Detection of semantic contradictory sentences is one of the most challenging and fundamental issues for NLP applications such as recognition of textual entailments. Contradiction in this study includes different types of semantic confrontation, such as conflict and antonymy. Due to lack of sufficient data to apply precise machine learning and specifically deep learning methods to Persian and other low resource languages, rule-based approaches that can function similarly to these systems will be of a great interest. Also recently, emergence of new methods such as transfer learning, has opened up the possibility of deep learning for low-resource languages. Considering two above points, in this study, along with a simple rule-base baseline, a novel rule-base system for identifying semantic contradiction along with a Bert base deep contradiction detection system for Persian texts have been introduced. The rule base system has used frequent rule mining method to extract appropriate contradiction rules using a development set. Extracted rules are tested for different categories of contradictory sentences. In this system the maximum f-measure among contradiction categories is obtained for negation about 90% and the average F-measure of system for all classes is about 76% which outperforms other algorithms on Persian texts. On the other hand, because of medium performance of rule base system for some categories of contradiction, we use a Bert base deep learning system using our translated dataset; with average F-measure of 73. Our hybrid system has f-measure of about 80.

翻译：检测语义自相矛盾的句子,是NLP应用中最具有挑战性和根本性的问题之一,例如承认文本的必然结果。本研究的矛盾包括不同类型的语义对抗,例如冲突和反调。由于缺乏足够的数据,无法对波斯语和其他低资源语言应用精确的机器学习,特别是深层次的学习方法,因此,与这些体系相似的基于规则的方法将引起极大的兴趣。此外,最近出现了新的方法,例如转移学习,为低资源语言的深层次学习开辟了可能性。考虑到以上两点,本研究中的上述两点,加上简单的规则基线,采用了一种新的规则基础系统,用以识别语义上的矛盾,同时对波斯文本采用伯特基础的深刻矛盾探测系统。由于规则基础系统经常使用规则采矿方法,利用一套开发集来找出适当的矛盾规则。抽取的规则为不同类别的自相矛盾的句子进行了测试。在这个系统中,为否定大约90%的自相矛盾的类别规定了最高法度,而所有类别的系统的平均F度测量法度是大约76 %,这种系统超越了我们平均波斯文本的系统,而我们用普通的法底的法系,因为我们用了一个基础学习了我们的标准。