Facet Mining for Web Result Re-ranking |
Author(s): |
| Swami N.M. , SVPM?s C.O.E. Malegaon, 413115, Savitribai Phule, Pune University, Maharashtra, India; Swami N. M., SVPM?s C.O.E. Malegaon, 413115, Savitribai Phule, Pune University, Maharashtra, India; Kenjale S. M., SVPM?s C.O.E. Malegaon, 413115, Savitribai Phule, Pune University, Maharashtra, India; Gore T. C., SVPM?s C.O.E. Malegaon, 413115, Savitribai Phule, Pune University, Maharashtra, India; Chirme S. R., SVPM?s C.O.E. Malegaon, 413115, Savitribai Phule, Pune University, Maharashtra, India |
Keywords: |
| Query Facet, Faceated Search, User Intent and Summarization |
Abstract |
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Now days, the matter of finding question aspects that area unit multiple teams of words or phrases that designate and summarize the content coated by a query [1]. The necessary aspects of a question area unit typically given and continual within the query high retrieved documents within the variety of lists, and question aspects may be deep-mined out by aggregating these vital lists. Systematic answer, that system visit as QDMiner, to mechanically mine question aspects by extracting and grouping frequent lists from free text, HTML tags, and repeat regions inside high search results[2].Query results area unit accessed from Google by exploitation Google API. Experimental results show that an outsized variety of lists do exist and helpful question aspects may be deep-mined by QDMiner. Analyse the matter of list duplication, and realize higher question aspects may be deep-mined by modelining fine-grained similarities. |
Other Details |
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Paper ID: IJSRDV4I110390 Published in: Volume : 4, Issue : 11 Publication Date: 01/02/2017 Page(s): 709-711 |
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