Structured Learning for Taxonomy Induction with Belief Propagation

Bansal, Mohit; Burkett, David; Melo, Gerard de; Klein, Dan

doi:10.3115/v1/p14-1098

Cited by 44 publications

(99 citation statements)

References 31 publications

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“…in Eqs. (3)(4)(5) and adjust the dimensionality of W (k −1) accordingly. Such position embeddings help us to learn better node representations from two aspects.…”

Section: Representing Anchor Conceptmentioning

confidence: 99%

TaxoExpan: Self-supervised Taxonomy Expansion with Position-Enhanced Graph Neural Network

Shen

Xiong

et al. 2020

Proceedings of the Web Conference 2020

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Taxonomies consist of machine-interpretable semantics and provide valuable knowledge for many web applications. For example, online retailers (e.g., Amazon and eBay) use taxonomies for product recommendation, and web search engines (e.g., Google and Bing) leverage taxonomies to enhance query understanding. Enormous efforts have been made on constructing taxonomies either manually or semi-automatically. However, with the fast-growing volume of web content, existing taxonomies will become outdated and fail to capture emerging knowledge. Therefore, in many applications, dynamic expansions of an existing taxonomy are in great demand. In this paper, we study how to expand an existing taxonomy by adding a set of new concepts. 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. Using such self-supervision data, TaxoExpan learns a model to predict whether a query concept is the direct hyponym of an anchor concept. 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. Extensive experiments on three large-scale datasets from different domains demonstrate both the effectiveness and the efficiency of TaxoExpan for taxonomy expansion.

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“…in Eqs. (3)(4)(5) and adjust the dimensionality of W (k −1) accordingly. Such position embeddings help us to learn better node representations from two aspects.…”

Section: Representing Anchor Conceptmentioning

confidence: 99%

TaxoExpan: Self-supervised Taxonomy Expansion with Position-Enhanced Graph Neural Network

Shen

Xiong

et al. 2020

Proceedings of the Web Conference 2020

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“…Navigli et al [25] and Velardi et al [42] use the same longest-path idea to weigh edges and then find the largestweight taxonomy as a Maximum Spanning Tree. Bansal et al [3] build a factor graph to model hypernymy relations and regard taxonomy induction as a structured learning problem, which can be inferred with loop belief propagation. Recently, Gupta et al [9] propose to build the initial graph using hypernym subsequence (instead of single hypernym pair) and model taxonomy induction as a minimum-cost flow problem [26].…”

Section: Taxonomy Constructionmentioning

confidence: 99%

HiExpan

Shen

Lei

et al. 2018

Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery &Amp; Data Mining

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Taxonomies are of great value to many knowledge-rich applications. As the manual taxonomy curation costs enormous human effects, automatic taxonomy construction is in great demand. However, most existing automatic taxonomy construction methods can only build hypernymy taxonomies wherein each edge is limited to expressing the "is-a" relation. Such a restriction limits their applicability to more diverse real-world tasks where the parent-child may carry different relations. In this paper, we aim to construct a task-guided taxonomy from a domain-specific corpus, and allow users to input a "seed" taxonomy, serving as the task guidance. We propose an expansion-based taxonomy construction framework, namely HiExpan, which automatically generates key term list from the corpus and iteratively grows the seed taxonomy. Specifically, HiExpan views all children under each taxonomy node forming a coherent set and builds the taxonomy by recursively expanding all these sets. Furthermore, HiExpan incorporates a weakly-supervised relation extraction module to extract the initial children of a newlyexpanded node and adjusts the taxonomy tree by optimizing its global structure. Our experiments on three real datasets from different domains demonstrate the effectiveness of HiExpan for building task-guided taxonomies.

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“…A similar approach formulated in terms of factor graphs can be seen in (Bansal et al, 2013). Finally, (Yamada et al, 2011) employ a hybrid strategy, scoring edges by likelihood of appearance in Wikipedia.…”

Section: Related Workmentioning

confidence: 99%

MSejrKu at SemEval-2016 Task 14: Taxonomy Enrichment by Evidence Ranking

Schlichtkrull

Alonso²

2016

Proceedings of the 10th International Workshop on Semantic Evaluation (SemEval-2016)

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Automatic enrichment of semantic taxonomies with novel data is a relatively unexplored task with potential benefits in a broad array of natural language processing problems. Task 14 of SemEval 2016 poses the challenge of designing systems for this task. In this paper, we describe and evaluate several machine learning systems constructed for our participation in the competition. We demonstrate an f1-score of 0.680 for our submitted systems -a small improvement over the 0.679 produced by the hard baseline.

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Structured Learning for Taxonomy Induction with Belief Propagation

Cited by 44 publications

References 31 publications

TaxoExpan: Self-supervised Taxonomy Expansion with Position-Enhanced Graph Neural Network

TaxoExpan: Self-supervised Taxonomy Expansion with Position-Enhanced Graph Neural Network

HiExpan

MSejrKu at SemEval-2016 Task 14: Taxonomy Enrichment by Evidence Ranking

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