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always have specific preferences regarding their trips. Instead of restricting users to limited query options such as locations, activities, or time periods, we consider arbitrary text descriptions as keywords about personalized requirements. Moreover, a diverse and representative set of recommended travel routes is needed
document. We think that our graph captures many properties of the text documents and can be used for different application in the field of text mining and NLP, such as keyword extraction and to know the nature of the document. Our approach to construct a semantic graph is independent of any language. We performed an
It is truly said by Michelangelo Antonisoni that “We live in a society that compels us to go on using the concepts, and we no longer know what they mean”. A central challenge in understanding the vital concepts of a thesis lies in the complexity of jargons. Our goal is to build an interactive Web application coupled with a chat interface to make learning or knowledge gaining easier. Thus our Web application...
keywords of a target technology field, we represented patent documents in vector space model and cluster patent documents by the kohonen's self-organising neural network algorithm. With the clustering results,we formed a semantic network of keywords without respect of filing dates.And a technical development trend graph was
livelihoods, how to deal with its negative impacts, and which mitigation or adaptation policies to support. A line of related work has used bag of words and word-level features to detect frames automatically in text. Such works face limitations since standard keyword based features may not generalize well to accommodate surface
Useful information from a text document can be extracted using text mining techniques. Many text mining methods are generally uses term or keyword based approach. They all have advantages, but get stuck into the issue of synonymy: two terms have the same meaning and polysemy: one term has many meanings. So phrase
Technology progress brings the very rapid growth of patent publications, which increases the difficulty of domain experts to measure the development of various topics, handle linguistic terms used in evaluation and understand massive technological content. To overcome the limitations of keyword-ranking type of text
This paper suggests a SAO network for identifying technological opportunities by reusing inventive knowledge of patents. Despite the keyword-based approach's ease of use and simplicity, the approach is not sufficient for addressing the reuse of technological knowledge because it cannot represent how technological
In this paper we propose an automated method for generating domain specific stop words to improve classification of natural language content. Also we implemented a bayesian natural language classifier working on web pages, which is based on maximum a posteriori probability estimation of keyword distributions using bag
A novel text association rule approach FHAR algorithm is presented. To overcome the defect of traditional keywords which does not take into account the semantic relation between keywords, FHAR algorithm in the paper is based on concept vector. The density of semantic field and the weight of meaning are used to
In this paper, we designed a knowledge supporting software system in which sentences and keywords are extracted from large scale document database. This system consists of semantic representation scheme for natural language processing of the document database. Documents originally in a form of PDF are broken into
This paper aims to design a system model that analyzes the unstructured data inside the posts about electronic products on social networking websites. For the purposes of this study, posts on social networking websites have been mined and the keywords are extracted from such posts. The extracted keywords and the
Nowadays growing number of popularization in the World Wide Web promotes e-learning via web. During e-learning the users can easily share, reuse, and organize the knowledge. Using the search engine the e-learners search the web pages by set of keywords. But the pages which are unrelated for our tags come frequently
latent semantic analysis), is used to classify event types. To decide an appropriate number of event types, lexical pattern analysis is used. By the experimental results, it is observed that our approach could be used to extract event keywords from the basketball webcast texts.
Most of the exist Web search engines utilize matching the query keywords to pieces of information approach to identify of the data satisfying user's request. These methods are not only inefficient, but also wasted a lot of user's time to find a satisfactory results. In order to improve the problem above, we presented
keywords. Ontology can play a very important role in the process of creating as well as managing the knowledge. This paper addresses the important issues in developing domain specific ontology for agriculture domain. We propose a generic approach for agriculture domain ontology representing entities and their relationships
Bloom taxonomy verb keywords being assigned to more than one category of Bloom taxonomy. The presence of this poses a problem in respect of classifying a particular question into a right category of Bloom taxonomy. And feature extraction plays an important role in improving the accuracy of classifier such as Support Vector
difficulty due to the large size of the list of words in a thesaurus. In this paper, we present a new method for solving the problem of text categorization over a corpus of newspaper articles where the annotation must be composed of thesaurus elements. The method consists of applying lemmatization, obtaining keywords and named
documents based on keywords, users normally have a more abstract perception what information they require. Semantic gap, which is the disparity between user's request and query results, has been identified as a challenging issue. In this paper, we are interested in scientific document indexing for retrieval. Knowing the
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