توجه: محتویات این صفحه به صورت خودکار پردازش شده و مقاله‌های نویسندگانی با تشابه اسمی، همگی در بخش یکسان نمایش داده می‌شوند.
۱Authorship Attribution in Persian Scripts Using Neural Networks
اطلاعات انتشار: دوازدهمین کنفرانس سالانه انجمن کامپیوتر ایران، سال
تعداد صفحات: ۴
Authorship Attribution is an interesting problem with many applications like Fraud detection, email classification , deciding the authorship of famous documents like the bible, attributing authors to pieces of texts in collaborative writing, and software forensics. Authorship attribution attempts to recognize authors of different scripts based on their writing style. In this paper, four parsian bloggers have been selected and then using their writing style and features, we have tried to apply a neural network based solution for authorship attribution problem among them. A couple of experiments have been designed for evaluating performance of this neural network and results are reported.<\div>

۲Impact of Topic Modeling on Rule–Based Persian Metaphor Classification and its Frequency Estimation
اطلاعات انتشار: International Journal Information and Communication Technology Research، هفتم،شماره۲، spring ۲۰۱۵، سال
تعداد صفحات: ۸
The impact of several topic modeling techniques have been well established in many various aspects of Persian language processing. In this paper, we choose to investigate the influence of Latent Dirichlet Allocation technique in the metaphor processing aspect and show this technique helps measure metaphor frequency effectively. In the first step, we apply LDA on Persian or so–called Bijankhan corpus to extract classes containing the words which share the most natural semantic proximity. Then, we develop a rule–based classifier for identifying natural and metaphorical sentences. The underlying assumption is that the classifier allocates a topic for each word in a sentence. If the overall topic of the sentence diverges from the topic of one of the words in the sentence, metaphoricity is detected. We run the classifier on whole the corpus and observed that roughly at least two and at most four sentence in the corpus carries metaphoricity. This classifier with an f–measure of 68.17% in a randomly 100 selected sentences promises that a LDA–based metaphoricty analysis seems efficient for Persian language processing.
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