The following output was produced by TextLadder using the regular defaults (maximum # words per text = 25, minimum # encounters = 5, etc.). The first set of output is the result of TextLadder 2.0's analysis of Voice of America simplified news texts. Note that this is a different analysis than the one described in the article. The second set of output is the result of an analysis of economics news texts.


For short texts (300 - 1000 words):
       Sequence List
       Pre-Reading List
       Included Low-Frequence Words


For longer texts (1000 - 1700 words):
       Sequence List
       Pre-Reading List
       Included Low-Frequence Words






This next piece of output is the list of high-frequency non-list words obtainable by selecting the "Use TextLadder to generate a list of high- frequency non-list words" option in TextLadder. In this case, the option was selected while processing a large number of economics texts. As the Nation1000, Nation2000, and AWL lists were all chosen, the list of high-frequency non-list words below represents all the high- frequency words found in these texts that are not in these 3 lists.

High-Frequency Non-List Words (Economics)
As you can see, both frequency and range information is given about each word. You can use this information to compile your own "rough and ready" domain-specific word list. Below is an economics domain- specific word list that I have compiled on the basis of the list of high-frequency non-list words above:

Economics Domain Specific List

(Note: When you are building your own lists for use with TextLadder, it is important that all headwords be on the left margin - i.e. with no spaces between them and the margin - and that the non-headwords have either a space or a tab separating them from the left margin.)




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Sina Ghadirian 2001