In our approach to lot zoning, we focus on identifying relevant text content areas, specifically targeting the selection of pages and tables that describe lot references and items.
The text filtering process is critical to ensure that we accurately classify relevant content for further processing, transitioning from raw text to categorized data suitable for analysis.
To effectively classify relevant pages, we implemented a Bag of Words (BOW) representation, employing language-independent features to enhance classification across different languages.
By systematically examining the frequency of domain-specific words within the BOW representation of a page, we can derive key metrics that inform the relevance of the text.
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