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Documents contain various types of information, and money information is one of such information. In the sentence “He borrowed ten dollars from me”, the expression ‘ten dollars’ conveys important information. When it is normalized into a specific way (e.g., 10 USD), then it can be used to develop various applications: Question-Answering (QA) system or Dialog system. In this paper, we propose an annotation...
Existing studies in skeleton-based action recognition mainly utilise skeletal data taken from a single camera. Since the quality of skeletal tracking of a single camera is noisy and unreliable, however, combining data from multiple cameras can improve the tracking quality and hence increase the recognition accuracy. In this study, the authors propose a method called weighted averaging fusion which...
With a growing number of Web documents, many approaches have been proposed for knowledge discovery on Web documents. The documents do not always provide keywords or categories, so unsupervised approaches are desirable, and topic modeling is such an approach for knowledge discovery without using labels. Further, Web documents usually have time information such as publish years, so knowledge patterns...
We proposed neural network architecture based on Convolution Neural Network(CNN) for temporal relation classification in sentence. First, we transformed word into vector by using word embedding. In Feature Extraction, we extracted two type of features. Lexical level feature considered meaning of marked entity and Sentence level feature considered context of the sentence. Window processing was used...
Documents usually contain temporal expressions such as ‘morning’ and ‘yesterday’, and they also often contain spatial expressions such as ‘house’ and ‘the South Pole’. Extracting temporal information or spatial information from the documents is important because such information can be useful for various applications. Although there have been many studies aiming at extracting temporal information...
With the exponential growth in data, we often find ourselves struggling to deal with information overload. Techniques such as timeline summarization tackle this problem by generating short summaries for each time stamp on a timeline. However, we argue that rather than reading a set of blocks of texts, it is easier and quicker for a reader to observe dynamically changing relations between important...
With the proliferation of mobile devices, many users now take advantage of location-based services that use their current position. However, careful consideration should be made when sending one's location to another as the location often includes personal attributes such as home address and reveals private information such as health or religion. To resolve this issue, a dummy generation technique...
This paper investigates the performance of Elman-type and Jordan-type recurrent neural networks (RNN) in extracting temporal information from textual data. The RNN architectures are applied to two tasks of TempEval-2 challenge: (1) extracting the extent of TIMEX3 tags and its TYPE, and (2) extracting the extent of EVENT tags and its CLASS attribute. For the first task, the performances of the RNN...
In Korea, authors of the newspaper article tend to express their intention indirectly, that is, they choose a method to leave out some important facts, or sometimes uses biased terms to support their opinion. Since they're not expressing their opinion directly, detecting the political bias is a difficult task. In this paper, we propose a method to detect political bias in the Korean articles by first...
Due to the growing number of unlabeled documents, it is becoming important to develop unsupervised methods capable of automatically extracting information. Topic models and neural networks represent two such methods, and parameter approximation algorithms are typically employed to estimate the parameters because it is not possible precisely to compute the parameters when using these methods. One of...
Unsupervised methods for image segmentation have recently drawn significant attention because most images do not have labels or tags. A topic model is an unsupervised probabilistic method that captures the latent aspects of data, where each latent aspect or topic is associated with one homogeneous region. In this paper, we propose a new topic model for image segmentation task that incorporates spatial...
With the growing number of web documents, it becomes difficult to analyze and obtain information from such an array of documents. Furthermore, unsupervised methods are preferable, as most web documents are unlabeled. Probabilistic topic modeling is one such method. It discovers latent structures among unstructured documents. While many traditional topic models usually assume that the topics are independent...
Existing healthcare services on smartphones are not convenient to use as they typically require too many manual inputs from users. This paper presents a semi-automated obesity-care application for helping people manage their own lifestyles. In our approach, a person's lifestyle is captured by a log of daily activities performed (e.g., eating, visiting, etc.), which can be captured automatically. For...
Ubiquitous computing technologies have been developed fast and various decision support systems were proposed. Consecutively, in recently, people are working on an implicit service agent of mobile phone to make people to be provided useful services without paying attentions. However, when people want to buy some products or to go somewhere, people are still going to use Internet to get preference...
The paper introduces an approach to automatically recognize people's activity patterns within an “intelligent” building. We envisage a model of interactions with a smart phone and building. Various sensors in smart phone enable to recognize daily routine of people's activities automatically in building. The smart phone application; `Activity Pattern Recognition in Mobile Environment (APRiME)' recognized...
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