GADE+: A Graph-Based Anchor-Enhanced Framework for Targeted Document Detection

The documents mentioning a target entity are es sential prerequisites of various applications, such as market intelligence analysis, knowledge base enrichment, fact checking and retrieval augmented generation. A simple solution to acquire these documents is to exploit search engines via querying the name of the target entity. However, the name of the target entity appearing in a returned document does not necessarily mean it really refers to the target entity due to the name ambiguity, as it may refer to another entity sharing the same name as the target entity. Thus, in this paper, we explore a new task of targeted document detection, which aims to detect those targeted documents (i.e., documents really mentioning the target entity) from the given candidate documents each of which contains an ambiguous name of the target entity. We propose GADE+, a novel Graph-based Anchor-enhanced framework to solve the task of targeted Document dEtection by leveraging both the local relevance information (via a local relevance model) and the global cross-document interactions (via an anchor-based global interaction model) jointly. The framework GADE, as presented in our conference paper, relies on a complete graph to model global cross-document interactions, resulting in many useless interactions and limited scalability. To address the above issues, we first introduce virtual anchors representing virtual candidate documents, and construct a document interaction bipartite graph between candidate documents and virtual anchors. Then we apply a graph neural network over the graph to model the global cross-document interactions via anchor-based message passing mechanism. To further learn more discriminative representations of virtual anchors and candidate documents, an anchor-guided regularization is devised to explicitly improve the inter-class separability of virtual anchors and the intra-class compactness of candidate documents. We construct four labeled datasets for this task based on Wikipedia and Web documents respectively, and a thorough experimental study shows that our framework GADE+ significantly outperforms all the baseline methods in terms of F1-score.