By Mark Watson
Whereas net 2.0 used to be approximately facts, internet 3.0 is ready wisdom and knowledge. Scripting Intelligence: net 3.0 details collecting and Processing bargains the reader Ruby scripts for clever info administration in an internet 3.0 environment—including details extraction from textual content, utilizing Semantic internet applied sciences, details accumulating (relational database metadata, net scraping, Wikipedia, Freebase), combining info from a number of assets, and methods for publishing processed details. This e-book may be a precious instrument for an individual wanting to assemble, procedure, and put up net or database info around the glossy internet environment.
* textual content processing recipes, together with speech tagging and automated summarization
* accumulating, visualizing, and publishing info from the Semantic Web
* details amassing from conventional resources akin to relational databases and websites
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Extra info for Scripting Intelligence: Web 3.0 Information Gathering and Processing
With regard to spell-checking, I provided a script for using GNU Aspell in Ruby, which includes functions for getting a list of suggestions and getting only the most likely spelling correction. I showed you how to remove invalid text from binary files, process “noisy” text by removing unwanted characters, and discard all string tokens that are not in a spelling dictionary. The Ruby patp)naokqn_a gem that I’ve included with source code for this book (downloadable from the Apress web site) integrates the cleanup and sentence-segmentation code snippets and methods that were developed in this chapter.
Recognizing and Removing Noise Characters from Text In this section, I’ll show you how to remove valid text from binary files. If document files are properly processed, you shouldn’t get any noise characters in the extraction. ) However, it is a good strategy to have tools for pulling readable text from binary files and recovering text from old word-processing files. Another reason you’d want to extract at least some valid text from arbitrary binary files is if you must support search functionality.
A disadvantage of this approach is that extracted text will not contain “words” that are product numbers, product names, and the like. This is a real shortcoming if the extracted text is indexed for a search engine; a user searching for a product name, for example, probably won’t get any search results. One applicationspecific way to work around this problem is to include application-specific names in a custom word dictionary. For our purposes, a spelling dictionary is a large text file from which you will extract all unique words.
Scripting Intelligence: Web 3.0 Information Gathering and Processing by Mark Watson