  A multitude of factors are responsible for the overall quality of scientific
papers, including readability, linguistic quality, fluency,semantic complexity,
and of course domain-specific technical factors. These factors vary from one
field of study to another. In this paper, we propose a measure and method for
assessing the overall quality of the scientific papers in a particular field of
study. We evaluate our method in the computer science domain, but it can be
applied to other technical and scientific fields.Our method is based on the
corpus linguistics technique. This technique enables the extraction of required
information and knowledge associated with a specific domain. For this purpose,
we have created a large corpus, consisting of papers from very high impact
conferences. First, we analyze this corpus in order to extract rich
domain-specific terminology and knowledge. Then we use the acquired knowledge
to estimate the quality of scientific papers by applying our proposed measure.
We examine our measure on high and low scientific impact test corpora. Our
results show a significant difference in the measure scores of the high and low
impact test corpora. Second, we develop a classifier based on our proposed
measure and compare it to the baseline classifier. Our results show that the
classifier based on our measure over-performed the baseline classifier. Based
on the presented results the proposed measure and the technique can be used for
automated assessment of scientific papers.
