In recent years, Question Answering systems have become more popular and widely used by users. Despite the increasing popularity of these systems, their performance is not even sufficient for textual data and requires further research. These systems consist of several parts that one of them is the Answer Selection component. This component detects the most relevant answer from a list of candidate answers. The methods presented in previous researches have attempted to provide an independent Model to undertake the answer-selection task. An independent Model cannot comprehend the syntactic and semantic features of questions and answers with a small training dataset. To fill this gap, language Models can be employed in implementing the answer selection part. This action enables the Model to have a better understanding of the language in order to understand questions and answers better than previous works. In this research, we will present the 'BAS' stands for BERT Answer Selection that uses the BERT language Model to comprehend language. The empirical results of applying the Model on the TrecQA Raw, TrecQA Clean, and WikiQA datasets demonstrate that using a robust language Model such as BERT can enhance the performance. Using a more robust classifier also enhances the effect of the language Model on the answer selection component. The results demonstrate that language comprehension is an essential requirement in natural language processing tasks such as answer selection.

In today's information age, efficient document ranking plays a crucial role in information retrieval systems. This article proposes a new approach to document ranking using embedding Models, with a focus on the BERT language Model to improve ranking results. The proposed approach uses vocabulary embedding methods to represent the semantic representations of user queries and document content. By converting textual data into semantic vectors, the relationships and similarities between queries and documents are evaluated under the proposed ranking relationships with lower cost. The proposed ranking relationships consider various factors to improve accuracy, including vocabulary embedding vectors, keyword location, and the impact of valuable words on ranking based on semantic vectors. Comparative experiments and analyses were conducted to evaluate the effectiveness of the proposed relationships. The empirical results demonstrate the effectiveness of the proposed approach in achieving higher accuracy compared to common ranking methods. These results indicate that the use of embedding Models and their combination in proposed ranking relationships significantly improves ranking accuracy up to 0. 87 in the best case. This study helps improve document ranking and demonstrates the potential of the BERT embedding Model in improving ranking performance.

Seljuk Pretrained language Models are very important because of their application in issues related to natural language processing. Language Models such as BERT have become more popular among researchers. Due to the focus of these language Models on English, other languages ​​are limited to some multilingual Models. In this article, the PersianSportBERT language Model is presented for the purpose of Persian sports analysis in topics related to this linguistic field. This language Model is based on the BERT language Model and was trained using the collected dataset. Three problems were used to evaluate the new language Model: sentiment analysis, named entity recognition and text infilling. In order to train this language Model, due to the lack of a suitable dataset, a wide range of sports events and news in the Persian language was prepared from several online sources. Due to the specialization of this Model and compared to the language Models presented for the Persian language, this Model provided better results in all three problems. This Model had the best performance with 71. 7% and 95. 2% in text infilling and named entity recognition, respectively. In sentiment analysis, the sports Model presented better results. These findings demonstrate that using a language Model related to any specialized field will have better results compared to language Models related to the general field of texts.

Using the context and order of words in sentence can lead to its better understanding and comprehension. Pre-trained language Models have recently achieved great success in natural language processing. Among these Models, The BERT algorithm has been increasingly popular. This problem has not been investigated in Persian language and considered as a challenge in Persian web domain. In this article, the embedding of Persian words forming a sentence was investigated using the BERT algorithm. In the proposed approach, a Model was trained based on the Persian web dataset, and the final Model was produced with two stages of fine-tuning the Model with different architectures. Finally, the features of the Model were extracted and evaluated in document ranking. The results obtained from this Model are improved compared to results obtained from other investigated Models in terms of accuracy compared to the multilingual BERT Model by at least one percent. Also, applying the fine-tuning process with our proposed structure on other existing Models has resulted in the improvement of the Model and embedding accuracy after each fine-tuning process. This process will improve result in around 5% accuracy of the Persian web ranking.