Bilingual Terminology Extraction Using Neural Word Embeddings on Comparable Corpora (2021)

Abstract: Term and glossary management are vital steps of preparation of every language specialist, and they play a very important role at the stage of education of translation professionals. The growing trend of efficient time management and constant time constraints we may observe in every job sector increases the necessity of the automatic glossary compilation. Many well-performing bilingual AET systems are based on processing parallel data, however, such parallel corpora are not always available for a specific domain or a language pair. Domain-specific, bilingual access to information and its retrieval based on comparable corpora is a very promising area of research that requires a detailed analysis of both available data sources and the possible extraction techniques. This work focuses on domain-specific automatic terminology extraction from comparable corpora for the English – Russian language pair by utilizing neural word embeddings.

## introduction terminology studies represent a strictly hierarchical discipline that forms a basement for global scientific knowledge. one of the main challenges of the term extraction task is mostly associated with the "deficiency of unique norms and rules among linguists and lexicographers to identify a massive amount of modern terminology vocabulary, systematize and place it on international databases because the appearance of new scientific and technological notions occurs faster than it can be defined" @xcite . despite the controversy about the notion on "termhood", the main obstacle in the term extraction process consists in the restricted access to cross-lingual parallel content being a major bottleneck for compiling a high-quality specialised dictionary with minimum effort. many well-performing bilingual aet systems are based on processing parallel data, however, such parallel corpora are not always available for a specific domain or a language pair @xcite . even though the number of parallel corpora available online is gradually increasing, this number is still not sufficient to include a wide range of all possible domains, especially if the domain is relatively new. comparable corpora, on the contrary, can be an excellent alternative with a lot of potential and greater possibilities for domain-and language adaptability @xcite . other advantages of comparable corpora include its relatively cheap construction process and its usefulness in building a domain-specific corpus even for low-resource languages and underrepresented topics. it is important to note that a specialised comparable corpus is traditionally of a smaller size (when compared to a general-language corpus), this is usually related to the scarcity of resources in languages other than english @xcite . such a corpus may also pose issues associated with the unfavorable duplication of web-crawled texts, noisy data, and a general lack of control over the corpus compilation process. nevertheless, the use of comparable corpora remains a very promising area of research in both monolingual and bilingual term extraction. this research is hypothesized on the idea that language specialists are usually provided with the minimum information about an event and little or no additional information about the specific domain they must work with @xcite . the general pipeline of the terminology extraction process can be divided into three main stages: 1. building a corpus; 2. application of term extraction methods; 3. either automatic or manual validation of extracted term candidates. considering all the advantages of using a bilingual comparable corpus, we would like to exploit its properties in the terminology extraction task and make it domain-independent i.e., adaptable to any other domain where the compilation of corpora is possible; however, this work will be mainly focused on covid -19 domain whereas our rationale will be discussed later in the next sections 2 related work ## research questions the main motivation of this research relates to the restricted access to cross-lingual content being a major obstacle for compiling a high-quality specialised dictionary with minimum effort. thus, our research questions can be formulated as follows: