جستجوی مقالات مرتبط با کلیدواژه "فضای برداری" در نشریات گروه "کتابداری و مدیریت اطلاعات"

Since 2001s (1380s according to the Iran’s solar calendar), the increasing rate of writing and publishing scientific articles in Iran has become very intense. This caused in addition to the governmental organizations, such as Irandoc & the National Library and Archives of the Islamic Republic of Iran, numerous other online systems, such as the General Portal of Humanities, Noormags, Magiran, Elmnet, Civilica, etc, to manage knowledge and to provide structured archives of the scientific documents. Each of these archives provides facilities to the user. One of these facilities is searching on the documents. An accurate search can greatly improve the usage of these online systems. To increase the accuracy of the search result, it is necessary to determine the scientific field of articles. Classifying large volumes of scientific resources in different fields is very time-consuming. Using machinery methods can be a solution to reduce the severity of the task.The main contribution of this paper is to provide a classification model to classify Persian scientific articles. Although in previous studies, the classification task has been mainly used for simple texts, in this study, the neural network-based classification models, such as convolutional and perceptron neural networks, are used with the contextualized semantic representation, such as ParsBERT; and the results are compared with the other common method utilized for vectorization, namely Word2Vec. To this end, we use the data from the General Portal of Humanities, which includes various articles in the Humanities and each article contains the label of the field. One of the neural network characteristics is that a set of hidden features from the data in the vector space is created and used to train the model. According to the experimental results, the Perceptron classifier that utilized ParsBERT representation obtained the highest performance which is 74.71% based on the Micro F-score, and 72.55% based on the Macro F-score.

A word is the smallest unit in the language that has 'form' and 'meaning'. The word might have more than one meaning in which its exact meaning is determined according to the context it is appeared. Collecting all words’ senses manually is a tedious and time consuming task. Moreover, it is possible that the words’ meanings change over time such that the meaning of an existing word will become unusable or a new meaning will be added to the word. Computational methods is one of the approaches used for identifying words’ senses with respect to the linguistic contexts.In this paper, we put an effort to propose an algorithm to identify senses of Persian words automatically without a human supervision. To reach this goal, we utilize the word embedding method in a vector space model. To build words’ vectors, we use an algorithm based on the neural network approach to gather the context information of the words in the vectors. In the proposed model of this research, the divisive clustering algorithm as one of hierarchical clustering algorithms fits with the requirements of our research question. In the proposed model, two modes, namely the Sentence-based and the Context-based, are introduced to identify words’ senses. In the Sentence-based mode, all of the words in a sentence that contain the target word are involved to build the sentence vector; while in the Context-based mode, only a limited number of surrounding words of the target word is involved to build the sentence vector. Two evaluation methods, namely internal and external, are required to evaluate the performance of the clustering algorithm. The silhouette score for each cluster is computed as the internal evaluation metric for both modes of the proposed model. The external evaluation requires a gold standard data for which a data set containing 20 ambiguous words and 100 sentences for each target word is developed.According to the obtained results of the internal evaluation, the Sentence-based mode has higher density of clusters than the Context-based mode, and the difference between them is statistically significant. According to the V- and F-measure evaluation metrics in the external evaluation, the Context-based mode has obtained higher performance against the baselines with statistically significant difference.