Odkrivanje koreferenčnosti v slovenskem jeziku na označenih besedilih iz coref149

Odkrivanje koreferenčnosti je ena izmed treh ključnih nalog ekstrakcije informacij iz besedil, kamor spadata še prepoznavanje imenskih entitet in ekstrakcija povezav. Namen odkrivanja koreferenčnosti je prek celotnega besedila ustrezno združiti vse omenitve entitet v skupine, v katerih vsaka skupina predstavlja svojo entiteto. Metode za reševanje te naloge se za nekatere jezike z več govorci razvijajo že dalj časa, medtem ko za slovenski jezik še niso bile izdelane. V prispevku predstavljamo nov, ročno označen korpus za odkrivanje koreferenčnosti v slovenskem jeziku – korpus coref149. Za avtomatsko odkrivanje koreferenčnosti smo prilagodili sistem SkipCor, ki smo ga izdelali za angleški jezik. Sistem SkipCor je na slovenskem gradivu dosegel 76 % ocene CoNLL 2012. Ob tem smo analizirali še vplive posameznih tipov značilk in preverili, katere so pogoste napake. Pri analiziranju besedil smo razvili tudi programsko knjižnico s spletnim vmesnikom, prek katere je možno izvesti vse opisane analize in neposredno primerjati njihovo uspešnost. Rezultati analiz so obetavni in primerljivi z rezultati pri drugih, bolj razširjenih jezikih. S tem smo dokazali, da je avtomatsko odkrivanje koreferenčnosti v slovenskem jeziku lahko uspešno, v prihodnosti pa bi bilo potrebno izdelati še večji in kvalitetnejši korpus, v katerem bodo koreferenčno naslovljene vse posebnosti slovenskega jezika, kar bi omogočilo izgradnjo učinkovitih metod za avtomatsko reševanje koreferenčnih problemov

Do PageRank-based author rankings outperform simple citation counts?

The basic indicators of a researcher's productivity and impact are still the number of publications and their citation counts. These metrics are clear, straightforward, and easy to obtain. When a ranking of scholars is needed, for instance in grant, award, or promotion procedures, their use is the fastest and cheapest way of prioritizing some scientists over others. However, due to their nature, there is a danger of oversimplifying scientific achievements. Therefore, many other indicators have been proposed including the usage of the PageRank algorithm known for the ranking of webpages and its modifications suited to citation networks. Nevertheless, this recursive method is computationally expensive and even if it has the advantage of favouring prestige over popularity, its application should be well justified, particularly when compared to the standard citation counts. In this study, we analyze three large datasets of computer science papers in the categories of artificial intelligence, software engineering, and theory and methods and apply 12 different ranking methods to the citation networks of authors. We compare the resulting rankings with self-compiled lists of outstanding researchers selected as frequent editorial board members of prestigious journals in the field and conclude that there is no evidence of PageRank-based methods outperforming simple citation counts.Comment: 28 pages, 5 figures, 6 table

LOD-Connected Offensive Language Ontology and Tagset Enrichment

CC BY 4.0The main focus of the paper is the definitional revision and enrichment of offensive language typology, making reference to publicly available offensive language datasets and testing them on available pretrained lexical embedding systems. We review over 60 available corpora and compare tagging schemas applied there while making an attempt to explain semantic differences between particular concepts of the category OFFENSIVE in English. A finite set of classes that cover aspects of offensive language representation along with linguistically sound explanations is presented, based on the categories originally proposed by Zampieri et al. [1, 2] in terms of offensive language categorization schemata and tested by means of Sketch Engine tools on a large web-based corpus. The schemata are juxtaposed and discussed with reference to non-contextual word embeddings FastText, Word2Vec, and Glove. The methodology for mapping from existing corpora to a unified ontology as presented in this paper is provided. The proposed schema will enable further comparable research and effective use of corpora of languages other than English. It will also be applied in building an enriched tagset to be trained and used on new data, with the application of recently developed LLOD techniques [3]

Implicit Offensive Language Taxonomy and Its Application for Automatic Extraction and Ontology

Purpose: In this current study, we intend to explore varying forms of implicit (mostly figurative) offensiveness (e.g., irony, metaphor, hyperbole, etc.) in order to propose a linguistic taxonomy of implicit offensiveness (and how it permeates explicit forms), and an ontology of offensive terms readily applicable to fine-tuned, pre-trained language models (word and phrase embedding). Offensive language has recently attracted great attention from computational scientists (e.g., Zampieri et al., 2019) and linguists alike (e.g., Haugh & Sinkeviciute, 2019). While in NLP scholars focus on ways of automatic extraction of what is generally and most often referred to as toxic language, in linguistics the concept of hate speech is frequently explored. Implicit offensive language, however, as opposed to explicit offence, has received little scholarly attention which so far has focused solely on single and unrelated concepts/terms. This paper aims at proposing an overarching model where varying subtypes of implicitness used in the context of offensive language are conceptually linked (Bączkowska et al., 2022)

