  Pronoun resolution is a challenging subset of an essential field in natural
language processing called coreference resolution. Coreference resolution is
about finding all entities in the text that refers to the same real-world
entity. This paper presents a hybrid model combining multiple rulebased sieves
with a machine-learning sieve for pronouns. For this purpose, seven
high-precision rule-based sieves are designed for the Persian language. Then, a
random forest classifier links pronouns to the previous partial clusters. The
presented method demonstrates exemplary performance using pipeline design and
combining the advantages of machine learning and rulebased methods. This method
has solved some challenges in end-to-end models. In this paper, the authors
develop a Persian coreference corpus called Mehr in the form of 400 documents.
This corpus fixes some weaknesses of the previous corpora in the Persian
language. Finally, the efficiency of the presented system compared to the
earlier model in Persian is reported by evaluating the proposed method on the
Mehr and Uppsala test sets.
