Tag Cloud Generation Research Articles

On Building and Evaluating a Medical Records Exploration Interface Using Text Mining Techniques.

Medical records contain many terms that are difficult to process. Our aim in this study is to allow visual exploration of the information in medical databases where texts present a large number of syntactic variations and abbreviations by using an interface that facilitates content identification, navigation, and information retrieval. We propose the use of multi-term tag clouds as content representation tools and as assistants for browsing and querying tasks. The tag cloud generation is achieved by using a novelty mathematical method that allows related terms to remain grouped together within the tags. To evaluate this proposal, we have carried out a survey over a spanish database with 24,481 records. For this purpose, 23 expert users in the medical field were tasked to test the interface and answer some questions in order to evaluate the generated tag clouds properties. In addition, we obtained a precision of 0.990, a recall of 0.870, and a F1-score of 0.904 in the evaluation of the tag cloud as an information retrieval tool. The main contribution of this approach is that we automatically generate a visual interface over the text capable of capturing the semantics of the information and facilitating access to medical records, obtaining a high degree of satisfaction in the evaluation survey.

Exploiting relational tag expansion for dynamic user profile in a tag-aware ranking recommender system

A tag-aware recommender system (TRS) presents the challenge of tag sparsity in a user profile. Previous work focuses on expanding similar tags and does not link the tags with corresponding resources, therefore leading to a static user profile in the recommendation. In this article, we have proposed a new social tag expansion model (STEM) to generate a dynamic user profile to improve the recommendation performance. Instead of simply including most relevant tags, the new model focuses on the completeness of a user profile through expanding tags by exploiting their relations and includes a sufficient set of tags to alleviate the tag sparsity problem. The novel STEM-based TRS contains three operations: (1) Tag cloud generation discovers potentially relevant tags in an application domain; (2) Tag expansion finds a sufficient set of tags upon original tags; and (3) User profile refactoring builds a dynamic user profile and determines the weights of the extended tags in the profile. We analysed the STEM property in terms of recommendation accuracy and demonstrated its performance through extensive experiments over multiple datasets. The analysis and experimental results showed that the new STEM technique was able to correctly find a sufficient set of tags and to improve the recommendation accuracy by solving the tag sparsity problem. At this point, this technique has consistently outperformed state-of-art tag-aware recommendation methods in these extensive experiments.

블로그 포스팅을 이용한 방송 콘텐츠 영상의 타임라인 단위 태그 클라우드 생성

Due to the recent increasement of user created contents like SNS, blog posts, and so on, broadcast contents are actively re-construction by its users. Especially, on some genres like drama, movie, various information from cars and film sites to clothes and watches in a content is spreaded out to other users through blog postings. Since such information can be an additional information for the content, they can be used for providing high-quality broadcast services. For this purpose, in this paper, we propose timeline tag cloud generation method for broadcasting contents. In the proposed method, blog postings on the target contents are first gathered and then, images and words around images are extracted from a blog post as a tag set. An extracted tag set is tagged on a specific timeline of the target content. In experiments, to prove the efficiency of the proposed method, we evaluated the performances of the proposed image matching and tag cloud generation methods.

A cloud of FAQ: A highly-precise FAQ retrieval system for the Web 2.0

FAQ (Frequency Asked Questions) lists have attracted increasing attention for companies and organizations. There is thus a need for high-precision and fast methods able to manage large FAQ collections. In this context, we present a FAQ retrieval system as part of a FAQ exploiting project. Following the growing trend towards Web 2.0, we aim to provide users with mechanisms to navigate through the domain of knowledge and to facilitate both learning and searching, beyond classic FAQ retrieval algorithms. To this purpose, our system involves two different modules: an efficient and precise FAQ retrieval module and, a tag cloud generation module designed to help users to complete the comprehension of the retrieved information. Empirical results evidence the validity of our approach with respect to a number of state-of-the-art algorithms in terms of the most popular metrics in the field.

A Keyphrase-Based Tag Cloud Generation Framework to Conceptualize Textual Data

Tag clouds have become an effective tool to quickly perceive the most prominent terms embedded within textual data. Tag clouds help grasp the main theme of a corpus without exploring the pile of documents. However, the effectiveness of tag clouds to conceptualize text corpora is directly proportional to the quality of the tags. In this paper, the authors propose a keyphrase-based tag cloud generation framework. In contrast to existing tag cloud generation systems that use single words as tags and their frequency counts to determine the font size of the tags, the proposed framework identifies feasible keyphrases and uses them as tags. The font-size of a keyphrase is determined as a function of its relevance weight. Instead of using partial or full parsing, which is inefficient for lengthy sentences and inaccurate for the sentences that do not follow proper grammatical structure, the proposed method applies n-gram techniques followed by various heuristics-based refinements to identify candidate phrases from text documents. A rich set of lexical and semantic features are identified to characterize the candidate phrases and determine their keyphraseness and relevance weights. The authors also propose a font-size determination function, which utilizes the relevance weights of the keyphrases to determine their relative font size for tag cloud visualization. The efficacy of the proposed framework is established through experimentation and its comparison with the existing state-of-the-art tag cloud generation methods.