This paper proposes an Approximate n-gram Markov Model for bag generation. Directed word association pairs with distances are used to approximate (n-1)-gram and n-gram training tables. This model has parameters of word association model, and merits of both word association model and Markov Model. The training knowledge for bag generation can be also applied to lexical selection in machine trans...

Distributed word representation is an efficient method for capturing semantic and syntactic word relations. In this work, we introduce an extension to the continuous bag-of-words model for learning word representations efficiently by using implicit structure information. Instead of relying on a syntactic parser which might be noisy and slow to build, we compute weights representing probabilitie...

Traditional bag-of-words model and recent wordsequence kernel are two well-known techniques in the field of text categorization. Bag-of-words representation neglects the word order, which could result in less computation accuracy for some types of documents. Word-sequence kernel takes into account word order, but does not include all information of the word frequency. A weighted kernel model th...

Effectively encoding visual properties from multimedia content is challenging. One popular approach to deal with this challenge is the visual dictionary model. In this model, images are handled as an unordered set of local features being represented by the so-called bag-of-(visual-)words vector. In this thesis, we work on three research problems related to the visual dictionary model. The first...

Feature selection in Word Sense Disambiguation (WSD) is as important as the selection of algorithm to remove sense ambiguity. Bag-of-word (BoW) features comprise the information of neighbors around the ambiguous target word without considering any relation between words. In this study, we investigate the effect of BoW features and Bag-of-bigrams (BoB) on Turkish WSD and compare the results with...

We introduce openXBOW, an open-source toolkit for the generation of bag-of-words (BoW) representations from multimodal input. In the BoW principle, word histograms were first used as features in document classification, but the idea was and can easily be adapted to, e. g., acoustic or visual low-level descriptors, introducing a prior step of vector quantisation. The openXBOW toolkit supports ar...

In this paper we present the first Content-Based Image Retrieval (CBIR) framework in the field of in vivo endomicroscopy, with applications ranging from training support to diagnosis support. We propose to adjust the standard Bag-of-Visual-Words method for the retrieval of endomicroscopic videos. Retrieval performance is evaluated both indirectly from a classification point-of-view, and directl...

We focus on the task of supervised sentiment classification of short and informal texts in Croatian, using two simple yet effective methods: word embeddings and string kernels. We investigate whether word embeddings offer any advantage over corpusand preprocessing-free string kernels, and how these compare to bag-ofwords baselines. We conduct a comparison on three different datasets, using diff...