which teams from the University of Turin and the Society for Language Resources and Technologies JeRTeh participated, and whose goal was the development of the It-Sr-NER web service for named entity annotations in the text and displaying them on the map. Named entities in these services are names of persons, places, organizations, demonyms (ethnicities), events and works of art.

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Section: Service Implementationmentioning

confidence: 99%

Section: Service Implementationmentioning

confidence: 99%

It-Sr-NER: Web Services for Recognizing and Linking Named Entities in Text and Displaying Them on a Web Map

Perišić,

Stanković,

Ikonić Nešić

et al. 2023

Infotheca

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“…Building on GDEX, Stanković et al (2019) adopted machine learning to identify good candidate examples for Serbian. First, they analyzed lexical and syntactic features in a corpus compiled of illustrative examples from the five digitized volumes of the Serbian Academy of Sciences and Arts (SASA) dictionary.…”

Section: Pedagogical Corpora and Language Examplesmentioning

confidence: 99%

Data preparation in crowdsourcing for pedagogical purposes

Kuhn¹,

Holdt

Kosem

et al. 2022

SLO2.0

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One way to stimulate the use of corpora in language education is by making pedagogically appropriate corpora, labeled with different types of problems (sensitive content, offensive language, structural problems). However, manually labeling corpora is extremely time-consuming and a better approach should be found. We thus propose a combination of two approaches to the creation of problem-labeled pedagogical corpora of Dutch, Estonian, Slovene and Brazilian Portuguese: the use of games with a purpose and of crowdsourcing for the task. We conducted initial experiments to establish the suitability of the crowdsourcing task, and used the lessons learned to design the Crowdsourcing for Language Learning (CrowLL) game in which players identify problematic sentences, classify them, and indicate problematic excerpts. The focus of this paper is on data preparation, given the crucial role that such a stage plays in any crowdsourcing project dealing with the creation of language learning resources. We present the methodology for data preparation, offering a detailed presentation of source corpora selection, pedagogically oriented GDEX configurations, and the creation of lemma lists, with a special focus on common and language-dependent decisions. Finally, we offer a discussion of the challenges that emerged and the solutions that have been implemented so far.

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“…Old Serbian novels from the 1840s to the 1920s are collected in SrELTeC (SrELTeC, Table 1) and have been digitally preserved as part of the COST action CA16204 (Stanković et al, 2021). ELTeC 's section for Serbian contains 120 novels (Odebrecht et al, 2021).…”

Section: Monolingual Corporamentioning

confidence: 99%

Creating a stop word dictionary in Serbian

Marovac¹,

Avdić²,

Ljajić³

2021

Sci Pub Univ Novi Pazar Ser A

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By using natural language processing techniques, it is possible to get a lot of information from the extraction of document topics through mapping of document key words or content-based classification of documents, etc. To get this information, an important step is to separate words that carries informative value in a sentence from those words that do not affect its meaning. By using dictionaries of stop words specific to each natural language, the marking of words that do not carry meaning in the sentence is achieved. This paper presents creating a stop word dictionary in Serbian. The influence of stop words to the text processing is presented on three different data set. It is shown that by using proposed dictionary of Serbian stop words the data set dimension is reduced from 15% to 39%, while the quality of the obtained n-gram language models is improved.

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Annotation of the Serbian ELTeC Collection

Cited by 6 publications

References 0 publications

It-Sr-NER: Web Services for Recognizing and Linking Named Entities in Text and Displaying Them on a Web Map

It-Sr-NER: Web Services for Recognizing and Linking Named Entities in Text and Displaying Them on a Web Map

Data preparation in crowdsourcing for pedagogical purposes

Creating a stop word dictionary in Serbian

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