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Similarity Calculation via Passage-Level Event Connection Graph

Version 2 2024-06-02, 14:48
Version 1 2023-02-13, 02:41
journal contribution
posted on 2024-06-02, 14:48 authored by M Liu, L Chen, Z Zheng
Recently, many information processing applications appear on the web on the demand of user requirement. Since text is one of the most popular data formats across the web, how to measure text similarity becomes the key challenge to many web applications. Web text is often used to record events, especially for news. One text often mentions multiple events, while only the core event decides its main topic. This core event should take the important position when measuring text similarity. For this reason, this paper constructs a passage-level event connection graph to model the relations among events mentioned in one text. This graph is composed of many subgraphs formed by triggers and arguments extracted sentence by sentence. The subgraphs are connected via the overlapping arguments. In term of centrality measurement, the core event can be revealed from the graph and utilized to measure text similarity. Moreover, two improvements based on vector tunning are provided to better model the relations among events. One is to find the triggers which are semantically similar. By linking them in the event connection graph, the graph can cover the relations among events more comprehensively. The other is to apply graph embedding to integrate the global information carried by the entire event connection graph into the core event to let text similarity be partially guided by the full-text content. As shown by experimental results, after measuring text similarity from a passage-level event representation perspective, our calculation acquires superior results than unsupervised methods and even comparable results with some supervised neuron-based methods. In addition, our calculation is unsupervised and can be applied in many domains free from the preparation of training data.

Funding

Building resilience in at-risk rural communities through improving Media Communication on Climate Change Policies | Funder: Department of Foreign Affairs and Trade | Grant ID: 1447/CRG/2023/26-DU

Large Language Models in Engineering. | Funder: Aurecon Australasia Pty Ltd | Grant ID: INT-1239

Personalised Privacy-Preserving Network Data Publishing System | Funder: Australian Research Council | Grant ID: LP220200746

History

Journal

Applied Sciences (Switzerland)

Volume

12

Pagination

9887-9887

ISSN

2076-3417

eISSN

2076-3417

Language

en

Publication classification

C1 Refereed article in a scholarly journal

Issue

19

Publisher

MDPI AG

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