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| Content Provider | ACM Digital Library |
|---|---|
| Author | Stamou, Sofia Plegas, Yannis |
| Abstract | It is well-known that the web contains many duplicate and near-duplicate documents. Despite the efforts that have been put towards equipping search engines with duplicate detection algorithms, still there are cases where the documents retrieved in response to web queries contain redundant information. In this paper, we are concerned with effectively identifying and reducing redundant information in search results. In particular, we describe how we automatically detect content that is lexically and/or semantically duplicated across search results and we introduce a novel algorithm that upon the detection of significant (i.e., above a given threshold) content duplication, it filters out redundant information. Information filtering takes place in two-steps depending on whether we are dealing with documents of (nearly) identical lexical content or with documents of lexically distinct but semantically equivalent content. In the first case, our algorithm retains in the result list the document that is the most relevant to the query intention and removes duplicates. In the second case, our algorithm merges into a single text, which we call SuperText, the documents of redundant information in a way that every document contributes diverse semantic content to the generated SuperText. Additionally, the algorithm re-ranks the remaining documents based on their contextual relevance to the query intention. The experimental evaluation of our approach demonstrates that it is very effective in identifying lexical and semantic information redundancy across search results. In addition, we have found that our algorithm manages to filter out successfully content duplication from the results list and the SuperTexts it generates for reducing information redundancy are syntactically and semantically coherent texts. |
| Starting Page | 886 |
| Ending Page | 893 |
| Page Count | 8 |
| File Format | |
| ISBN | 9781450316569 |
| DOI | 10.1145/2480362.2480533 |
| Language | English |
| Publisher | Association for Computing Machinery (ACM) |
| Publisher Date | 2013-03-18 |
| Publisher Place | New York |
| Access Restriction | Subscribed |
| Subject Keyword | Ranking Semantics Shingling Removing redundant information Web search |
| Content Type | Text |
| Resource Type | Article |
National Digital Library of India (NDLI) is a virtual repository of learning resources which is not just a repository with search/browse facilities but provides a host of services for the learner community. It is sponsored and mentored by Ministry of Education, Government of India, through its National Mission on Education through Information and Communication Technology (NMEICT). Filtered and federated searching is employed to facilitate focused searching so that learners can find the right resource with least effort and in minimum time. NDLI provides user group-specific services such as Examination Preparatory for School and College students and job aspirants. Services for Researchers and general learners are also provided. NDLI is designed to hold content of any language and provides interface support for 10 most widely used Indian languages. It is built to provide support for all academic levels including researchers and life-long learners, all disciplines, all popular forms of access devices and differently-abled learners. It is designed to enable people to learn and prepare from best practices from all over the world and to facilitate researchers to perform inter-linked exploration from multiple sources. It is developed, operated and maintained from Indian Institute of Technology Kharagpur.
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