One of the main challenges in the proximity models is the speed of data retrieval. These models define a distance concept which is calculated based on the positions of query terms in the documents. This means that finding the positions and calculating the distance is a More
One of the main challenges in the proximity models is the speed of data retrieval. These models define a distance concept which is calculated based on the positions of query terms in the documents. This means that finding the positions and calculating the distance is a time consuming process and because it usually executed during the search time it has a special importance to users. If we can reduce the number of documents, retrieval process becomes faster. In this paper, the SNTK3 algorithm is proposed to prune documents dynamically. To avoid allocating too much memory and reducing the risk of errors during the retrieval, some documents' scores are calculated without any pruning (Skip-N). The SNTK3 algorithm uses three pyramids to extract documents with the highest scores. Experiments show that the proposed algorithm can improve the speed of retrieval.
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Analysis of published news content is one of the most important issues in information retrieval. Much research has been conducted to analyze individual news articles, while most news events in the media are published in the form of several related articles. Event detect More
Analysis of published news content is one of the most important issues in information retrieval. Much research has been conducted to analyze individual news articles, while most news events in the media are published in the form of several related articles. Event detection is the task of discovering and grouping documents that describe the same event. It also facilitates better navigation of users in news spaces by presenting an understandable structure of news events. With rapid and increasing growth of online news, the need for search engines to retrieve news events is felt more than ever. The main assumption of event detection is that the words associated with an event appear in the same time windows and similar documents. Accordingly, in this research, we propose a retrospective and feature-pivot method that clusters words into groups according to semantic and temporal features. We then use these words to produce a time frame and a human readable text description. The proposed method is evaluated on the All The News dataset, which consists of two hundred thousand articles from 15 news sources in 2016 and compared to other methods. The evaluation shows that the proposed method outperforms previous methods in terms of precision and recall.
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