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Using example keyword lists


Applies to: Forefront Protection for SharePoint

Topic Last Modified: 2010-01-27

To aid you in filtering for profanity, example keyword lists in various languages are included with Microsoft Forefront Protection 2010 for SharePoint (FPSP).

If you want to install one or more of these lists, follow these steps.

To use the example lists
  1. Either begin the creation of a new keyword filter list or edit an existing keyword filter list.

  2. On the Filter Details dialog box, click Import.

  3. In the file Explorer window, navigate to the directory that contains the example keyword lists. By default, they are in the FPSP data folder, in a subfolder called Example Keywords. For the location of the default FPSP data folder on your operating system, see Default folders. To display the lists, change the file type from Text files (*.txt) to Encrypted keyword files (*.ekf).

  4. You are presented with a list of available files, each for a different language. You can select a single file to import. (You may import multiple files into a single keyword filter list, but each must be imported separately.)

  5. Click Open to import the selected file into your filter list.

  6. You can edit or delete any of the items in the list. To edit items in the list, double-click the item, edit the item, and then press ENTER. To delete items from the list, select the item and then click Remove.

  7. Finish creating or editing your filter list, and then click Save.

It is your responsibility to visually inspect all of the selected files to determine if there are words that are completely harmless in your environment, especially if you are using multiple language files. You must review the imported list and decide if you are going to eliminate any word clashes. If a certain word is unacceptable in one language but harmless in another, you must determine what is more important to you: catching everything (the default, if you accept all the words in all the selected lists) at the risk of false positives or risk not detecting something by deleting words from the list (which avoids those false positives).
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