Unni Cathrine Eiken February 2005

Figure 7
Interestingly enough, however, the POS-based list of structures proved to be just as well suited
as the EPAS list for subsequent classification using TiMBL. When training and testing the
classifier on the POS-based structures, it assigned the correct antecedent in 57,69% (15/26) of
the test cases. In comparison, the EPAS classifier performed with an accuracy of 57,69% when
trained and tested on argument 1, and with an overall accuracy of 46,87%.
These results are interesting mainly because they show that for the purposes of using a memory
based classifier, an extraction method based on a syntactic parser does not necessarily provide
better results than a POS-tagger based method. Even though the list of extracted structures was
decidedly poorer than the EPAS list, especially because it contained “wrong” information in the
sense that logical objects were listed as subjects by virtue of their syntactic role, it provided
useful input for the classification process. It is, however, as suggested by the FCA diagram
above, likely that the POS-based list would be of less use for the concept association phase,
since this approach relies on the presence of similar entities in similar positions in the structures.
As a conclusion, it can probably be stated that the advantages of using a syntactic parser in the
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