Authors of short papers such as letters or editorials often express complementary opinions, and sometimes contradictory
ones, on related work in previously published articles. The MEDLINE® citations for such short papers are required to
list bibliographic data on these "commented on" articles in a "CON" field. The challenge is to automatically identify the
CON articles referred to by the author of the short paper (called "Comment-in" or CIN paper). Our approach is to use
support vector machines (SVM) to first classify a paper as either a CIN or a regular full-length article (which is exempt
from this requirement), and then to extract from the CIN paper the bibliographic data of the CON articles. A solution to
the first part of the problem, identifying CIN articles, is addressed here. We implement and compare the performance of
two types of SVM, one with a linear kernel function and the other with a radial basis kernel function (RBF). Input
feature vectors for the SVMs are created by combining four types of features based on statistics of words in the article
title, words that suggest the article type (letter, correspondence, editorial), size of body text, and cue phrases.
Experiments conducted on a set of online biomedical articles show that the SVM with a linear kernel function yields a
significantly lower false negative error rate than the one with an RBF. Our experiments also show that the SVM with a
linear kernel function achieves a significantly higher level of accuracy, and lower false positive and false negative error
rates by using input feature vectors created by combining all four types of features rather than any single type.