Pre-Experiments on Annotation of Russian Coreference Corpus

Toldova, Svetlana; Azerkovich, Ilya; Lyashevskaya, Olga; Михайловна, Ройтберг Анна; Юлия, Гришина; Ladygina, Alina; Галина, Сим; Мария, Васильева

doi:10.2139/ssrn.2709072

Cited by 4 publications

(4 citation statements)

References 9 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Missing/redundant markable (about 20% and 28% respectively) is the case when an annotator missed one or several mentions, although the coreference cluster is there in both annotation versions. For these cases we examined types of NPs missed by one annotator in the annotation from scratch stage, having preserved the taxonomy as in (Toldova et al, 2015) in order to compare them. See Table 2 to check numbers.…”

Section: Disagreement Analysismentioning

confidence: 99%

“…Thus, most of the largest corpora with coreference annotation contain predominantly English texts; however, with the growing interest in natural language processing of Non-English languages, corpora in other languages are being developed more often. As for the Russian language, there now exist two such datasets, one of them being RuCor (Toldova et al, 2014;Toldova et al, 2015) and the other AnCor (Budnikov et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

RuCoCo: a new Russian corpus with coreference annotation

Dobrovolskii¹,

Michurina²,

Ivoylova³

2022

Computational Linguistics and Intellectual Technologies

View full text Add to dashboard Cite

We present a new corpus with coreference annotation, Russian Coreference Corpus (RuCoCo). The goal of RuCoCo is to obtain a large number of annotated texts while maintaining high inter-annotator agreement. RuCoCo contains news texts in Russian, part of which were annotated from scratch, and for the rest the machine-generated annotations were refined by human annotators. The size of our corpus is one million words and around 150,000 mentions. We make the corpus publicly available.

show abstract

Section: Disagreement Analysismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

RuCoCo: a new Russian corpus with coreference annotation

Dobrovolskii¹,

Michurina²,

Ivoylova³

2022

Computational Linguistics and Intellectual Technologies

View full text Add to dashboard Cite

show abstract

“…Our experiments were conducted on RuCoref, a corpus of Russian texts with coreference annotation 2 released during RU-EVAL evaluation forum ( [36]). This corpus consists of short texts in a variety of genres: news, scientific articles, blog posts and fiction.…”

Section: Datamentioning

confidence: 99%

Mention Detection for Improving Coreference Resolution in Russian Texts: A Machine Learning Approach

Toldova¹,

Ionov²

2016

CyS

View full text Add to dashboard Cite

Coreference resolution task is a well-known NLP application that was proven helpful for all high-level NLP applications: machine translation, summarization, and others.Mention detection is the sub-task of detecting the discourse status of each noun phrase, classifying it as a discourse-new, singleton (mentioned only once) or discourse-old occurrence. It has been shown that this task applied to a coreference resolution system may increase its overall performance. So, we decided to adapt current approaches for English language into Russian.We present some quality results of experiments regarding classifiers for mention detection and their application into the coreference resolution task in Russian languages.

show abstract

“…Methods of anaphora resolution (see, e.g. [7]) could not help find different names for main characters.…”

Section: Introductionmentioning

confidence: 99%

Remus, Lupin and Moony Walk in a Bar… Grouping of Proper Names Related to the Same Denotation in Large Literary Texts Collections

Zykova,

Klyshinsky

2023

Computational Linguistics and Intellectual Technologies”

View full text Add to dashboard Cite

In this article, we present a method of anaphoric proper names detection in fictional texts using Word2Vec model and algorithms of community detection on graphs. This method allows grouping different namings of a single entity and can be useful as a part of preprocessing texts for further analysis such as building social networks or training neural models. The method uses large text collection, related to the same domain. The foundation of the method is training of a Word2Vec model using information on direct characters interactions. This model allows building a social graph of characters. Than, the Louvain algorithm is used to divide the graph into communities containing different names of characters related to the same denotation.

show abstract

Pre-Experiments on Annotation of Russian Coreference Corpus

Cited by 4 publications

References 9 publications

RuCoCo: a new Russian corpus with coreference annotation

RuCoCo: a new Russian corpus with coreference annotation

Mention Detection for Improving Coreference Resolution in Russian Texts: A Machine Learning Approach

Remus, Lupin and Moony Walk in a Bar… Grouping of Proper Names Related to the Same Denotation in Large Literary Texts Collections

Contact Info

Product

Resources

About