NEWordS A News Search Engine for English Vocabulary Learning

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 26 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 26 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.