In this paper, we propose a web document ranking method using topic modeling for effective information collection and classification. The proposed is applied to the technique avoid duplicated crawling when at high speed. Through technique, it feasible remove redundant documents, classify documents efficiently, confirm that crawler service running. enables rapid of many documents; user can searc...

Inverted indexes continue to be a mainstay of text search engines, allowing efficient querying large document collections. While there are number possible organizations, document-ordered the most common, since they amenable various query types, support index updates, and allow for dynamic pruning operations. One disadvantage with is that high-scoring documents can distributed across identifier ...

Abstract Ranking models are the main components of information retrieval systems. Several approaches to ranking based on traditional machine learning algorithms using a set hand-crafted features. Recently, researchers have leveraged deep in retrieval. These trained end-to-end extract features from raw data for tasks, so that they overcome limitations A variety been proposed, and each model pres...

Abstract BERT-based ranking models are emerging for its superior natural language understanding ability. All word relations and representations in the concatenation of query document modeled self-attention matrix as latent knowledge. However, some knowledge has none or negative effect on relevance prediction between document. We model observable unobservable confounding factors a causal graph p...

Text readability is typically defined in terms of “grade level”; the expected educational level of the reader at which the text is directed. Mechanisms for measuring readability in English documents are well established; however this is not in case in many other languages, such as syllabic alphabetic languages. In this paper seven different mechanisms for assessing the readability of syllabic a...