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Extracting stop purpose information from raw GPS data is a crucial task in most location-aware applications. With the continuous growth of GPS data collected from mobile devices, this task is becoming more and more interesting; a lot of recent research has focused on pedestrians (mobile phones) data, while the commercial vehicles sector is almost unexplored. In this paper we target the problem of...
Automatic sentiment classification is becoming a popular and effective way to help online users or companies process and make sense of customer reviews. In this article, a learning-based method for classification of online reviews that achieves better classification accuracy is obtained by (a) combining valence shifters and opinion words into bigrams for use as features in an ordinal margin classifier...
Keyword extraction is an automated process that collects a set of terms, illustrating an overview of the document. The term is defined how the keyword identifies the core information of a particular document. Analyzing huge number of documents to find out the relevant information, keyword extraction will be the key approach. This approach will help us to understand the depth of it even before we read...
Coreference resolution plays a significant role in natural language processing systems. It is the method of figuring out all the noun phrases that refer back to the identical real world entity. Several researches have been done in noun phrase coreference resolution by using certain machine learning techniques. Our paper proposes a machine learning approach using support vector machines (SVM) towards...
Most studies on why-question answering system usually used the keyword-based approaches. They rarely involved domain ontology in capturing the semantic of the document contents, especially in detecting the presence of the causal relations. Consequently, the word mismatch problem usually occurs and the system often retrieves not relevant answers. For solving this problem, we propose an answer extraction...
Recently, many researchers have shown interest in using Word2Vec as the features for text classification tasks such as sentiment analysis. Its ability to model high quality distributional semantics among words has contributed to its success in many of the tasks. However, due to the high dimensional nature of the Word2Vec features, it increases the complexity for the classifier. In this paper, a method...
Online Social Networks (OSNs) have become fundamental parts of our online lives, and their popularity is increasing at a surprising rate every day. However, besides the revolution the OSNs have generated in social networking, they have also introduced some issues; first, since the amount of multimedia data on the Internet is growing continuously, it is extremely important for users not only to share...
This paper presents the results of systematic and comparative experimentation with major types of methodologies for automatic duplicate question detection when these are applied to datasets of progressively larger sizes, thus allowing to study the learning profiles of this task under these different approaches and evaluate their merits. This study was made possible by resorting to the recent release...
Traditional machine learning techniques, including support vector machine (SVM), random walk, and so on, have been applied in various tasks of text sentiment analysis, which makes poor generalization ability in terms of complex classification problem. In recent years, deep learning has made a breakthrough in the research of Natural Language Processing. Convolutional neural network (CNN) and recurrent...
Traditional action recognition methods aim to recognize actions with complete observations/executions. However, it is often difficult to capture fully executed actions due to occlusions, interruptions, etc. Meanwhile, action prediction/recognition in advance based on partial observations is essential for preventing the situation from deteriorating. Besides, fast spotting human activities using partially...
Over the years, indoor scene parsing has attracted a growing interest in the computer vision community. Existing methods have typically focused on diverse subtasks of this challenging problem. In particular, while some of them aim at segmenting the image into regions, such as object or surface instances, others aim at inferring the semantic labels of given regions, or their support relationships....
Fine-grained activity understanding in videos has attracted considerable recent attention with a shift from action classification to detailed actor and action understanding that provides compelling results for perceptual needs of cutting-edge autonomous systems. However, current methods for detailed understanding of actor and action have significant limitations: they require large amounts of finely...
Polarimetric SAR classification is an effective approach in image understanding. This paper proposes a novel semantic method for classification of Polarimetric SAR data. The method combines superpixels and semantic model to benefit from both the object-oriented classification and the high-level semantic information. Firstly, pixels was grouped into superpixels via Simple Linear Iterative Clustering...
In this paper, we propose a new approach to zone identification based on considering features with high semantic richness such as specialized names and mode of verbs belonging to a text's domain of interest and besides that mode of verbs, while taking into account features with less computational cost compared to those of conventional methods. Out of the scenarios of selecting features for identifying...
With the explosion of Web 2.0, customers are able to share their opinions and sentiments online. This has led to new opportunities for companies and organizations to understand people's opinions towards their products or services and can serve to improve their products or market strategy more effectively. However, the data on the Web is huge and unstructured, which makes it difficult to analyze automatically...
In this paper we present results of a research on automatic extremist text detection. For this purpose an experimental dataset in the Russian language was created. According to the Russian legislation we cannot make it publicly available. We compared various classification methods (multinomial naive Bayes, logistic regression, linear SVM, random forest, and gradient boosting) and evaluated the contribution...
Stop words occur multiple times in a document and the occurrence of stop words have least semantic value in the document sentences. These words cover a noteworthy bundle of archives that have no semantic significance. So, the stop words ought to be removed for better language description. In this paper, we have proposed a proficient algorithm which will eliminate the Urdu document stop words. Many...
Perceptual image of a product plays a significant role in decision making when users choose a product whose basic function is homogeneous nowadays. Designers try to design products that meet the all kinds of demands of users. However, a big gap between designers and users exists owning to the subjectivity of designers' experience. An objective model to recognize perceptual image of products is proposed...
Nowadays, many people express their opinions using user generated contains such as social media, forums and reviews. Opinion mining is a field of study that extracts sentiments from user generated contents. Because of the complexity of the Arabic language, extracting those opinions are challenging. Better representation of reviews can help to improve extraction of opinions. The traditional way of...
Nowadays, the “semantic gap” problems have greatly limited development of image classification. The key to this problem is to get semantic information of the images. A semantic image feature extraction method is proposed in this paper, in which eye movement information is integrated. Firstly, the underlying visual features of images are extracted. Secondly, weighed feature vectors of images are constructed...
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