Automatic keyphrase extraction and ontology mining for content-based tag recommendation

Pudota, Nirmala; Dattolo, Antonina; Baruzzo, Andrea; Ferrara, Felice; Tasso, Carlo

doi:10.1002/int.20448

Cited by 44 publications

(31 citation statements)

References 32 publications

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“…This is the reason why our English model performs better on our collections than on the SE-MEVAL 2010 dataset, the reason why the Arabic model performs better on the AKEC collection than on the other datasets, and a probable reason of the poor performance of the unsupervised weights, since Pudota et al (2010) tailored them on documents with a different length.…”

Section: Resultsmentioning

confidence: 95%

Exploiting and Evaluating a Supervised, Multilanguage Keyphrase Extraction Pipeline for Under-Resourced Languages

Basaldella¹,

Helmy²,

Antolli³

et al. 2017

RANLP 2017 - Recent Advances in Natural Language Processing Meet Deep Learning

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This paper evaluates different techniques for building a supervised, multilanguage keyphrase extraction pipeline for languages which lack a gold standard. Starting from an unsupervised English keyphrase extraction pipeline, we implement pipelines for Arabic, Italian, Portuguese, and Romanian, and we build test collections for languages which lack one. Then, we add a Machine Learning module trained on a well-known English language corpus and we evaluate the performance not only over English but on the other languages as well. Finally, we repeat the same evaluation after training the pipeline over an Arabic language corpus to check whether using a language-specific corpus brings a further improvement in performance. On the five languages we analyzed, results show an improvement in performance when using a machine learning algorithm, even if such algorithm is not trained and tested on the same language.

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Section: Resultsmentioning

confidence: 95%

Exploiting and Evaluating a Supervised, Multilanguage Keyphrase Extraction Pipeline for Under-Resourced Languages

Basaldella¹,

Helmy²,

Antolli³

et al. 2017

RANLP 2017 - Recent Advances in Natural Language Processing Meet Deep Learning

Self Cite

View full text Add to dashboard Cite

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“…Recently, Pudota et al [61] pro posed a recommendation system that generate and recommend tags automatically for web resources. The web documents are annotated and matched by terms in the ontology first.…”

Section: E Ontology-based Recommendation Systemmentioning

confidence: 99%

“…Peiformance gain in precision, recall, and consistency of data mining results Many previous ontology-based efforts have reported per formance gain in the data mining results. Ontology-based approaches have been reported to have better precision and recall than the traditional approaches in various tasks such as text clustering [32], [33], [35], [65], [82], infonnation extraction [17], [27], [56], [57], link prediction [6], [15], [74], and recOlmnendation systems [33], [52], [60], [61]. Research in recommendation system suggests that ontology based recommendation systems have better prediction preci sion than traditional recommendation methods [13], [83].…”

Section: Perf or Mance Evaluation And App Licationsmentioning

confidence: 99%

Semantic data mining: A survey of ontology-based approaches

Dou

Wang

Liu

2015

Proceedings of the 2015 IEEE 9th International Conference on Semantic Computing (IEEE ICSC 2015)

145

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Semantic Data Mining refers to the data mining tasks that systematically incorporate domain knowledge, especially for mal semantics, into the process. In the past, many research efforts have attested the benefits of incorporating domain knowledge in data mining. At the same time, the proliferation of knowledge engineering has enriched the family of domain knowledge, espe cially formal semantics and Semantic Web ontologies. Ontology is an explicit specification of conceptualization and a formal way to define the semantics of knowledge and data. The formal structure of ontology makes it a nature way to encode domain knowledgefor the data mining use. In this survey paper, we introduce general concepts of semantic data mining. We investigate why ontology has the potential to help semantic data mining and how formal semantics in ontologies can be incorporated into the data mining process. We provide detail discussions for the advances and state of art of ontology-based approaches and an introduction of approaches that are based on other form of

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“…The first one deals with word (words sequences) ranking, selection of top-ranked units and phrase construction [1][2][3]. The second one is the most widely used and spans keyphrases construction from candidates, candidate ranking and selection of the best keyphrases or classification of candidates [4][5][6][7][8][9][10][11]. Candidate phrase building often uses n-grams, word sequences that satisfy a number of constraints, e.g.…”

Section: Introduction and Related Workmentioning

confidence: 99%

Keyphrase Extraction Abstracts Instead of Full Papers

Popova

Danilova

2014

2014 25th International Workshop on Database and Expert Systems Applications

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In the present paper 1 we consider keyphrase extraction problem from scientific articles. Finding an appropriate solution is important for the organization of fast navigation in databases, indexing, clustering and classification of academic papers. The base collection includes keyphrases selected by the experts for each text (SemEval2010). It is shown that the use of abstracts instead of full texts allows to improve the results obtained by processing full texts or abstracts with introduction and conclusion section. Our approach uses the extraction of keyphrases with linguistic patterns (part of speechbased); patterns are built on the basis of an auxiliary dataset. The use of abstracts in this approach allows to reduce the number of words sequences extracted with patterns, as compared to the use of full texts. It allows to simplify or totally omit the ranking stage. Ranking is usually needed, because out of many keyphrases candidates we have to choose only 10-15. This stage is the most difficult and its effectiveness depends on the number of the selected candidates to keyphrases. The use of abstracts makes it possible to considerably reduce the number of candidate phrases and at the same time yields high recall.

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Automatic keyphrase extraction and ontology mining for content-based tag recommendation

Cited by 44 publications

References 32 publications

Exploiting and Evaluating a Supervised, Multilanguage Keyphrase Extraction Pipeline for Under-Resourced Languages

Exploiting and Evaluating a Supervised, Multilanguage Keyphrase Extraction Pipeline for Under-Resourced Languages

Semantic data mining: A survey of ontology-based approaches

Keyphrase Extraction Abstracts Instead of Full Papers

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