TERM inator: A System for Scientific Texts Processing (2022)

Abstract: This paper is devoted to the extraction of entities and semantic relations between them from scientific texts, where we consider scientific terms as entities. In this paper, we present a dataset that includes annotations for two tasks and develop a system called TERMinator for the study of the influence of language models on term recognition and comparison of different approaches for relation extraction. Experiments show that language models pre-trained on the target language are not always show the best performance. Also adding some heuristic approaches may improve the overall quality of the particular task. The developed tool and the annotated corpus are publicly available at https://github.com/iis-research-team/terminator and may be useful for other researchers.

## introduction nowadays the amount of scientific publications is constantly growing. in this regard, the processing of scientific texts becomes especially relevant in relation to rapidly developing scientific fields, for example, computer science. information extraction from scientific texts can be useful in domainspecific areas, for completion of knowledge graphs, in search and question-answering systems. this paper describes the study on entity recognition and relation extraction from scientific texts on computer science in russian. currently, there are a number of datasets with annotations of entities and relations in a general domain @xcite @xcite , biomedical domain @xcite @xcite , or even multi-domains @xcite . still it is more difficult to find a publicly available dataset such as scierc @xcite for scientific fields other than biomedical, and especially in languages other than english. despite that the named entity recognition task is well studied, it still faces multiple challenges @xcite , namely, ner in domain-specific areas @xcite , ner from noisy data @xcite , code-mixed data @xcite , and detection of fine-grained and nested named entities @xcite @xcite . this is caused by several issues: defining boundaries of compound terms; recognition of whether a lexical unit is part of a compound term; identification of a lexical unit as a term depending on the context and topic of the text in which this lexical unit is used etc. the relation extraction task also remains an unsolved problem, as it often requires the use of knowledge outside the text (for example, from knowledge bases or obtained in another way), and also due to the lack of a large amount of labeled data in different languages. selection of the most appropriate language model, which is able to provide the best quality for extraction of terms and relations from scientific texts is one of the relevant issues. our experimental results not only show the usefulness of the proposed dataset, but also provide baselines for further research. we make the following contributions: ## data preparation for the experiments we create an annotated dataset, which consists of abstracts of scientific papers on information technology in russian. as entities we consider nouns or noun groups, which are terms in this particular domain. terms that we recognize as entities may consist of one or several tokens ("software", "non-preemptive multitasking"), abbreviations ("cpu", "dll"), names of programming languages ("python", "c++") and libraries ("pytorch", "spacy"), hyphenated concepts containing latin characters ("n-gram", "webservice"). thereby we consider all possible chains of tokens that can be terms, except for those that are recursive or overlap. the entities are marked in the bio format: each token is assigned a b-term tag if it is the initial tag for an entity, i-term if it is inside a term, or o if it is outside any entity. statistics for our dataset is presented in