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Multi-modal Graph Fusion for Named Entity Recognition with Targeted Visual Guidance
AAAI Conference on Artificial Intelligence, 2021 Multi-modal named entity recognition (MNER) aims to discover named entities in free text and classify them into pre-defined types with images. However, dominant MNER models do not fully exploit fine-grained semantic correspondences between semantic units
Dong Zhang +5 more
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Name Disambiguation Based on Graph Convolutional Network [PDF]
Scientific Programming, 2021 Recently, massive online academic resources have provided convenience for scientific study and research. However, the author name ambiguity degrades the user experience in retrieving the literature bases. Extracting the features of papers and calculating the similarity for clustering constitute the mainstream of present name disambiguation approaches ...
Ya Chen +3 more
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Graph Ranking for Collective Named Entity Disambiguation [PDF]
Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), 2014 Named Entity Disambiguation (NED) refers to the task of mapping different named entity mentions in running text to their correct interpretations in a specific knowledge base (KB). This paper presents a collective disambiguation approach using a graph model.
Ayman Alhelbawy, Robert Gaizauskas
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Named entity resolution in personal knowledge graphs [PDF]
, 2023 Entity Resolution (ER) is the problem of determining when two entities refer to the same underlying entity. The problem has been studied for over 50 years, and most recently, has taken on new importance in an era of large, heterogeneous 'knowledge graphs' published on the Web and used widely in domains as wide ranging as social media, e-commerce and ...
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Named Graphs for Semantic Representation [PDF]
Proceedings of the Seventh Joint Conference on Lexical and
Computational Semantics, 2018 A position paper arguing that purely graphical representations for natural language semantics lack a fundamental degree of expressiveness, and cannot deal with even basic Boolean operations like negation or disjunction. Moving from graphs to named graphs leads to representations that stand some chance of having sufficient expressive power.
Richard Crouch, Aikaterini-Lida Kalouli
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Modelling Calculi with Name Mobility using Graphs with Equivalences [PDF]
Electronic Notes in Theoretical Computer Science, 2007 AbstractIn the theory of graph rewriting, the use of coalescing rules, i.e., of rules which besides deleting and generating graph items, can coalesce some parts of the graph, turns out to be quite useful for modelling purposes, but, at the same time, problematic for the development of a satisfactory partial order concurrent semantics for rewrites ...
Paolo Baldan +2 more
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Deep Learning-Based Named Entity Recognition and Knowledge Graph Construction for Geological Hazards
ISPRS International Journal of Geo-Information, 2019 Constructing a knowledge graph of geological hazards literature can facilitate the reuse of geological hazards literature and provide a reference for geological hazard governance.
Runyu Fan +5 more
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The naming game on the complete graph
Electronic Journal of Probability, 2018 We consider a model of language development, known as the naming game, in which agents invent, share and then select descriptive words for a single object, in such a way as to promote local consensus. When formulated on a finite and connected graph, a global consensus eventually emerges in which all agents use a common unique word.
Eric Foxall
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Few-Shot Named Entity Recognition Based on the Collaborative Graph Attention Network
IEEE AccessFew-shot Named Entity Recognition (NER) aims to extract entity information from limited annotated samples, addressing the scarcity of data in specialized domains.
Haoran Niu, Zhaoman Zhong
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What can knowledge graph do for few-shot named entity recognition
Web SemanticsDue to its extensive applicability in various downstream domains, few-shot named entity recognition (NER) has attracted increasing attention, particularly in areas where acquiring sufficient labeled data poses a significant challenge. Recent studies have
Binling Nie, Yiming Shao, Yigang Wang
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