GANTEE: Generative Adversatial Network for Taxonomy Entering Evaluation

• URL: http://arxiv.org/abs/2303.14480v1
• Date: Sat, 25 Mar 2023 14:24:50 GMT
• Title: GANTEE: Generative Adversatial Network for Taxonomy Entering Evaluation
• Authors: Zhouhong Gu, Sihang Jiang, Jingping Liu, Yanghua Xiao, Hongwei Feng, Zhixu Li, Jiaqing Liang, Jian Zhong
• Abstract summary: The traditional taxonomy expansion task aims at finding the best position for new coming concepts in the existing taxonomy. The previous methods suffer from low-efficiency since they waste much time when most of the new coming concepts are indeed noisy concepts. This paper proposes a pluggable framework called Generative Adversarial Network for Taxonomy Entering Evaluation (GANTEE) to alleviate these drawbacks.
• Score: 19.036529022923194
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Taxonomy is formulated as directed acyclic concepts graphs or trees that support many downstream tasks. Many new coming concepts need to be added to an existing taxonomy. The traditional taxonomy expansion task aims only at finding the best position for new coming concepts in the existing taxonomy. However, they have two drawbacks when being applied to the real-scenarios. The previous methods suffer from low-efficiency since they waste much time when most of the new coming concepts are indeed noisy concepts. They also suffer from low-effectiveness since they collect training samples only from the existing taxonomy, which limits the ability of the model to mine more hypernym-hyponym relationships among real concepts. This paper proposes a pluggable framework called Generative Adversarial Network for Taxonomy Entering Evaluation (GANTEE) to alleviate these drawbacks. A generative adversarial network is designed in this framework by discriminative models to alleviate the first drawback and the generative model to alleviate the second drawback. Two discriminators are used in GANTEE to provide long-term and short-term rewards, respectively. Moreover, to further improve the efficiency, pre-trained language models are used to retrieve the representation of the concepts quickly. The experiments on three real-world large-scale datasets with two different languages show that GANTEE improves the performance of the existing taxonomy expansion methods in both effectiveness and efficiency.

Related papers

• CodeTaxo: Enhancing Taxonomy Expansion with Limited Examples via Code Language Prompts [40.52605902842168]
  textscCodeTaxo is a novel approach that leverages large language models through code language prompts to capture the taxonomic structure. Experiments on five real-world benchmarks from different domains demonstrate that textscCodeTaxo consistently achieves superior performance across all evaluation metrics.
  arXiv  Detail & Related papers  (2024-08-17T02:15:07Z)
• Find Parent then Label Children: A Two-stage Taxonomy Completion Method with Pre-trained Language Model [46.00652942385366]
  We propose a two-stage method called ATTEMPT for taxonomy completion. Our method inserts new concepts into the correct position by finding a parent node and labeling child nodes. We take advantage of pre-trained language models for hypernym/hyponymy recognition.
  arXiv  Detail & Related papers  (2024-06-25T17:25:02Z)
• Insert or Attach: Taxonomy Completion via Box Embedding [75.69894194912595]
  Previous approaches embed concepts as vectors in Euclidean space, which makes it difficult to model asymmetric relations in taxonomy. We develop a framework, TaxBox, that leverages box containment and center closeness to design two specialized geometric scorers within the box embedding space. These scorers are tailored for insertion and attachment operations and can effectively capture intrinsic relationships between concepts.
  arXiv  Detail & Related papers  (2023-05-18T14:34:58Z)
• Who Should Go First? A Self-Supervised Concept Sorting Model for Improving Taxonomy Expansion [50.794640012673064]
  As data and business scope grow in real applications, existing need to be expanded to incorporate new concepts. Previous works on taxonomy expansion process the new concepts independently and simultaneously, ignoring the potential relationships among them and the appropriate order of inserting operations. We propose TaxoOrder, a novel self-supervised framework that simultaneously discovers the local hypernym-hyponym structure among new concepts and decides the order of insertion.
  arXiv  Detail & Related papers  (2021-04-08T11:00:43Z)
• Taxonomy Completion via Triplet Matching Network [18.37146040410778]
  We formulate a new task, "taxonomy completion", by discovering both the hypernym and hyponym concepts for a query. We propose Triplet Matching Network (TMN), to find the appropriate hypernym, hyponym> pairs for a given query concept. TMN achieves the best performance on both taxonomy completion task and the previous taxonomy expansion task, outperforming existing methods.
  arXiv  Detail & Related papers  (2021-01-06T07:19:55Z)
• Octet: Online Catalog Taxonomy Enrichment with Self-Supervision [67.26804972901952]
  We present a self-supervised end-to-end framework, Octet for Online Catalog EnrichmenT. We propose to train a sequence labeling model for term extraction and employ graph neural networks (GNNs) to capture the taxonomy structure. Octet enriches an online catalog in production to 2 times larger in the open-world evaluation.
  arXiv  Detail & Related papers  (2020-06-18T04:53:07Z)
• STEAM: Self-Supervised Taxonomy Expansion with Mini-Paths [53.45704816829921]
  We propose a self-supervised taxonomy expansion model named STEAM. STEAM generates natural self-supervision signals, and formulates a node attachment prediction task. Experiments show STEAM outperforms state-of-the-art methods for taxonomy expansion by 11.6% in accuracy and 7.0% in mean reciprocal rank.
  arXiv  Detail & Related papers  (2020-06-18T00:32:53Z)
• TaxoExpan: Self-supervised Taxonomy Expansion with Position-Enhanced Graph Neural Network [62.12557274257303]
  Taxonomies consist of machine-interpretable semantics and provide valuable knowledge for many web applications. We propose a novel self-supervised framework, named TaxoExpan, which automatically generates a set of query concept, anchor concept> pairs from the existing taxonomy as training data. We develop two innovative techniques in TaxoExpan: (1) a position-enhanced graph neural network that encodes the local structure of an anchor concept in the existing taxonomy, and (2) a noise-robust training objective that enables the learned model to be insensitive to the label noise in the self-supervision data.
  arXiv  Detail & Related papers  (2020-01-26T21:30:21Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.