Using Distributional Semantics for Automatic Taxonomy Induction

Zafar, Bushra; Cochez, Michael; Qamar, Usman

doi:10.1109/fit.2016.070

Cited by 7 publications

(4 citation statements)

References 28 publications

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“…Many research efforts have been devoted to the construction of taxonomies and folksonomies. Zafar, Cochez, and Qamar proposed a taxonomy induction system which was based on a word embedding trained from a large corpus [15]. Their research was based on the word2vec model and was focused on hyponym-and-hypernym identification.…”

Section: Literature Reviewmentioning

confidence: 99%

A semi-hierarchical clustering method for constructing knowledge trees from stackoverflow

Tseng

Lin

2020

Journal of Information Science

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To help students learn how to programme, we have to give them a clear knowledge map and sufficient materials. Question-based websites, such as stackoverflow, are excellent information sources for this goal. However, for beginners, the process can be a little tricky since they may not know how to ask correct questions if they do not have sufficient background knowledge, and a knowledge tree is usually considered more helpful in such a scenario. In this research, a method to infer a knowledge tree automatically from the type of websites and to group documents based on the resulting knowledge tree is proposed. The proposed method mainly addresses two issues: first, the quality of tags cannot be guaranteed, and second, clustering-based methods usually generate the flat schema. The occurrence count and the co-occurrence ratio were used together to identify important tags. Then, an algorithm was developed to infer the hierarchical relationship between tags. Using these tags as centres, the clustering performance is better than applying k-means alone.

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Section: Literature Reviewmentioning

confidence: 99%

A semi-hierarchical clustering method for constructing knowledge trees from stackoverflow

Tseng

Lin

2020

Journal of Information Science

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“…Word similarity methods usually represent each word as a vector and calculate the similarity between two words. In the method proposed by Zafar et al , each word is represented as a vector by word2vec model (Zafar et al , 2016). But this method requires large-corpus.…”

Section: Related Workmentioning

confidence: 99%

Using multiple Web resources and inference rules to classify Chinese word semantic relation

Zhang

2018

IDD

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Purpose The purpose of this paper is to classify Chinese word semantic relations, which are synonyms, antonyms, hyponyms and meronymys. Design/methodology/approach Basically, four simple methods are applied, ontology-based, dictionary-based, pattern-based and morpho-syntactic method. The authors make good use of search engine to build lexical and semantic resources for dictionary-based and pattern-based methods. To improve classification performance with more external resources, they also classify the given word pairs in Chinese and in English at the same time by using machine translation. Findings Experimental results show that the approach achieved an average F1 score of 50.87 per cent, an average accuracy of 70.36 per cent and an average recall of 40.05 per cent over all classification tasks. Synonym and antonym classification achieved high accuracy, i.e. above 90 per cent. Moreover, dictionary-based and pattern-based approaches work effectively on final data set. Originality/value For many natural language processing (NLP) tasks, the step of distinguishing word semantic relation can help to improve system performance, such as information extraction and knowledge graph generation. Currently, common methods for this task rely on large corpora for training or dictionaries and thesauri for inference, where limitation lies in freely data access and keeping built lexical resources up-date. This paper builds a primary system for classifying Chinese word semantic relations by seeking new ways to obtain the external resources efficiently.

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“…Recently, word embeddings representations are involved in the task of knowledge hierarchical organization [6], [30], [8]. However, these methods are only considering hypernymhyponym relationship extraction between lexical terms, using word embeddings.…”

Section: Related Workmentioning

confidence: 99%

“…In the final step, the algorithm computes the transitive closure over all the previously extracted axioms (lines [25][26][27][28][29][30][31][32][33][34][35][36][37][38][39][40][41][42][43][44]. We also make sure that there are no cycles in the final class hierarchy (line 34).…”

Section: Approachmentioning

confidence: 99%

Large-scale taxonomy induction using entity and word embeddings

Ristoski

Faralli

Ponzetto

et al. 2017

Proceedings of the International Conference on Web Intelligence

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Taxonomies are an important ingredient of knowledge organization, and serve as a backbone for more sophisticated knowledge representations in intelligent systems, such as formal ontologies. However, building taxonomies manually is a costly endeavor, and hence, automatic methods for taxonomy induction are a good alternative to build large-scale taxonomies. In this paper, we propose TIEmb, an approach for automatic unsupervised class subsumption axiom extraction from knowledge bases using entity and text embeddings. We apply the approach on the WebIsA database, a database of subsumption relations extracted from the large portion of the World Wide Web, to extract class hierarchies in the Person and Place domain.

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Using Distributional Semantics for Automatic Taxonomy Induction

Cited by 7 publications

References 28 publications

A semi-hierarchical clustering method for constructing knowledge trees from stackoverflow

A semi-hierarchical clustering method for constructing knowledge trees from stackoverflow

Using multiple Web resources and inference rules to classify Chinese word semantic relation

Large-scale taxonomy induction using entity and word embeddings

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