Signal and Data Processing پردازش علائم و داده‌ها JSDP Engineering & Technology http://jsdp.rcisp.ac.ir 1 admin 2538-4201 2538-421X 10.29252/jsdp 1 8888 fa jalali 1397 12 1 gregorian 2019 3 1 15 4 online 1 fulltext
fa برچسب‌گذاری ادات سخن زبان فارسی با استفاده از مدل شبکۀ فازی Part Of Speech Tagging of Persian Language using Fuzzy Network Model مقالات پردازش متن Paper پژوهشي Research <p dir="RTL" style="text-align: justify;"><span dir="RTL"><span style="font-family:b nazanin;"><span style="font-size:10.0pt;">برچسب‌گذاری ادات سخن یکی از مسائل مطرح در حوزۀ پردازش زبان‌های طبیعی است. هدف در این مسئله تعیین نقش واژگان در جمله است. برحسب این برچسب‌گذاری ویژگی‌های دستوری و نحوی واژگان نیز مشخص می‌شود. در این مقاله یک روش مبتنی بر آماری برای ادات سخن فارسی پیشنهاد شده است. در این روش محدودیت‌های روش‌های آماری با استفاده از معرّفی یک مدل شبکه فازی کاهش پیدا کرده است؛ به&shy;طوری&shy;که در&shy;صورت وجود تعداد کمی دادۀ آموزشی، مدل فازی پارامترهای قابل اطمینان‌تری را تخمین می‌زند. در این روش ابتدا هنجار‌سازی به‌عنوان پیش‌پردازش صورت گرفته و سپس فراوانی هر واژه با توجه به برچسب مربوطه به‌صورت یک تابع فازی تخمین زده و سپس مدل شبکه فازی &nbsp;تشکیل &shy;شده و درجۀ هر یال در این شبکه با استفاده از یک شبکۀ عصبی و تابع عضویت مشخص می‌شود. درنهایت بعد از این&shy;که مدل شبکۀ فازی برای یک جمله ساخته شد، از الگوریتم ویتربی</span></span></span> <span dir="RTL"><span style="font-family:b nazanin;"><span style="font-size:10.0pt;">برای تعیین محتمل‌ترین مسیر در این شبکه استفاده شده است. نتایج آزمایش روی پیکرۀ بی‌جن‌خان کارایی این روش را تأیید کرده و نشان می‌دهد که روش پیشنهادی در شرایطی که داده‌های آموزشی کم&shy;تری در اختیار باشد، از روش‌های مشابه، مثل مدل مخفی مارکوف عملکرد بهتری دارد.</span></span></span></p> <p class="REF" style="margin: 0in 0in 0pt 9pt; text-align: justify; unicode-bidi: embed; direction: ltr; -ms-text-justify: inter-ideograph; mso-list: none; tab-stops: .5in;"><strong>Part of speech tagging (POS tagging) is an ongoing research in natural language processing (NLP) applications. The process of classifying words into their&nbsp;<a name="parts_of_speech_index_term">parts of speech&nbsp;and labeling them accordingly is known as&nbsp;</a><a name="part_of_speech_tagging_index_term">part-of-speech tagging,&nbsp;</a><a name="pos_tagging_index_term">POS-tagging, or simply&nbsp;</a><a name="tagging_index_term">tagging. Parts of speech are also known as&nbsp;</a><a name="word_classes_index_term">word classes&nbsp;or&nbsp;</a><a name="lexical_categories_index_term">lexical categories</a>. The purpose of POS tagging is determining the grammatical category of the words in a sentence. Grammatical and syntactical features of words are determined based on these tags. </strong><span dir="RTL"></span><br> <strong>The function of existing tagging methods depends on the corpus. As if the educational and test data are extracted from a corpus, the methods are well-functioning, or if the number of educational data is low, especially in probabilistic methods, the accuracy level also decreases. The words used in sentences are often vague. For example, the word chr('39')Mahramichr('39') can be a noun or an adjective. Existing ambiguity can be eliminated by using neighbor words and an appropriate tagging method</strong><span dir="RTL">.</span><br> <strong>Methods in this domain are divided into several categories such as:based on memory , rule based methods , statistical , and neural network . The precision of more of these methods is an average of 95% . In the paper , using the TnT probabilistic tagging and smoothing and variations on the estimation of the three-words likelihood function, a tagging model has been created that has reached 96.7% in total on the Penn Treebank and NEGRA entities.  Using the representation of the dependency network and extensive use of lexical features, such as the conditional continuity of the sequence of words, as well as the effective use of the foreground in the linear models of linear logarithms and fine-grained modeling of the unknown words, on the Penn Treebank WSJ model, 97.24% accuracy is achieved.