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methods have their own shortcomings. In this paper, a new Machine Learning and Keyword-matching Integrated (MALKI) protocol identification method is proposed to overcome the shortcomings brought by these existing methods. The proposed method combines the content and behavior-based technologies together to identify the
The problem of automatically extracting the most interesting and relevant keyword phrases in a document has been studied extensively as it is crucial for a number of applications. These applications include contextual advertising, automatic text summarization, and user-centric entity detection systems. All these
method that combines both the character length of a word, and frequency of a word within a document to simulate a mass. Our model then computes the force of attraction and ranks the word-pair-force as a means of keyword and keyphrase extraction. Experimental results on several text documents demonstrated that the proposed
part of a trending discussion topic by the presence of a tagged keyword. Relying solely on this keyword, however, may be inadequate for identifying all the discussion associated with a trend. Our research demonstrates that machine learning techniques can be used identify the top trend a tweet belongs to with up to 85
combined effectively to predict the annotation for each image. Moreover, we combine both low-level feature of image and semantic information naturally. In addition, we also combine the correspondence between keywords and image visual tokens/regions, and the word-to-word correlation to enhance the annotation. We employ the
In this paper, a new method for question classification is proposed, which employs ensemble learning algorithms to train multiple question classifiers. These component learners are combined to produce the final hypothesis. In detail, the feature spaces are obtained through extracting high-frequency keywords from
exchange or committing crimes for its anonymity and completely uncontrolled chatting environment. To help enforce legitimate contents communicated in chat environments, IM monitoring systems have been developed for monitoring chat messages. Although most of these systems can provide good monitoring functions, they only
study how to identify known item queries in the context of a large academic institution's online public access catalog (OPAC), in which queries are issued via a simple keyword interface. We also examine how to recognize when a known item query has retrieved the item in question. Our approach combines techniques in machine
In this paper, computer-based techniques for stylistic analysis of paintings are applied to the five panels of the 14th century Peruzzi Altarpiece by Giotto di Bondone. Features are extracted by combining a dual-tree complex wavelet transform with a hidden Markov tree (HMT) model. Hierarchical clustering is used to
machine learning approach. The keywords are used to cluster the documents subset. The clustered result is the taxonomy of the subset. Lastly, the taxonomy is modified to the hierarchical structure for user navigation by manual adjustments. The topic digital library is constructed after combining the full-text retrieval and
becomes really difficult because name abbreviation, interdisciplinary, especially tautonym for Chinese scholars. The scholar classification can be achieved by the publications, journals that they published with, keywords in their publications using big-data techniques.
knowledge from existing knowledge bank by extracting linguistic information such as part-of-speech and co-occurrence of keywords and constructing a new domain-adaptive transfer knowledge bank. Through experiments on homogeneous and heterogeneous feature spaces, we testify the efficacy of our methods.
rely on indexing web pages so that the information obtained by the tourist is still unfavorable because it only shows a web page with keywords that exist on the article. A support system to recognize tourism places on the web pages is required to produce better information presentation. In this study, the recognition
semantic web search engines relates user keyword with terms, entities, texts, documents which have semantic correlation with user query. Both search engines does not use images within web pages to find more relevant information. Now in this paper we have formulated a web document integrated ranking method based on text
distinctive spam keywords. We investigate two ways of detecting such spams: 1) By comparing the similarity between the publisher posts and user comments, and 2) by learning a single representative meta-feature such as user name or ID. The first measure relieves us from repetitively learning a set of domain-dependent spam
. Thus, it is of great significance for enterprises to find reasonable solutions automatically. Combined with keyword tokenization, data mining, numerical optimization and neural network, this paper presents a system that compares and finds the most similar incident solution in the past, based on the description provided by
With the development of the World Wide Web, there exists more and more illicit drug Webpages. Thus, how to screen cannabis Webpages on the internet is a quite important issue. Conventional methods that only use the keyword-based or image-based approaches are not sufficient. We propose a Multi-Modal Multiple-Instance
analysis and keyword extraction information retrieval. As well as, through doing the named entity recognition, we consider that it can mine exact information from text document to respond to user. This paper describes how to do the Japanese sightseeing named entity recognition due to we are constructing a Japanese sightseeing
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