آزاده شاکری

Occurrence of physiological diseases such as coronary heart disease disrupts the cognitive and emotional distress of patients and makes them face emotional distress. According to this the objective of the present study was the structural model of emotional distress based on health-promoting behaviors and a sense of psychological coherence with the mediating role of the meaning of life in people with coronary heart disease. The research is applicative and the research method was correlational through structural equations modeling. The statistical population included people with coronary heart disease who referred to Bandar Abbas medical centers in the winter of 2022. 245 people with coronary heart disease were selected from the statistical population as the available sampling. The research instruments were emotional distress questionnaire (Kessler et al, 2002), health-promoting behaviors questionnaire (Walker et al, 1987), sense of psychological coherence questionnaire (Flensborg, Ventegodt, Merrick, 2006) and meaning of life questionnaire (Steger et al, 2006). The data analysis was conducted through structural equations modeling via SPSS and Amos versions 23. The results showed that Health-promoting behaviors has a significant direct effect on meaning of life (p < 0.05, β = 0.617) and emotional distress (p < 0.05, β = -0.482) of people with coronary heart disease and it was able to explain 38 and 23.2 percent of the variance of these variables, respectively. Also, sense of psychological coherence has a significant direct effect on meaning of life (β = 0.548, p < 0.05) and emotional distress (β = -0.538, p < 0.05) of people with coronary heart disease and was able to explain 30 and 28.9 percent of the variance of these variables, respectively. Finally, meaning of life had a significant direct effect on emotional distress (p < 0.05, β =-0.442) of people with coronary heart disease and was able to explain 19.5% of the variance of this variable. Meanwhile, meaning of life has a significant mediating role in the relationship between health-promoting behaviors and sense of psychological coherence with emotional distress in people with coronary heart disease (p<0.05). Considering the significant mediating role of meaning of life, it is possible to reduce the occurrence of emotional distresss in people with coronary heart disease by using effective interventions such as meaning of life training.

Patents play an important role in Intellectual Property protection. So, in recent years a considerable attention has been paid to patent and prior-art search. In process of patent application filing, searching in the previous patent database is of great importance. Patent examiners search in a huge database of patents to find if there exists any similarity between applicant’s claim and the previous registered patents. This process that called patent invalidity run, is one of the important stages of patent registration. Because of legal aspects of this process, the searcher should not leave any relevant patent document. So patent searching is essentially a recall-oriented issue in information retrieval applications. In recent years, the use of intelligent information retrieval in this search process has been investigated by many researchers. In this paper we investigate various methods of information retrieval that have been proven to be effective in retrieving relevant results. The survey also has focused on query formulation and how to transform a query patent into a search query. So we have explored different factors of a successful transformation, such as how many query words should be used, where to extract query words, how to weight them and whether to use noun-phrases instead of individual words. Furthermore, the survey covers researches that combine different features and has been proven to make a significant improvement in retrieval performance.

The goal in the named entity recognition task is to classify proper nouns of a piece of text into classes such as person, location, and organization. Named entity recognition is an important preprocessing step in many natural language processing tasks such as question-answering and summarization. Although many research studies have been conducted in this area in English and the state-of-the-art NER systems have reached performances of higher than 90 percent in terms of F1 measure, there are very few research studies on this task in Persian. One of the main important reasons for this may be the lack of a standard Persian NER dataset to train and test the NER systems. In this research we create a standard tagged Persian NER dataset which will be distributed freely for research purposes. In order to construct this standard dataset, we studied the existing standard NER datasets in English and came to the conclusion that almost all of these datasets are constructed using news data. Thus we collected documents from ten news websites in Persian. In the next step, in order to provide the annotators with guidelines to tag these documents, we studied the guidelines used for constructing CoNLL and MUC English datasets and created our own guidelines considering the Persian linguistic rules. Using these guidelines, all words in documents can be labeled as person, location, organization, time, date, percent, currency, or other (words that are not in any of these 7 classes). We use IOB encoding for annotating named entities in documents, like most of the existing English NER datasets. Using this encoding, the first token of a named entity is labeled with B, and the next tokens (if exist) are labeled with I. The words that are not part of any named entity are labeled with O. The constructed corpus, named PAYMA, consists of 709 documents and includes 302530 tokens. 41148 tokens out of these tokens are labeled as named entities and the others are labeled as O. In order to determine the inter-annotator agreement, 160 documents were labeled by a second annotator. Kappa statistic was estimated as 95% using words that are labeled as named entities. After creating the dataset, we used the dataset to design a hybrid system for named entity recognition. We trained a statistical system based on the CRF algorithm, and used its output as a feature to train a bidirectional LSTM recurrent neural network. Moreover, we used the k-means word clustering method to cluster the words and fed the cluster number of each word to the LSTM neural network. This form of combining CRF with neural networks and using the cluster number for each word is the novelty of this research work. Experimental results show that the final model can reach an F1 score of 87% at word-level and 80% at phrase level.

Learning to rank refers to machine learning techniques for training a model in a ranking task. Learning to rank has been shown to be useful in many applications of information retrieval, natural language processing, and data mining. Learning to rank can be described by two systems: a learning system and a ranking system. The learning system takes training data as input and constructs a ranking model. The ranking system then makes use of the learned ranking model for ranking prediction. In this paper, a new learning algorithm based on ensemble learning for learning ranking models in information retrieval is proposed. This algorithm iteratively constructs weak learners using a fraction of the training data whose weight distribution is determined based on previous weak learners. The proposed algorithm combines the weak rankers to achieve the final ranking model. This algorithm constructs a ranking model on a fraction of the training data to increase the accuracy and reduce the learning time. Experimental results based on Letor.3 benchmark dataset shows that the proposed algorithm significantly outperforms other ensemble learning algorithms.

Parallel corpora regard as rich linguistic resources for Natural Language Processing and Cross Language Information Retrieval tasks. It is usually needed to align sentences before using these valuable resources; however, sentence alignments are expensive in terms of time and cost. With development of the World Wide Web and free access to it, automatically building parallel corpus from the Web is desirable. In this paper, we first choose bilingual pages with parallel content to extract parallel sentence candidates. Then, by computing several features and learning a Maximum Entropy classifier, parallel sentences are extracted from the candidate sentences. Our approach is not dependent on specific domain and it can cover different domains in the Web.