T ax F ree: a Visualization Tool for Candidate-free Taxonomy Enrichment (2022)

Abstract: 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.

## introduction in this paper, we focus on visualisation of taxonomic structures which are quite relevant for many natural language processing (nlp) tasks, e.g. lexical entailment @xcite and entity linking @xcite . taxonomies are tree-like structures where words are considered as nodes (synsets) and the edges are the relations between them. such kind of relationship is called a hypo-hypernym relationship. for instance, let us consider two words: "apple" and "fruit". the former word is hyponym ("child") to the latter and the latter is hypernym ("parent") to the former. however, taxonomies are hard to maintain, while the manual taxonomy annotation process is very expensive and time-consuming. moreover, it requires expertise in the field. the process of selection new words is even more challenging for the large existing taxonomies like wordnet @xcite , as most existing words already present there. we expect that the large pre-trained language models such as bert @xcite and gpt @xcite could be useful in the task, as they are pre-trained on large-scale corpora. it has been proven that language models possess syntactic, semantic and word knowledge which could be applied for further language inference @xcite . in this demo we demonstrate how the existing taxonomy can be enriched automatically without predefined candidates on wordnet-3.0. figure 1 demonstrates the candidate-free task setting where the node "milk.n.01" ("n" stands for "noun", "01" denotes the ordinal number of word sense, the standard notation for the synset in wordnet) is extended with multiple synsets of different types of milk: "'low-fat milk", "chocolate milk", "dry milk", etc. thus, the contribution of this demonstration system is three-fold: the link to the demo is as follows: https://taxgen.ltdemos.informatik. uni-hamburg.de . the code link: https://github.com/ skoltech-nlp/taxgen-demo . link to the screencast video demonstrating the system: https://youtu.be/gf2avlnwgag . ## related work in this section we review the existing approaches for taxonomy enrichment as well as the existing tools for taxonomy visualization. ## candidate-free taxonomy enrichment this section presents candidate-free taxonomy enrichment -the problem of new word prediction in order to enhance the existing taxonomy. we briefly describe the cross-modal contextualized hidden state projection method (chsp) used in the demo and the results obtained on the dataset based on wordnet-3.0. ## system design taxfree is designed to help lexicographers in their work on updating taxonomies and inspecting them for the required modifications. in the current section we discuss each part of the tool and its usage in detail. ## conlusion the growing popularity of implementing taxonomies in different research and industry tasks has created the need for a platform for visualization of tree-like taxonomic subgraphs (query node in context). taxfree provides such a platform for the visualisation and analysis of hypo-hypernymy subgraphs. the tool allows users to explore wordnet synsets in context and predict new synsets for the leaf nodes. our work aims to bring taxonomies to a broader audience, by making wordnet interface user-friendly in comparison to the standard wordnet foot_5 visualization.