Text visualization for construction document information management

Web page keyword extraction is widely used in web text classification, text clustering, and information retrieval. However, the keyword extraction of the Chinese web page still need be improved and applied, especially in the medical field. This paper proposes an improved TF-IDF algorithm based on WF-TF-IDF to extract keywords from Chinese medical web page. The WF-TF-IDF algorithm considers three factors which are word frequency in the title, description and word distribution of categories in the corpus. We do the data-preprocessing which includes web page denoising, regular expression processing, Chinese word segmentation, synonyms exchanging and stop word filtering. Then we extract keywords based on the result of data-preprocessing. We filter the meaningless words in the extracted keywords according to the part of speech. The experimental results shows that the WF-TF-IDF algorithm improves the precision rate and recall rate by about 7% compared to the traditional TF-IDF algorithm.

The Keyword Extraction of Chinese Medical Web Page Based on WF-TF-IDF Algorithm

Lecture videos constitute an important part of the e-learning paradigm. These online video-lectures contain multimedia materials aimed at explaining complex concepts in a more effective way. The videos are mostly grouped by their subjects. However, often there are overlaps between the subjects, e.g. Mathematics and Statistics. Hence, educational content-wise, some of the lecture videos can belong to more than one subject. When they are labeled by only one subject, students searching for the content of the lecture might miss some of these videos. To solve this problem, we aim to provide a clustering of these lecture videos based on their educational content rather than their titles so that such lectures will not be missed out based on the subject labels. Our novel algorithm uses topic modeling on video transcripts generated by automatic captions to extract the contents of these videos. We choose representative text documents for each of the clusters from the Wikipedia. Then we calculate a similarity between the topics extracted from the videos and those of the representative documents of the clusters. Finally we apply fuzzy clustering based on these similarity values and provide a lecture-content based clustering for these lecture videos. The initial results are plausible and confirm the effectiveness of the proposed scheme.

Fuzzy clustering of lecture videos based on topic modeling

The task of keyword extraction aims at capturing expressions (or entities) that best represent the main topics of a document. Given the rapid adoption of these online semantic annotators and their contribution to the growth of the Semantic Web, one important task is to assess their quality. This article presents an evaluation of the quality and stability of semantic annotators on domain-specific and open domain corpora. We evaluate five semantic annotators and compare them to two state-of-the-art keyword extractors, namely KP-miner and Maui. Our evaluation demonstrates that semantic annotators are not able to outperform keyword extractors and that annotators perform best on domains having a high keyword density.

An Assessment of Online Semantic Annotators for the Keyword Extraction Task

Wikipedia - the online encyclopedia - has long been used as a source of information for researchers, as well as being a subject of research itself Wikipedia has been shown to be effective in recommender systems, sentiment analysis, validation and multiple domains in information retrieval One of the reasons for Wikipedia's popularity among researchers and practitioners is the multiple types of information it contains, which enables practitioners to select the right "tool" for their respective tasks In addition to its great potential, this multitude of information sources also poses a challenge: which sources of information are best suited for a specific problem and how can different types of data be combined? This tutorial aims to provide a holistic view of Wikipedia's different features - text, links, categories, page views, editing history etc - and explore the different ways they can be utilized in a machine learning framework By presenting and contrasting the latest works that utilize Wikipedia in multiple domains, this tutorial aims to increase the awareness among researchers and practitioners in these fields to the benefits of utilizing Wikipedia in their respective domains, in particular to the use of multiple sources of information simultaneously

Exploiting Wikipedia for Information Retrieval Tasks

In this paper we present three new methods to extract keywords from web pages using Wikipedia as an external source of information. The information used from Wikipedia includes the titles of articles, co-occurrence of keywords and categories associated with each Wikipedia definition. We compare our methods with three keyword extraction methods used as baselines: (i) all the terms of a web page, (ii) a TF-IDF implementation that extracts single weighted words of a web page and (iii) a previously proposed Wikipedia-based keyword extraction method presented in the literature. We compare our three keyword extraction methods with the baseline methods in three distinct scenarios, all related to our target application, which is the selection of ads in a context-based advertising system. In the first scenario, the target pages to place ads were extracted from Wikipedia articles, whereas the target pages in the other two scenarios were extracted from a news web site. Experimental results show that our methods are quite competitive solutions for the task of selecting good keywords to represent target web pages, albeit being simple, effective and time efficient. For instance, in the first scenario our best method used to extract keywords from Wikipedia articles achieved an improvement of 33% when compared to the second best baseline, and a gain of 26% when considering all the terms.

Selecting keywords to represent web pages using Wikipedia information

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Selecting keywords to represent web pages using Wikipedia information