Systemic features use linguisticallyderived language models as a basis for text classification. The graph structure of these models allows for feature representations not available with traditional bag-of-words approaches. This paper explores the set of possible representations, and proposes feature selection methods that aim to produce the most compact and effective set of attributes for a giv...

This paper investigates the capabilities of the Bag-of-Words (BWs) method in the 3D shape retrieval field. The contributions of this paper are (1) the 3D shape retrieval task is categorized from different points of view: specific versus generic, partial-toglobal retrieval (PGR) versus global-to-global retrieval (GGR), and articulated versus nonarticulated (2) the spatial information, represente...

In the context of the CLEF-IP 2010 classification task, we conducted a series of experiments with the Linguistic Classification System (LCS). We compared two document representations for patent abstracts: a bag-of-words representation and a syntactic/semantic representation containing both words and dependency triples. We evaluated two types of output: using a fixed cut-off on the ranking of th...

We present the BossaNova scheme for the ImageCLEF 2012 Flickr Photo Annotation Task. BossaNova is a mid-level image representation, recently developed by our team, that enriches the Bag-of-Words representation, by keeping a histogram of distances between the descriptors found in the image and those in the codebook. Our scheme has the advantage of being conceptually simple, non-parametric, and e...

A set of words labelled with their prior emotion is an obvious place to start on the automatic discovery of the emotion of a sentence, but it is clear that context must also be considered. No simple function of the labels on the individual words may capture the overall emotion of the sentence; words are interrelated and they mutually influence their affectrelated interpretation. We present a me...

Finding similar questions in Community Question Answering (CQA) services plays more and more important role in current web and IR applications. The task aims to retrieve historical questions that are similar or relevant to new questions posed by users. However, traditional “bag-of-words” based models would fail to measure the similarity between question sentences, as they usually ignore sequent...

This paper presents our work on textual inference and situates it within the context of the larger goals of machine reading. The textual inference task is to determine if the meaning of one text can be inferred from the meaning of another combined with background knowledge. Most existing work either provides only very limited text understanding by using bag-of-words lexical similarity models or...

This paper describes our participation at the ImageClef 2009 medical annotation task. In this task we have used the bag-of-words approach for image representation. We submitted one run, using support-vector-machines trained on the visual word histograms in multiple scales. In this task our result ranked first, with error score of 852.8.

This paper describes our participation in Genre Tagging Task of MediaEval 2011, which aims to predict the videos’ category label. We use bag-of-words approaches with different features derived from visual content and associated textual information. We perform different experiments in which different constellations in respect of single modalities, classification methods, visual features and thei...

one of the major criticisms leveled at task-based language teaching (tblt), despite its countless merits, is developing fluency at the cost of accuracy. the post-task stage affords a number of options to counteract this downside through task repetition and task recycling. these two options are considered to positively affect learners' oral performance in terms of fluency, accuracy, and complexi...