A scene graph is a semantic representation that expresses the objects, attributes, and relationships between objects in a scene. Scene graphs play an important role in many cross modality tasks, as they are able to capture the interactions between images and texts. In this paper, we focus on scene graph modification (SGM), where the system is required to learn how to update an existing scene graph based on a natural language query. Unlike previous approaches that rebuilt the entire scene graph, we frame SGM as a graph expansion task by introducing the incremental structure expanding (ISE). ISE constructs the target graph by incrementally expanding the source graph without changing the unmodified structure. Based on ISE, we further propose a model that iterates between nodes prediction and edges prediction, inferring more accurate and harmonious expansion decisions progressively. In addition, we construct a challenging dataset that contains more complicated queries and larger scene graphs than existing datasets. Experiments on four benchmarks demonstrate the effectiveness of our approach, which surpasses the previous state-of-the-art model by large margins.
This paper presents a novel system HENNA (Hybrid Person Name Analyzer) for identifying language origin and analyzing linguistic structures of person names. We conduct ME-based classification methods for the language origin identification and achieve very promising performance. We will show that word-internal character sequences provide surprisingly strong evidence for predicting the language origin of person names. Our approach is context-, language- and domain-independent and can thus be easily adapted to person names in or from other languages. Furthermore, we provide a novel strategy to handle origin ambiguities or multiple origins in a name. HENNA also provides a person name parser for the analysis of linguistic and knowledge structures of person names. All the knowledge about a person name in HENNA is modelled in a person-name ontology, including relationships between language origins, linguistic features and grammars of person names of a specific language and interpretation of name elements. The approaches presented here are useful extensions of the named entity recognition task.