Bag-of-words (BOW) is now the most popular way to model text in machine learning based sentiment classification. However, the performance of such approach sometimes remains rather limited due to some fundamental deficiencies of the BOW model. In this paper, we focus on the polarity shift problem, and propose a novel approach, called dual training and dual prediction (DTDP), to address it. The b...

Sentiment analysis generally uses large feature sets based on a bag-of-words approach, which results in a situation where individual features are not very informative. In addition, many data sets tend to be heavily skewed. We approach this combination of challenges by investigating feature selection in order to reduce the large number of features to those that are discriminative. We examine the...

In this paper, we summarize how the action recognition can be improved when multiple views are available. The novelty is that we explore various combination schemes within the robust and simple bag-of-words (BoW) framework, from early fusion of features to late fusion of multiple classifiers. In new experiments on the publicly available IXMAS dataset, we learn that action recognition can be imp...

Background

objective: the purpose of the current research is to determine the impact of play therapy on academic achievement of students with mental deficiency of khorramabad exceptional elementary school. method: it was a pre-test, post-test study design. 60 students were selected and according to their mental deficiency characteristics were assigned to either experimental or control group. each group co...

Function words play an important role in sentence structures and express grammatical relationships with other words. Most statistical machine translation (SMT) systems do not pay enough attention to translations of function words which are noisy due to data sparseness and word alignment errors. In this paper, a novel method is designed to separate the generation of target function words from ta...

In this paper we report on our participation in the CLEF-IP 2011 prior art retrieval task. We investigated whether adding syntactic information in the form of dependency triples to a bag-of-words representation could lead to improvements in patent retrieval. In our experiments, we investigated this effect on the title, abstract and first 400 words of the description section. The experiments wer...

In this paper, we introduce iBoW-LCD, a novel appearance-based loop closure detection method. The presented approach makes use of an incremental Bag-of-Words (BoW) scheme based on binary descriptors to retrieve previously seen similar images, avoiding any vocabulary training stage usually required by classic BoW models. In addition, to detect loop closures, iBoW-LCD builds on the concept of dyn...

We present the study of sentiment classification of Chinese contrast sentences in this paper, which are one of the commonly used language constructs in text. In a typical review, there are at least around 6% of such sentences. Due to the complex contrast phenomenon, it is hard to use the traditional bag-of-words to model such sentences. In this paper, we propose a Two-Layer Logistic Regression ...