Boosting Collective Entity Linking via Type-Guided Semantic Embedding

Lü, Weiming; Zhou, Yangfan; Lu, Haijiao; Ma, Pengkun; Zhang, Zhenyu; Wei, Baogang

doi:10.1007/978-3-319-73618-1_45

Cited by 2 publications

(3 citation statements)

References 18 publications

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“…In these equations, IN e i is the set of incoming hyperlinks in the Wikipedia page for e i , and r is a relationship between e i and e j in a broad sense (a hyperlink for the Wikipedia KB or a relation for an RDF KB). For example, WLM is used in [16,23,28], Ref in [1,23], cosine similarity between entities in [24,25,29,42], while a combination of the three is used in [32]. Beyond the entity relatedness measure, collective linking techniques also differ on their linking strategy, either relying on the direct optimization of Eq.…”

Section: Collective Entity Linkingmentioning

confidence: 99%

“…Beyond the entity relatedness measure, collective linking techniques also differ on their linking strategy, either relying on the direct optimization of Eq. 3 [21,23,24,29,42] or on a mention-entity graph [1,6,13,16,22,25,28,32,41] such as the one illustrated in Fig. 1.…”

Section: Collective Entity Linkingmentioning

confidence: 99%

“…Mention-entity graph-based techniques build a graph whose nodes are entity mentions and candidate entities, and whose edges connect either some mention to one of its candidate entities or candidate entities of different mentions. Edges are weighted, in the first case with the local score, in the last case with the global score [16,25]. A slightly different graph is used in [1]: nodes denote (mention, candidate entity) pairs and edges connect nodes with similar candidate entities, weighted with a global score.…”

Section: Collective Entity Linkingmentioning

confidence: 99%

See 2 more Smart Citations

Using Knowledge Base Semantics in Context-Aware Entity Linking

Vaigh

Goasdoué

Gravier

et al. 2019

Proceedings of the ACM Symposium on Document Engineering 2019

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Entity linking is a core task in textual document processing, which consists in identifying the entities of a knowledge base (KB) that are mentioned in a text. Approaches in the literature consider either independent linking of individual mentions or collective linking of all mentions. Regardless of this distinction, most approaches rely on the Wikipedia encyclopedic KB in order to improve the linking quality, by exploiting its entity descriptions (web pages) or its entity interconnections (hyperlink graph of web pages). In this paper, we devise a novel collective linking technique which departs from most approaches in the literature by relying on a structured RDF KB. This allows exploiting the semantics of the interrelationships that candidate entities may have at disambiguation time rather than relying on raw structural approximation based on Wikipedia's hyperlink graph. The few approaches that also use an RDF KB simply rely on the existence of a relation between the candidate entities to which mentions may be linked. Instead, we weight such relations based on the RDF KB structure and propose an efficient decoding strategy for collective linking. Experiments on standard benchmarks show significant improvement over the state of the art.

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Section: Collective Entity Linkingmentioning

confidence: 99%

Section: Collective Entity Linkingmentioning

confidence: 99%

Section: Collective Entity Linkingmentioning

confidence: 99%

See 1 more Smart Citation

Using Knowledge Base Semantics in Context-Aware Entity Linking

Vaigh

Goasdoué

Gravier

et al. 2019

Proceedings of the ACM Symposium on Document Engineering 2019

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RSVN: A RoBERTa Sentence Vector Normalization Scheme for Short Texts to Extract Semantic Information

Gao

Zhang

et al. 2022

Applied Sciences

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With the explosive growth in short texts on the Web and an increasing number of Web corpora consisting of short texts, short texts are playing an important role in various Web applications. Entity linking is a crucial task in knowledge graphs and a key technology in the field of short texts that affects the accuracy of many downstream tasks in natural language processing. However, compared to long texts, the entity-linking task of Chinese short text is a challenging problem due to the serious colloquialism and insufficient contexts. Moreover, existing methods for entity linking in Chinese short text underutilize semantic information and ignore the interaction between label information and the original short text. In this paper, we propose a RoBERTa sentence vector normalization scheme for short texts to fully extract the semantic information. Firstly, the proposed model utilizes RoBERTa to fully capture contextual semantic information. Secondly, the anisotropy of RoBERTa’s output sentence vectors is revised by utilizing the standard Gaussian of flow model, which enables the sentence vectors to more precisely characterize the semantics. In addition, the interaction between label embedding and text embedding is employed to improve the NIL entity classification. Experimental results demonstrate that the proposed model outperforms existing research results and mainstream deep learning methods for entity linking in two Chinese short text datasets.

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Boosting Collective Entity Linking via Type-Guided Semantic Embedding

Cited by 2 publications

References 18 publications

Using Knowledge Base Semantics in Context-Aware Entity Linking

Using Knowledge Base Semantics in Context-Aware Entity Linking

RSVN: A RoBERTa Sentence Vector Normalization Scheme for Short Texts to Extract Semantic Information

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