Recently, online health expert question-answering (HQA) services (systems) have attracted more and more health consumers to ask health-related questions everywhere at any time due to the convenience and effectiveness. However, the quality of answers in existing HQA systems varies in different situations. It is significant to provide effective tools to automatically determine the quality of the ...

In this paper, we address the problem for predicting cQA answer quality as a classification task. We propose a multimodal deep belief nets based approach that operates in two stages: First, the joint representation is learned by taking both textual and non-textual features into a deep learning network. Then, the joint representation learned by the network is used as input features for a linear ...

Humans upload over 1.8 billion digital images to the internet each day, yet the relationship between the images that a person shares with others and his/her psychological characteristics remains poorly understood. In the current research, we analyze the relationship between images, captions, and the latent demographic/psychological dimensions of personality and gender. We consider a wide range ...

In this paper, we present the methods we have proposed and evaluated through the ImageCLEF 2012 Photo Annotation task. More precisely, we have proposed the Histogram of Textual Concepts (HTC) textual feature to capture the relatedness of semantic concepts. In contrast to term frequency-based text representations mostly used for visual concept detection and annotation, HTC relies on the semantic...

Information in visually rich formats such as PDF and HTML is often conveyed by a combination of textual and visual features. In particular, genres such as marketing flyers and info-graphics often augment textual information by its color, size, positioning, etc. As a result, traditional text-based approaches to information extraction (IE) could underperform. In this study, we present a supervise...

This paper presents our submitted experiments in the Concept annotation and Concept Retrieval tasks using Flickr photos at ImageCLEF 2012. This edition we applied new strategies for both the textual and the visual subsystems included in our multimodal retrieval system. The visual subsystem has focus on extending the low-level features vector with concept features. These concept features have be...

In the first part of this paper we describe our experiments in the automatic and interactive search tasks of TRECVID 2006. We submitted five fully automatic runs, including a text baseline, two runs based on visual features, and two runs that combine textual and visual features in a graph model. For the interactive search, we have implemented a new video search interface with relevance feedback...

Compound figure detection on figures and associated captions is the first step to making medical figures from biomedical literature available for further analysis. The performance of traditional methods is limited to the choice of hand-engineering features and prior domain knowledge. We train multiple convolutional neural networks (CNNs), long short-term memory (LSTM) networks, and gated recurr...

Current textual CBR research focuses on generating knowledge-rich representations for working with document-cases. However, there are many \weakly-textual" contexts in which textual information plays an important , but ancillary role in case composition and reasoning. For retrieving weakly-textual \semi-structured" cases that contain one or more textual features, it is desirable to measure the ...