Alsu Vakhitova


2022

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TaxFree: a Visualization Tool for Candidate-free Taxonomy Enrichment
Irina Nikishina | Ivan Andrianov | Alsu Vakhitova | Alexander Panchenko
Proceedings of the 2nd Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 12th International Joint Conference on Natural Language Processing: System Demonstrations

Taxonomies are widely used in a various number of downstream NLP tasks and, therefore, should be kept up-to-date. In this paper, we present TaxFree, an open source system for taxonomy visualisation and automatic Taxonomy Enrichment without pre-defined candidates on the example of WordNet-3.0. As oppose to the traditional task formulation (where the list of new words is provided beforehand), we provide an approach for automatic extension of a taxonomy using a large pre-trained language model. As an advantage to the existing visualisation tools of WordNet, TaxFree also integrates graphic representations of synsets from ImageNet. Such visualisation tool can be used for both updating taxonomies and inspecting them for the required modifications.

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Cross-Modal Contextualized Hidden State Projection Method for Expanding of Taxonomic Graphs
Irina Nikishina | Alsu Vakhitova | Elena Tutubalina | Alexander Panchenko
Proceedings of TextGraphs-16: Graph-based Methods for Natural Language Processing

Taxonomy is a graph of terms organized hierarchically using is-a (hypernymy) relations. We suggest novel candidate-free task formulation for the taxonomy enrichment task. To solve the task, we leverage lexical knowledge from the pre-trained models to predict new words missing in the taxonomic resource. We propose a method that combines graph-, and text-based contextualized representations from transformer networks to predict new entries to the taxonomy. We have evaluated the method suggested for this task against text-only baselines based on BERT and fastText representations. The results demonstrate that incorporation of graph embedding is beneficial in the task of hyponym prediction using contextualized models. We hope the new challenging task will foster further research in automatic text graph construction methods.