A scalable comparison-shopping agent for the World-Wide Web
AGENTS '97 Proceedings of the first international conference on Autonomous agents
Probe, count, and classify: categorizing hidden web databases
SIGMOD '01 Proceedings of the 2001 ACM SIGMOD international conference on Management of data
Automated discovery of search interfaces on the web
ADC '03 Proceedings of the 14th Australasian database conference - Volume 17
Mining Web Pages for Data Records
IEEE Intelligent Systems
Fully automatic wrapper generation for search engines
WWW '05 Proceedings of the 14th international conference on World Wide Web
Postal Address Detection fromWeb Documents
WIRI '05 Proceedings of the International Workshop on Challenges in Web Information Retrieval and Integration
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
A comparison of techniques for estimating IDF values to generate lexical signatures for the web
Proceedings of the 10th ACM workshop on Web information and data management
Blog post and comment extraction using information quantity of web format
AIRS'08 Proceedings of the 4th Asia information retrieval conference on Information retrieval technology
| Hi-index | 0.00 |
When a query is submitted to a search engine, the search engine returns a dynamically generated result page that contains the number of hits (i.e., the number of matching results) for the query. Hit number is a very useful piece of information in many important applications such as obtaining document frequencies of terms, estimating the sizes of search engines and generating search engine summaries. In this paper, we propose a novel technique for automatically identifying the hit number for any search engine and any query. This technique consists of three steps: first segment each result page into a set of blocks, then identify the block(s) that contain the hit number using a machine learning approach, and finally extract the hit number from the identified block(s) by comparing the patterns in multiple blocks from the same search engine. Experimental results indicate that this technique is highly accurate.