[This corrects the article DOI: 10.5888/pcd12.140529.]. In the article “Tobacco Use Screening and Counseling During Hospital Outpatient Visits Among US Adults, 2005–2010,” the caption for Figure 2 was incorrectly phrased by the author. The original caption read as follows: “Percentage of cessation assistance (counseling, or medications, or both) ordered or provided during hospital outpatient vi...

When a neural language model is used for caption generation, the image information can be fed to the neural network either by directly incorporating it in a recurrent neural network – conditioning the language model by injecting image features – or in a layer following the recurrent neural network – conditioning the language model by merging the image features. While merging implies that visual...

In this paper we discuss and evaluate a method for automatic text illustration, applied to answers to medical questions. Our method for selecting illustrations is based on the idea that similarities between the answers and picture-related text (the picture’s caption or the section/paragraph that includes the picture) can be used as evidence that the picture would be appropriate to illustrate th...

In the article “Brain Stimulation Reward Supports More Consistent and Accurate Rodent Decision-Making than Food Reward,” by Matthew S. McMurray, Sineadh M. Conway, and Jamie D. Roitman, which appeared on e0015-17.2017 of the April 18, 2017, issue, there was a mislabeled axis on Figure 3D, which led to an inaccuracy in the figure caption and statistical table (Table 1). The y-axis of this figure...

This paper describes the SINAI team participation in the ImageCLEF campaign. In this paper we only explain the experiments accomplished in the medical task. We have experimented with query expansion and the text information of the collection. For expansion, we carry out experiments using MeSH ontology and UMLS separately. With respect to text collection, we have used three different collections...

In this work, we showcase the Image2Text system, which is a real-time captioning system that can generate human-level natural language description for any input image. We formulate the problem of image captioning as a multimodal translation task. Analogous to machine translation, we present a sequence-to-sequence recurrent neural networks (RNN) model for image caption generation. Different from...

Many proper names are spelled inconsistently in speech recognizer output, posing a problem for applications where locating mentions of named entities is critical. We model the distortion in the spelling of a name due to the speech recognizer as the effect of a noisy channel. The models follow the framework of the IBM translation models. The model is trained using a parallel text of closed capti...

We present our tracking system for the 1998 Topic Detection and Tracking project (TDT-2). This project addresses multiple sources of information in the form of both text and speech from newswire, radio and television news broadcast programs. Our tracking system is probability based and we successfully solve the problem of score normalization across topics with a simple but e ective solution. Te...

Colloquial English (CE) as found in television programs or typical conversations is different than text found in technical manuals, newspapers and books. Phrases tend to be shorter and less sophisticated. In this paper, we look at some of the theoretical and implementational issues involved in translating CE. We present a fully automatic large-scale multilingual natural language processing syst...

In this paper we present NewsFlash, an adaptive search system that assists a searcher to efficiently search a library of stored TV news reports. The system automatically summarises the closed-caption subtitles embedded in the TV broadcasts and selects appropriate sentences to best describe report content in respect to the searcher’s query. During interaction the system selects useful terms from...