Linked Hypernyms: Enriching DBpedia with Targeted Hypernym Discovery

Kliegr, Tomáš

doi:10.2139/ssrn.3199181

Cited by 6 publications

(15 citation statements)

References 13 publications

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“… POS tags and rules. A couple of authors use part of speech (POS) taggers and/or several rules in order to identify named entities [19][20][21][22][23][24][25][26][27][28][29][30][31][32][33][34]. The rules range from simple rules such as "capitalized letter" (if a word contains a capitalized letter the word will be treated as a spot), stop word lists, "At Least One Noun Selector"-rule to complex, combined rules.…”

Section: State Of the Art In Entity Detection (Spotting)mentioning

confidence: 99%

“…Some approaches expand the surface forms by searching the textual context based on heuristic pattern matching [46,47,50]. Other authors use N-Grams [31,33,44,51,52], others experiment with CRF [25,26], Topic Modeling [53,54], Naïve Bayes and Hidden Markov Models [55]. Last but not least one can find approaches based on Finite-state machines [14,15,30,34].…”

Section: State Of the Art In Entity Detection (Spotting)mentioning

confidence: 99%

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Improving Language-Dependent Named Entity Detection

Petz

Wetzlinger

Nedbal

2017

Lecture Notes in Computer Science

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Named Entity Recognition (NER) and Named Entity Linking (NEL) are two research areas that have shown big advancements in recent years. The majority of this research is based on the English language. Hence, some of these improvements are language-dependent and do not necessarily lead to better results when applied to other languages. Therefore, this paper discusses TOMO, an approach to language-aware named entity detection and evaluates it for the German language. This also required the development of a German gold standard dataset, which was based on the English dataset used by the OKE 2016 challenge. An evaluation of the named entity detection task using the web-based platform GERBIL was undertaken and results show that our approach produced higher F1 values than the other annotators did. This indicates that language-dependent features do improve the overall quality of the spotter.

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Section: State Of the Art In Entity Detection (Spotting)mentioning

confidence: 99%

Section: State Of the Art In Entity Detection (Spotting)mentioning

confidence: 99%

Improving Language-Dependent Named Entity Detection

Petz

Wetzlinger

Nedbal

2017

Lecture Notes in Computer Science

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“…The accuracy of LHD matches the results for Tipalo algorithm -as reported by its authors in [7] -for the type selection subtask (0.93 precision and 0.90 recall). A detailed comparison between LHD and Tipalo is presented in [2] as well as a more extensive literature review on pattern-based extraction.…”

Section: Pattern-based Analysis Of Unstructured Contentmentioning

confidence: 99%

“…Parts of the work presented in this article have been published within the conference paper "Towards Linked Hypernyms Dataset 2.0: complementing DBpedia with hypernym discovery and statistical type inference (Kliegr and Zamazal, 2014)" [5]. This article extends the conference paper by introducing the hierarchical SVM approach and by performing extensive evaluation on the contributed gold standard dataset allowing the community to track progress in accuracy and coverage of entity typing and extraction tools.…”

Section: Introductionmentioning

confidence: 98%

“…This combined approach can complete types for about 70% of Wikipedia articles without a type in DBpedia. Our previously published Linked Hypernyms Dataset (LHD) framework [2] extracts types from the first sentence of Wikipedia articles using lexicosyntactic patterns. In this work we extend it with Statistical Type Inference (STI) which helps to map LHD results to the DBpedia Ontology used by the native DBpedia solution.…”

Section: Introductionmentioning

confidence: 99%

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LHD 2.0: A Text Mining Approach to Typing Entities in Knowledge Graphs

Kliegr

Zamazal

2016

SSRN Journal

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The type of the entity being described is one of the key pieces of information in linked data knowledge graphs. In this article, we introduce a novel technique for type inference that extracts types from the free text description of the entity combining lexico-syntactic pattern analysis with supervised classification. For lexicosyntactic (Hearst) pattern-based extraction we use our previously published Linked Hypernyms Dataset Framework. Its output is mapped to the DBpedia Ontology with exact string matching complemented with a novel co-occurrence-based algorithm STI. This algorithm maps classes appearing in one knowledge graph to a different set of classes appearing in another knowledge graph provided that the two graphs contain common set of typed instances. The supervised results are obtained from a hierarchy of Support Vector Machines classifiers (hSVM) trained on the bag-of-words representation of short abstracts and categories of Wikipedia articles. The results of both approaches are probabilistically fused. For evaluation we created a gold-standard dataset covering over 2,000 DBpedia entities using a commercial crowdsourcing service. The hierarchical precision of our hSVM and STI approaches is comparable to SDType, the current state-of-theart type inference algorithm, while the set of applicable instances is largely complementary to SDType as our algorithms do not require semantic properties in the knowledge graph to type an instance. The paper also provides a comprehensive evaluation of type assignment in DBpedia in terms of hierarchical precision, recall and exact match with the gold standard. Dataset generated by a version of the presented approach is included in DBpedia 2015.

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Hypernym Extraction: Combining Machine-Learning and Dependency Grammar

Espinosa-Anke

Ronzano

Saggion

2015

Lecture Notes in Computer Science

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Hypernym extraction is a crucial task for semantically motivated NLP tasks such as taxonomy and ontology learning, textual entailment or paraphrase identification. In this paper, we describe an approach to hypernym extraction from textual definitions, where machine-learning and post-classification refinement rules are combined. Our best-performing configuration shows competitive results compared to state-of-the-art systems in a well-known benchmarking dataset. The quality of our features is measured by combining them in different feature sets and by ranking them by their Information Gain score. Our experiments confirm that both syntactic and definitional information play a crucial role in the hypernym extraction task.

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Linked Hypernyms: Enriching DBpedia with Targeted Hypernym Discovery

Cited by 6 publications

References 13 publications

Improving Language-Dependent Named Entity Detection

Improving Language-Dependent Named Entity Detection

LHD 2.0: A Text Mining Approach to Typing Entities in Knowledge Graphs

Hypernym Extraction: Combining Machine-Learning and Dependency Grammar

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