This paper compares a neural network DSM relying on textual co-occurrences with a multi-modal model integrating visual information. We focus on nominal vs. verbal compounds, and zoom into lexical, empirical and perceptual target properties to explore the contribution of the visual modality. Our experiments show that (i) visual features contribute differently for verbs than for nouns, and (ii) i...

This paper presents an experimental framework for the Placing tasks, both estimation and verification at MediaEval Benchmarking 2016. The proposed framework provides results for four runs first, using metadata (such as user tags and title of images and videos), second, using visual features extracted from the images (such as tamura), third, by using the textual and visual features together and ...

In this paper we present Carnegie Mellon University’s submission to the TREC 2009 Relevance Feedback Track. In this submission we take a classification approach on document pairs to using relevance feedback information. We explore using textual and non-textual document-pair features to classify unjudged documents as relevant or non-relevant, and use this prediction to re-rank a baseline documen...

The image retrieval applications are designed to fetch required images from the image databases. Images are searched using textual query or images. The textual query based retrieval is performed with image annotations. The image features are used in the content based image retrieval process on the image database provides huge collection of images. Query image features are compared with the data...

In this paper, we propose a novel strategy at an abstract level by combining textual and visual clustering results to retrieve images using semantic keywords and auto-annotate images based on similarity with existing keywords. Our main hypothesis is that images that fall in to the same textcluster can be described with common visual features of those images. In this approach, images are first c...

Textual entailment among sentences is an important part of applied semantic inference. In this paper we propose a novel technique to address the recognizing textual entailment challenge, which based on the distribution hypothesis that words that tend to occur in the same contexts tend to have similar meanings. Using the IDF of the overlapping words between the two propositions, we calculate the...

The paper describes the QCRI submissions to the shared task of automatic Arabic dialect classification into 5 Arabic variants, namely Egyptian, Gulf, Levantine, North-African (Maghrebi), and Modern Standard Arabic (MSA). The relatively small training set is automatically generated from an ASR system. To avoid over-fitting on such small data, we selected and designed features that capture the mo...

In this paper, we report our work on automatic image annotation by combining several textual features drawn from the text surrounding the image. Evaluation of our system is performed on a dataset of images and texts collected from the web. We report our findings through comparative evaluation with two gold standard collections of manual annotations on the same dataset.

We present a supervised learning approach to cross-lingual textual entailment that explores statistical word alignment models to predict entailment relations between sentences written in different languages. Our approach is language independent, and was used to participate in the CLTE task (Task#8) organized within Semeval 2013 (Negri et al., 2013). The four runs submitted, one for each languag...