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NEWordS: A News Search Engine for English Vocabulary Learning
[Full Text]
AUTHOR(S)
Xuejing Huang, Sushma Chandra Reddy
KEYWORDS
Special Purpose Search Engine, Vocabulary Learning, News Retrieval, data mining
ABSTRACT
Vocabulary is the first hurdle for English learners to over- come. Instead of simply showing a word again and again, we come up with an idea to develop an English news article search engine based on users word-reciting record on Shanbay.com. It is designed for advanced English learners to find suitable reading materials. The search engine consists of Crawling Module, Document Normalizing module, Indexing Module, Querying Module and Interface Module. We propose three sorting & ranking algorithms for Querying Module. For the basic algorithm, five crucial principles are taken into consideration. Term frequency, inverse document frequency, familiarity degree and article freshness degree are factors in this algorithm. Then we think of a improved algorithm for the scene in which a user read multiple articles in the searching result list. Here we adopt a iterative & greedy method. The essential idea is to select English news articles one by one according to the query, meanwhile dynamically update the unfamiliarity of the words during each iterative step. Moreover, we develop an advanced algorithm to take article difficulty in to account. Interface Module is designed as a website, meanwhile some data visualization technologies (e.g. word cloud) are applied here. Furthermore, we conduct both applicability check and performance evaluation. Metrics such as searching time, word-covering ratio and minimum number of articles that completely cover all the queried vocabulary are randomly sampled and profoundly analyzed. The result shows that our search engine works very well with satisfying performance.
REFERENCES
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