of the sources: Quote and Y ahoo Quote**. For all other sources, if a field was successfully**
extracted, it was correctly identified in at least three, and in most cases almost all, of the pages in the set. A false positive occurred when the reinduction algorithm incorrectly identified some text on a page as a correct example of a data field. In many cases, false positives consisted of partial fields, *e.g.*, "Cloudy" rather than "Mostly Cloudy" (**yahoo weather**).

A false negative occurred when the algorithm did not identify any examples of a data field. We ran the reinduction experiment attempting to extract the **fields listed in Table 8. The** second column of the table lists the fractions of data sets for which the field was successfully extracted. We were able to correctly identify fields 277 times **across all data sets making** 61 mistakes, of which 31 were attributed to false positives and 30 to the false negatives.

There are several reasons the reinduction algorithm failed **to operate perfectly. In many**
cases the reason was the small training set.9 We can achieve better learning for the yellowpages-type sources Bigbook and Smartpages **by using more training examples (see Fig. 8).**
In two cases, the errors were attributable to changes in the format of data, which resulted in the failure of patterns to capture the structure of data correctly: *e.g.*, the airport **source**
changed airport names from capitalized words to allcaps, and in the Quote **source in which** the patterns were not able to identify negative price changes because they were learned for a data set in which most of the stocks had a positive price change. For two sources the reinduction algorithm could not distinguish between correct examples of the field and other examples of the same data type: for the Quote **source, in some cases it extracted** opening price or high price for the stock price field, while for the yahoo weather **source, it**
extracted high or low temperature, rather than the current temperature. This problem was also evident in the Smartpages **source, where the city name appeared in several places on**
the page. In these cases, user intervention or meta-analysis **of the fields may be necessary** to improve results of data extraction. Extracting with landmark-based rules **The final validation experiment consisted of**
using the automatically generated wrappers to extract data **from test pages. The last three** columns in Table 8 list precision, recall and accuracy for extracting data from test pages.

The performance is very good for most fields, with the notable exception of the *STATE* **field** of Bigbook **source. For that field, the pattern** <Allcaps> **was overly general, and a wrong**
group received the highest score during the scoring step of the reinduction algorithm. The average precision and recall values were P = 0.90 and R = 0.80.

Within the data set we studied, five sources, listed in Table 9, **experienced a total of seven**
changes. In addition to these sources, the airport **source changed the format of the data it**
returned, but since it simultaneously changed the presentation of data from a detail page to a list, we could not use this data to learn Stalker **rules. Table 9 shows the performance of**
the automatically reinduced wrappers for the changed sources. For most fields precision P, the more important of the performance measures, is close to its maximum value, indicating that there were few false positives. However, small values of recall indicate that not all examples of these fields were extracted. This result can be traced to a limitation of our approach: if the same field appears in a different context, more than one rule is necessary