Search

The abundance of medical resources has encouraged the development of systems that allow for efficient searches of information in large medical image data sets. State-of-the-art image retrieval models are classified into three categories: content-based (visual) models, textual models, and combined models. Content-based models use visual features to answer image queries, textual image retrieval models use word matching to answer textual queries, and combined image retrieval models, use both textual and visual features to answer queries. Nevertheless, most of previous works in this field have used the same image retrieval model independently of the query type. In this article, we define a list of generic and specific medical query features and exploit them in an association rule mining technique to discover correlations between query features and image retrieval models. Based on these rules, we propose to use an associative classifier ( NaiveClass) to find the best suitable retrieval model given a new textual query. We also propose a second associative classifier ( SmartClass) to select the most appropriate default class for the query. Experiments are performed on Medical ImageCLEF queries from 2008 to 2012 to evaluate the impact of the proposed query features on the classification performance. The results show that combining our proposed specific and generic query features is effective in query classification. [ABSTRACT FROM AUTHOR]

Aspect-level sentiment classification is a fundamental subtask of fine-grained sentiment analysis. The syntactic information and commonsense knowledge are important and useful for aspect-level sentiment classification, while only a limited number of studies have explored to incorporate them via flexible graph convolutional neural networks (GCN) for this task. In this paper, we propose a new Syntax- and Knowledge-based Graph Convolutional Network (SK-GCN) model for aspect-level sentiment classification, which leverages the syntactic dependency tree and commonsense knowledge via GCN. In particular, to enhance the representation of the sentence toward the given aspect, we develop two strategies to model the syntactic dependency tree and commonsense knowledge graph, namely SK-GCN 1 and SK-GCN 2 respectively. SK-GCN 1 models the dependency tree and knowledge graph via Syntax-based GCN (S-GCN) and Knowledge-based GCN (K-GCN) independently, and SK-GCN 2 models them jointly. We also apply pre-trained BERT to this task and obtain new state-of-the-art results. Extensive experiments on five benchmark datasets demonstrate that our approach can effectively improve the performance of aspect-level sentiment classification compared with the state-of-the-art methods. [ABSTRACT FROM AUTHOR]