</strong><br> <strong>The first work in Farsi that has used the word neighborhoods and the similarity distribution between them. The accuracy of the system is 57.5%. In , a Persian open source tagger called HunPoS was proposed. This tag uses the same TnT method based on the Hidden Markov model and a triple sequence of words, and 96.9% has reached on the chr('39')chr('39')Bi Jen Khanchr('39')chr('39') corpus.</strong><span dir="RTL"></span><br> <strong>In this paper a statistical based method is proposed for Persian POS tagging. The limitations of statistical methods are reduced by introducing a fuzzy network model, such that the model is able to estimate more reliable parameters with a small set of training data. In this method, normalization is done as a preprocessing step and then the frequency of each word is estimated as a fuzzy function with respect to the corresponding tag. Then the fuzzy network model is formed and the weight of each edge is determined by means of a neural network and a membership function. Eventually, after the construction of a fuzzy network model for a sentence, the Viterbi algorithm as s subset of Hidden Markov Model (HMM) algorithms is used to specify the most probable path in the network.</strong><br> <strong>The goal of this paper is to solve a challenge of probabilistic methods when the data is low and estimation made by these models&nbsp; is mistaken</strong><span dir="RTL">.</span><br> <strong>The results of testing this method on ``Bi Jen Khanchr('39')chr('39') corpus verified that the proposed method has better performance than similar methods, like hidden Markov model, when fewer training examples are available. In this experiment, several times the data is divided into two groups of training and test with different sizes ascending. On the other hand, in the initial experiments, we reduced the train data size and, in subsequent experiments, increased its size and compared with the HMM algorithm.</strong><br> <strong>As shown in figure 4, the train and test set and are directly related to each other, as the error rate decreases with increasing the training set and</strong> <strong>vice versa</strong><span dir="RTL">.</span><strong> In tests, three</strong> <strong>criteria involving precision, recall and F1 have been used. In Table 4, the implementation of HMM models and a fuzzy network is compared with each other and the results are shown</strong><span dir="RTL">.</span><br> &nbsp;</p> <p class="MsoNormal" style="text-align: justify; unicode-bidi: embed; direction: ltr; -ms-text-justify: inter-ideograph;"><b><span .="" a="" according="" after="" aimed="" algorithm="" and="" are="" as="" be="" been="" built="" by="" class="MsoNormal" corresponding="" degree="" determine="" determined="" determining="" each="" edge="" estimated.="" farsi="" few="" field="" for="" formed="" frequency="" function="" fuzzy="" has="" have="" if="" in="" introducing="" is="" issues="" label="" language="" learning="" limitations="" membership="" methods="" model="" most="" natural="" network="" network.="" neural="" new="" normalization="" of="" one="" p="" parameters="" parts="" path="" performed="" phase="" probable="" processing.="" proposed="" reliable="" reprocessing="" role="" sentence="" so="" span="" speech="" statistical="" statistical-based="" style="font-size: 10pt;" tagging="" that="" the="" then="" there="" this="" times="" to="" used="" using="" was="" word="" words=""></span></b></p> پردازش زبان‌های طبیعی, برچسب‌زنی اجزای سخن, زبان فارسی, فازی, شبکۀ عصبی : Natural language processing, Part of speech (POS) tagging, Persian language, Fuzzy, Neural network 123 130 http://jsdp.rcisp.ac.ir/browse.php?a_code=A-10-941-1&slc_lang=fa&sid=1 mohammad badpeima محمد بادپیما badpeima.mohammad@chmail.ir `10031947532846007091` 10031947532846007091 No MUT دانشگاه مالک‌اشتر Fatemeh hourali فاطمه حورعلی hourali@esfarayen.ac.ir `10031947532846007092` 10031947532846007092 No Esfarayen مجتمع آموزش عالی اسفراین Maryam hourali مریم حورعلی maryam_hourali@yahoo.com `10031947532846007093` 10031947532846007093 Yes MUT دانشگاه مالک‌اشتر