Multimodal Entity Linking for Tweets

Adjali, Omar; Besançon, Romaric; Ferret, Olivier; Borgne, Hervé Le; Grau, Brigitte

doi:10.1007/978-3-030-45439-5_31

Cited by 38 publications

(55 citation statements)

References 47 publications

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“…Linking identities across social media platforms is beyond the scope of this work, but the interested reader is referred toAdjali et al (2020) for a recent contribution to the subject.…”

mentioning

confidence: 99%

Amplifying influence through coordinated behaviour in social networks

Weber

Neumann

2021

Soc. Netw. Anal. Min.

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“…Linking identities across social media platforms is beyond the scope of this work, but the interested reader is referred toAdjali et al (2020) for a recent contribution to the subject.…”

mentioning

confidence: 99%

Amplifying influence through coordinated behaviour in social networks

Weber

Neumann

2021

Soc. Netw. Anal. Min.

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“…e named entity disambiguation system DBpedia Spotlight [11] mainly relies on entity context similarity measures for disambiguation. Adjali et al [12] used entity semantic similarity, context similarity, and mention probability for entity disambiguation. Hoffart et al [13] fused features that mentioned probability, entity similarity, and similarity of candidate entities based on graph links and used linear models to fuse these features for entity disambiguation.…”

Section: Entity Features-based Entity Disambiguation Methodmentioning

confidence: 99%

A Knowledge Graph Entity Disambiguation Method Based on Entity‐Relationship Embedding and Graph Structure Embedding

Chen³

et al. 2021

Computational Intelligence and Neuroscience

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The purpose of knowledge graph entity disambiguation is to match the ambiguous entities to the corresponding entities in the knowledge graph. Current entity ambiguity elimination methods usually use the context information of the entity and its attributes to obtain the mention embedding vector, compare it with the candidate entity embedding vector for similarity, and perform entity matching through the similarity. The disadvantage of this type of method is that it ignores the structural characteristics of the knowledge graph where the entity is located, that is, the connection between the entity and the entity, and therefore cannot obtain the global semantic features of the entity. To improve the Precision and Recall of entity disambiguation problems, we propose the EDEGE (Entity Disambiguation based on Entity and Graph Embedding) method, which utilizes the semantic embedding vector of entity relationship and the embedding vector of subgraph structure feature. EDEGE first trains the semantic vector of the entity relationship, then trains the graph structure vector of the subgraph where the entity is located, and balances the weights of these two vectors through the entity similarity function. Finally, the balanced vector is input into the graph neural network, and the matching between the entities is output to achieve entity disambiguation. Extensive experimental results proved the effectiveness of the proposed method. Among them, on the ACE2004 data set, the Precision, Recall, and F1 values of EDEGE are 9.2%, 7%, and 11.2% higher than baseline methods.

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“…"-" in the column of Algorithms means the corresponding model uses a relatively simple algorithm to select the mapping entity, such as a linear combination of features. [42] pre-trained description, type -Deep-ED (EMNLP 2017) [43] pre-trained description, context MLP NeuPL (CIKM 2017) [44] learned description, context -Eshel et al (CoNLL 2017) [45] learned context MLP MR-Deep-ED (ACL 2018) [46] pre-trained description, context MLP Moon et al (ACL 2018) [47] pre-trained context - [51] pre-trained description, context MLP SGTB-BiBSG (NAACL 2018) [52] pre-trained description, context -NCEL (COLING 2018) [53] learned context MLP Le and Titov (ACL 2019) [54] pre-trained type MLP Le and Titov (ACL 2019) [55] pre-trained [57] description, context MLP RRWEL (IJCAI 2019) [58] learned surface form, description graph-based RLEL (WWW 2019) [59] pre-trained description, context RL DCA (EMNLP 2019) [60] pre-trained surface form, description, context MLP, RL Gillick et al (CoNLL 2019) [61] pre-trained description MLP E-ELMo (arXiv 2019) [62] learned context MLP FGS2EE (ACL 2020) [63] pre-trained description, context, type MLP ET4EL (AAAI 2020) [64] learned -Chen et al (AAAI 2020) [65] pre-trained description, context, type MLP REL (SIGIR 2020) [66] learned context MLP SeqGAT (WWW 2020) [67] description MLP DGCN (WWW 2020) [31] description, context, type MLP BLINK (EMNLP 2020) [68] description -ELQ (EMNLP 2020) [69] description -GNED (KBS 2020) [70] pre-trained description, context MLP JMEL (ECIR 2020) [71] learned MLP Yamada et al (arXiv 2020) [72] context -M3 (AAAI 2021) [73] -Bi-MPR (AAAI 2021) [74] description MLP Chen et al (AAAI 2021) [75] learned surface form MLP CHOLAN (EACL 2021) [76] -Zhang et al (DASFAA 2021)…”

Section: Word Embeddingmentioning

confidence: 99%

Entity Linking Meets Deep Learning: Techniques and Solutions

Shen¹,

Li²,

Liu³

et al. 2021

Preprint

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Entity linking (EL) is the process of linking entity mentions appearing in web text with their corresponding entities in a knowledge base. EL plays an important role in the fields of knowledge engineering and data mining, underlying a variety of downstream applications such as knowledge base population, content analysis, relation extraction, and question answering. In recent years, deep learning (DL), which has achieved tremendous success in various domains, has also been leveraged in EL methods to surpass traditional machine learning based methods and yield the state-of-the-art performance. In this survey, we present a comprehensive review and analysis of existing DL based EL methods. First of all, we propose a new taxonomy, which organizes existing DL based EL methods using three axes: embedding, feature, and algorithm. Then we systematically survey the representative EL methods along the three axes of the taxonomy. Later, we introduce ten commonly used EL data sets and give a quantitative performance analysis of DL based EL methods over these data sets. Finally, we discuss the remaining limitations of existing methods and highlight some promising future directions.

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Multimodal Entity Linking for Tweets

Cited by 38 publications

References 47 publications

Amplifying influence through coordinated behaviour in social networks

Amplifying influence through coordinated behaviour in social networks

A Knowledge Graph Entity Disambiguation Method Based on Entity‐Relationship Embedding and Graph Structure Embedding

Entity Linking Meets Deep Learning: Techniques and Solutions

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