Extracting Text Features Using Learning-To-Rank-Methods From The Perspective Of Information Retrieval
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AUTHOR(S)
YellepeddiVijayalakshmi, B. Arun Kumar, G. K. D. PrasannaVenkatesan
KEYWORDS
Information retrieval, Ranking, Text parsing, visual content, textual content
ABSTRACT
In recent times, retrieving relevant information from a huge amount of data has gained the attention of researchers. Diverse search systems are offered for this purpose; however, it should have the ability to attain the most appropriate search outcomes in accordance withuser query that fulfils user needs. Various techniques are also provided to retrieve information. Generally, in conventional search engines, text content is considered and images in the content may be violated. However, images in web pages are utilized for retrieving other appropriate images by evaluating their visual and textual content. Also, in conventional text-based search engines, appropriate images are retrieved with visual features by providing a textual query. Diverse search engines and systems are presented for easy access and retrieval of relevant multi-media content on a ranking basis. This ranking approach is based on textual content that is phrased from huge data with visual contents. So, this study provides an effectual ranking approach based on Text parsing from Multi-Source document (R-TPM) producing information retrieval by eliminating redundancy. Simulation is carried out in MATLAB environment; the proposed model shows better trade-off in contract with the existing IR approaches based on ranking.
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