Publications

Explore the latest research and findings in linguistic annotation and language processing technologies from the CoBaLD Annotation Project

2024

Petrova, Maria Andreevna and Ivoylova, Alexandra M and Tishchenkova, Anastasia. CoBaLD Annotation: The Enrichment of the Enhanced Universal Dependencies with the Semantical Pattern In Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024). 2024 Abstract:

The paper is devoted to the annotation format aimed at morphological, syntactic, and especially semantic markup. The format combines the Enhanced UD morphosyntax and the Compreno semantic pattern, enriching the UD annotation with word meanings and labels for semantic relations between words.

To adapt the Compreno semantics for the current purpose, we reduced the number of the semantic fields denoting lexical meanings by using hyperonym fields. Moreover, we used a generalized variant of the semantic relations, as the original roles possess rather narrow meanings, which makes them too numerous.

Creating such a format demands the Compreno-to-UD morphosyntax conversion as well, which, in turn, demands solving the asymmetry problem between the models. The asymmetry concerns tokenization, lemmatization, POS-tagging, sets of grammatical features, and dependency heads. To overcome this problem, the Compreno-to-UD converter was created.

As an application, the work presents a 150,000-token corpus of English news annotated according to the standard.

2023

Ivoylova, Alexandra and Dyachkova, Darya and Petrova, Maria and Michurina, Mariia. The problem of linguistic markup conversion: the transformation of the Compreno markup into the UD format In International Conference on Computational Linguistics and Intellectual Technologies “Dialog”. 2023 Abstract:

The linguistic markup is an important NLP task. Currently, there are several popular formats of markup (Universal Dependencies, Prague Dependencies, and so on), which are mostly focused on morphology and syntax. Full semantic markup can be found in the ABBYY Compreno model. However, the structure of this format differs significantly from the models mentioned above.

In the given work, we convert the Compreno markup into the UD format, which is rather popular among NLP researchers, and enrich it with the semantic pattern. Compreno and UD present morphology and syntax differently, particularly in tokenization, POS-tagging, ellipsis, coordination, and other aspects, making the conversion from one format to another more complicated. Nevertheless, the conversion allowed us to create the UD markup containing not only morpho-syntactic information but also semantic information.

Petrova, Maria and Ivoylova, Alexandra and Bayuk, Ilya and Dyachkova, Darya and Michurina, Mariia. The CoBaLD Annotation Project: the Creation and Application of the full Morpho-Syntactic and Semantic Markup Standard In Proceedings of the International Conference “Dialogue. Vol. 2023. 2023 Abstract:

The current paper is devoted to the Compreno-Based Linguistic Data (CoBaLD) Annotation Project aimed at creating text corpora annotated with full morphological, syntactic, and semantic markup. The first task of the project is to suggest a standard for the full universal markup which would include both morphosyntactic and semantic patterns.

To solve this problem, one needs a markup model that includes all necessary markup levels and presents the markup in a format convenient for users. The latter implies not only the fullness of the markup but also its structural simplicity and homogeneity. As a base for the markup, we have chosen a simplified version of the Compreno model, and for data presentation format, we have taken Universal Dependencies.

At the second stage of the project, the Russian corpus with 400 thousand tokens (CoBaLD-Rus) has been created, which is annotated according to the given standard.

The third stage is devoted to testing the new format. For this purpose, we have held the SEMarkup Shared Task aimed at creating parsers that would produce full morpho-syntactic and semantic markup. Within this task, we have elaborated a neural network-based parser trained on our dataset, which allows one to annotate new texts with the CoBaLD-standard.

Our further plans are to create fully annotated corpora for other languages and to carry out experiments on language transfers of the current markup to other languages.