  The biological literature is rich with sentences that describe causal
relations. Methods that automatically extract such sentences can help
biologists to synthesize the literature and even discover latent relations that
had not been articulated explicitly. Current methods for extracting causal
sentences are based on either machine learning or a predefined database of
causal terms. Machine learning approaches require a large set of labeled
training data and can be susceptible to noise. Methods based on predefined
databases are limited by the quality of their curation and are unable to
capture new concepts or mistakes in the input. We address these challenges by
adapting and improving a method designed for a seemingly unrelated problem:
finding alignments between genomic sequences. This paper presents a novel and
outperforming method for extracting causal relations from text by aligning the
part-of-speech representations of an input set with that of known causal
sentences. Our experiments show that when applied to the task of finding causal
sentences in biological literature, our method improves on the accuracy of
other methods in a computationally efficient manner.
