Alena Vasilevich is a linguist gone computational. She holds an international MSc degree in Language Science and Technology from Saarland University. Before diving into trees and graphs at Coreon, she worked as an NLP & ML researcher at an information management company and a market intelligence startup. At Coreon, Alena focuses on pragmatic data conversion, analytics and research. She is excited by challenges of marrying structured and unstructured data and supports the management in consulting tasks, helping customers deploy Multilingual Knowledge Systems. Alena's interests revolve around multilingual NLP and NLU, sentiment analysis of various granularity, and all things Python. She is fond of Cognitive Science and is an active participant of women-in-Tech Meetups
2021 Talk: Benefits of Collaborative AI vs. Manual Creation of the Graph: Taxonomization of IATE, the EU Terminology
In the realm of data-driven businesses, structured data, being highly organized and easily understood by machines, is a valuable resource. IATE, with almost one million concepts storing multilingual terms and metadata, holds a large part of the textual knowledge of the EU. However, it can only be accessed lexically, and the database concepts stand-alone. If IATE were taxonomized, i.e. related concepts linked up into knowledge graphs yielding a full-fledged ontology, its data could not only be consumed by linguists but would also become accessible by the machine-readable SPARQL endpoint, which makes it a powerful resource for AI projects, particularly within SMEs that rarely have the means to create multilingual formalized knowledge.
Coreon team elevated a sub-domain of IATE terminology into a multilingual knowledge graph. We taxonomized a flat list of 425 concepts within the COVID sub-domain, benchmarking two approaches to tackle this task: automatically through a custom-enhanced off-the-shelf language model and a manual creation of the knowledge graph by a linguist expert. The automatically created knowledge graph was later revised by a human, corrections and time effort measured and compared with performance metrics of the manual approach. In this talk, we will dwell on the performance and resource-saving advantages of our custom method and show how the achieved productivity rate can make the taxonomization of even large terminology databases economically viable.
We demonstrate empirically the effectiveness of our collaborative-robot approach in a typical industry use case scenario: using the resulting IATE/Covid graph for initialization of a Convolutional Neural Network (CNN) in a multilingual document classification task, we get a classification granularity that is not reachable by state-of-the-art models, such as non-initialized CNNs and zero-shot classifiers.