Information extraction systems apply machine learning to the task. These systems differ in how the IE problem characterized and in the style of text that they handle. The most important tasks in information extraction from the web are understanding webpage structure and its organization as many web sites contain large collections of pages displayed using a common template or layout,which makes it increasingly difficult to discover relevant data about a specific topic. Extracting data from such template pages has become an important issue in recent days as the number of web pages available on the Internet has growing in day by day. Tools and protocols to extract all this information have now come in demand as researchers and web surfers want to discover new knowledge at an ever increasing rate. A web crawler also known as, a robot or a spider is a system for the bulk downloading of web pages, whereas the goal of a focused crawler is seeking pages that are relevant to a pre-defined set of topics from a specific web resource. Collecting and indexing those accessible web documents,which can answer all ad-hoc queries, a focused crawler analyzes its crawl boundary to find the links that are likely to be most relevant for the crawl, and avoids irrelevant regions of the Web. Since all search engines take their data fed using crawlers, it is critical to improve its working ability. As the size of data is huge,Common Crawlers are no longer applicable in real life. So there is need to develop a domain specific crawler builds on stock of existing algorithms. This led to considerable savings in hardware and network resources, and helps keep the crawl more up-to-date. This paper proposed a novel framework called SWNLP, which enables bidirectional integration of page structure understanding and text understanding in an iterative manner. We have applied the proposed framework to the judgments information system to extract text of judgments and relate the similarity measures.
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