1 research outputs found
Towards Constructing a Corpus for Studying the Effects of Treatments and Substances Reported in PubMed Abstracts
We present the construction of an annotated corpus of PubMed abstracts
reporting about positive, negative or neutral effects of treatments or
substances. Our ultimate goal is to annotate one sentence (rationale) for each
abstract and to use this resource as a training set for text classification of
effects discussed in PubMed abstracts. Currently, the corpus consists of 750
abstracts. We describe the automatic processing that supports the corpus
construction, the manual annotation activities and some features of the medical
language in the abstracts selected for the annotated corpus. It turns out that
recognizing the terminology and the abbreviations is key for determining the
rationale sentence. The corpus will be applied to improve our classifier, which
currently has accuracy of 78.80% achieved with normalization of the abstract
terms based on UMLS concepts from specific semantic groups and an SVM with a
linear kernel. Finally, we discuss some other possible applications of this
corpus.Comment: medical relation extraction, rationale extraction, effects and
treatments, bioNL