Proper noun features on corpus level 1
This part of experiments constitutes the use of external proper noun lists. To overcome the miss-annotation between people name and place, we think of using a set of proper noun lists. People name and country lists are provided by the Revues.org article collection and place list is completed from top 3000 largest city list (http://www.mongabay.com/cities_pop_01.htm) and 215 livable city list (http://www.businessweek.com/interactive_reports/livable_cities_worldwide.html).
In fact, the previous study (Accurate Information Extraction from Research Papers using Conditional Random Fields, 2004) reports that the use of lexicon features does not influence much on the performance. But because of the uniqueness of our corpus that leads to some confusion between proper noun types (name and place), we expect that an appropriate use of proper noun lists would help the distinction of these two types.
As the first attempt to apply proper nouns in a CRF model, we simply use them as features like in the earlier study. Considering that the name list is far from enough, we separate the surname and forename. It means that for a given token, we first check if the token is in the surname list and then we check if a nearby token is in the forename list. In this way, even a full name separately found in the list can be detected. The place lists are simpler. We just search if the given token is in the lists.
We constructed four different models applying different lists as follows:
- People name list
- City list
- Country list + City list
- People name list + City list
In this version, we verified again the learning and test data and made some correction. In addition, the “PUNC” feature, which was ignored in the previous experiments, is counted.
As we can see in the above table, all the attempts to apply proper noun lists have been failed. The proper noun features may somewhat effective to detect the corresponding proper noun types however the incomplete lists rather reduce the overall annotation performance.
Consequently, we are now interested in developing a new method to insert these lists. Since the Revues.org data includes several useful additional lists, we expect to find an effective way to use them that would contribute to the annotation performance.