Anotacijska shema i njezina evaluacija: primjer uvredljivoga jezika

The present paper focuses on the presentation and discussion of aspects of OFFENSIVE LANGUAGE linguistic annotation, including the creation, annotation practice, curation, and evaluation of an OFFENSIVE LANGUAGE annotation taxonomy scheme, that was first proposed in Lewandowska-Tomaszczyk et al. (2021). An extended offensive language ontology comprising 17 categories, structured in terms of 4 hierarchical levels, has been shown to represent the encoding of the defined offensive language schema, trained in terms of non-contextual word embeddings – i.e., Word2Vec and Fast Text, and eventually juxtaposed to the data acquired by using a pair wise training and testing analysis for existing categories in the HateBERT model (Lewandowska-Tomaszczyk et al. submitted). The study reports on the annotation practice in WG 4.1.1. Incivility in media and social media in the context of COST Action CA 18209 European network for Web-centred linguistic data science (Nexus Linguarum) with the INCEpTION tool (https://github.com/inception-project/inception) – a semantic annotation platform offering assistance in the annotation. The results partly support the proposed ontology of explicit offense and positive implicitness types to provide more variance among widely recognized types of figurative language (e.g., metaphorical, metonymic, ironic, etc.). The use of the annotation system and the representation of linguistic data were also evaluated in a series of the annotators’ comments, by means of a questionnaire and an open discussion. The annotation results and the questionnaire showed that for some of the categories there was low or medium inter-annotator agreement, and it was more challenging for annotators to distinguish between category items than between aspect items, with the category items offensive, insulting and abusive being the most difficult in this respect. The need for taxonomic simplification measures on the basis of these results has been recognized for further annotation practices.U ovome je radu predstavljen proces označavanja uvredljivoga jezika koji uključuje izradu klasifikacije toga jezika, označivačku praksu, vođenje procesa i evaluaciju. Klasifikacijska je shema prvi put predložena u Lewandowska-Tomaszczyk i dr. (2021). Proširena ontologija uvredljivoga jezika sadrži 17 kategorija posloženih u četiri hijerarhijske razine te tako predstavlja shemu uvredljivoga jezika koja je trenirana u okviru nekontekstualiziranih vektorskih prikaza riječi (engl. word embeddings) poput Word2Vec i Fast Text koji su naposljetku supostavljeni podatcima prikupljenima korištenjem analize parova i analize testiranja za postojeće kategorije u modelu HateBERT (Lewandowska-Tomaszczyk i dr., u postupku recenzije). U radu se izvještava o označivačkoj praksi u okviru radne grupe WG 4.1.1. Incivility in media and social media COST-ove akcije CA 18209 European network for Web-centred linguistic data science (Nexus Linguarum). Označavanje je provedeno u alatu INCEpTION (https://github.com/inception-project/inception) – platformi za semantičko označavanje koja ima ugrađene alate za takvu obradu podataka. Dobiveni rezultati podupiru predloženu ontologiju eksplicitnoga i implicitnoga uvredljivog jezika koja omogućuje veću raznovrsnost među već prepoznatim tipovima figurativnoga jezika (primjerice metafora, metonimija, ironija itd.). Upotreba sustava za označavanje i prikazivanje jezičnih podataka također je procijenjena u povratnim komentarima koje su pružili označivači. Komentari označivača prikupljeni su metodom upitnika te otvorenom raspravom. Na kraju je usustavljen niz preporuka za buduće označivačke prakse

Annotation Scheme and Evaluation: The Case of OFFENSIVE Language

Purpose: Offensive discourse refers to the presence of explicit or implicit verbal attacks towards individuals or groups and has been extensively analyzed in linguistics (e.g., Culpeper, 2005; Haugh & Sinkeviciute, 2019) and in NLP (e.g., OffensEval (Zampieri et al., 2020), HASOC (Mandl et al., 2019)), under the names of hate speech, abusive language, offensive language, etc. The paper focuses on the presentation and discussion of aspects of the linguistic annotation of OFFENSIVE LANGUAGE, including creation, annotation practice, curation, and evaluation of an OFFENSIVE LANGUAGE annotation taxonomy scheme first proposed in Lewandowska-Tomaszczyk et al. (2021) and Žitnik et al. (in press). An extended offensive language ontology in terms of 17 categories, structured in terms of 4 hierarchical levels, has been shown to represent the encoding of the defined offensive language schema, trained in terms of non-contextual word embeddings – i.e., Word2Vec and Fast Text – and eventually juxtaposed to the data acquired by using pairwise training and testing analysis for existing categories in the HateBERT model

Iterative semantic information extraction from unstructured text sources

Nowadays we generate an enormous amount of data and most of it is unstructured. The users of Internet post more than 200,000 text documents and together write more than 200 million e-mails online every single minute. We would like to access this data in a structured form and that is why we throughout this dissertation deal with information extraction from text sources. Information extraction is a type of information retrieval, where the main tasks are named entity recognition, relationship extraction, and coreference resolution. The dissertation consists of the four main chapters, where each of them represents a separate information extraction task and the last chapter which introduces a combination all of the three tasks into an iterative method within an end-to-end information extraction system. First we introduce the task of coreference resolution with its goal of merging all of the mentions that refer to a specific entity. We propose SkipCor system that casts the task into a sequence tagging problem for which first order probabilistic models can be used. To enable the detection of distant coreferent mentions we propose an innovative transformation into skip-mention sequences and achieve comparable or better results than other known approaches. We also use a similar transformation for relationship extraction. There we use different tags and rules that enable the extraction of hierarchical relationships. The proposed solution achieves the best result at the relationship extraction challenge between genes that form a gene regulations network. Lastly we present the oldest and most thoroughly researched task of named entity recognition. The task deals with a tagging of one or more words that represent a specific entity type - for example, persons. In the dissertation we adapt the use of standard procedures for the sequence tagging tasks and achieve the seventh rank at the chemical compound and drug name recognition challenge. We successfully manage to solve all of the three problems using linear-chain conditional random fields models. We combine the tasks in an iterative method that accepts an unstructured text as input and returns extracted entities along with relationships between them. The output is represented according to a system ontology which provides better data interoperability. The information extraction field for the Slovene language is not yet well researched which is why we also include a list of translations of the selected terms from English to Slovene