There are 37 phonemes in SCK, including 8 vowels and 29 consonants [5]. This study uses the Hawar alphabet (standard Latin script for Northern Kurdish) with changes in some consonants. Table 1 compares IPA and the Standard Arabic alphabet of Kurdish with this study’s transcription of consonants.

As the syllable weight is the essential material in the identification of poem meter, we will discuss the SCK vowel’s length more precisely. Table 2 describes the details of Standard Central Kurdish vowels. The long vowels (/î, ê, a, o, û/) are shorter in final unstressed positions, and the short vowel /e/ in word-final positions can be pronounced longer [6]. The vowel /i/ (bizroke) is unstable in most environments [7] and does not have a grapheme in the standard Kurdish alphabet [5,8].

The Kurdish alphabet, which is adapted from the Perso-Arabic script, has ambiguities in three cases [5]:

In the syllable structure of Kurdish, the nucleus is always a vowel, and the onset is one or two consonants. In two-consonant onsets, the second consonant must be /w/ or /y/. Coda has zero to three consonants. Three-consonant coda is rare, occurs only in some dialects [9], and was not observed in our dataset of Kurdish poetry. Table 3 presents syllable types and their normal weight in SCK.

As mentioned, the Kurdish language is a collection of dialects whose speakers live in Iran, Iraq, Turkey, Syria, and parts of the Caucasus. Neighboring different nations has led Kurdish literature to enjoy the characteristics of different literature styles.

In classical literature of Kurdish, there are three categories of poetic works: “quantitative (Arudi) verse”, “beit (syllabic songs)” and “gorani (lyric songs)” [10]. Kurdish quantitative verses are an imitation of Arabic and especially Persian poetry [11], and in terms of meter, it is based on syllable weight, i.e., all lines of a poem have an equal number of syllables, repeating a pattern of light and heavy syllables [12]. Beit and gorani have “syllabic meter”. The syllabic or numerical meter is rooted in the ancient tradition of Iranian languages, and it has long existed among different ethnic groups in Iran [10]. There is evidence of a syllabic meter in pre-Islamic literature in the texts of the Zoroastrian and Manichaean rituals [10,13,14]. In a syllabic meter, the weight of the syllables and the place of stress do not affect the meter, and only the total number of syllables in each line is important.

Fixed-form poems in Kurdish consist of lines that have an equal number of syllables. In most of the forms, like ghazal and mathnawi, even lines rhyme; however, in some forms, like mukhammas, rhyming is different. In the modern literature of Kurdish, “free verse” is a new style that is not limited to a fixed form, and the number of syllables in each line may be different [15]. This study considers three types of Kurdish poems: Quantitative, Syllabic, and Free verses.

The principles of classification of quantitative meter in Kurdish are similar to Persian [12]. Experts of Persian poetry use the following traditional steps for the identification of meter in quantitative verses [19,21]:

If a pattern is repeated in all lines, the poem will be recognized as a quantitative verse, and that pattern is proposed as the poem’s meter. Otherwise, if all lines have an equal number of syllables, then the poem will be recognized as a syllabic verse of that number. Else, when lines have neither a consistent pattern nor an equal number of syllables, then the poem is free verse [12].

As far as the authors know, no research has been done on the automatic classification of Kurdish poetry. Considering the similarities between the Kurdish quantitative verses and classic Persian, Arabic, and Turkish ones, we will give a brief overview of the works done in these languages.

In the Arabic and Persian orthographies, short vowels are written only for kids or ritual texts. The absence of short vowels in poems is a challenge for the syllabification step [22]. Mojiri [23], Kurt & Kara [24], Alabbas et al. [25], and Abuata & Al-Omari [26] have considered preprocessing steps for insertion of short vowels (diacritizing) and turning the text into phonemic representation. For example, The Basrah system [25] converts the word like “سدّ” to “سَدْد” and “والشمس” into “وَشْشمس”. Mojiri [23] looks up the words that cannot be syllabified by the rules from a transliteration dictionary. Jafari Qamsari [27] relies on the distributive characteristics of Persian phonemes and by using poetic and phonetic rules, converts the input Persian couplet into light, heavy, and potentially heavy syllable string.

Recently, data-driven and machine-learning works have been done on Arabic and Persian meter classification. Yousef et al. [28] encode the input poem at the character level and directly fed it to the recurrent neural networks without feature handcrafting. Yousefi [29] finds the unwritten linking vowel (izafe) by convolutional neural networks. Al-shaibani et al. [30] by deep bidirectional recurrent neural networks classify the meter of Arabic poems without diacritizing. Abandah et al. [31] use recurrent neural networks with bidirectional long short-term memory cells for diacritizing the input Arabic poems.

A critical step in meter classification is the comparison with common patterns. Mojiri [23] and Yousefi [29] compare the poem with 31 common Persian patterns, Alabbas et al. [25] compare with 16 meters of Arabic, and Kurt & Kara [24] compares with 20 plain and 45 mixed Ottoman templates.

The efficiency of the rule-based works is varying. Mojiri [23] reports 65% precision, Jafarari Qamsari [27] accuracy of more than 98%, Alabbas et al. [25] precision higher than 96%.

The data-driven works have reported acceptable results. Yousefi [29] reports 92% of accuracy. Yousef et al. [28] report overall accuracy of 96.38%, Al-shaibani et al. [30] report more than 94% accuracy and Abandah et al. [31] report an average accuracy of 97.27%.

As the input of the proposed method is plain text, we perform the following normalization steps to prevent errors:

Next is the syllabification process. For orthographies like English, Arabic, and Central Kurdish that do not have a one-to-one correspondence between the alphabet letters and the phonemes of the language, there will be challenges for syllabification. In this study, we use the rule-based method of SCK grapheme-to-phoneme conversion presented by Mahmudi & Veisi [5], which converts the input text into a syllabified string of phonemes. This method also correctly merges the conjunction ‘و’ (and) with the previous word [5]. This merge (e.g., “تیر و کەوان” /tî.rû ke.wan/ and “پۆڵا و ئاسن” /po.ław ʔa.sin/) is required for the following scansion step.

For example, the following line by Nalî (1800–1877) will be normalized and syllabified as:

Inside the input poem, some words may occur more than once, therefore, we store the syllabified sequences of phonemes for each word to speed up the process of syllabification.

As vowel length is not distinctive in Kurdish, for preserving the meter in quantitative verses, sometimes, short vowels should be pronounced long and long ones short. There are some clues for recognizing syllable weight changes automatically:

For managing the uncertainties in syllable weights, considering the above clues, we generate possible weight sequence candidates. For example, in /ger nebexşê merhemî wesłî birînim karîye/, we have:

In the above example, for 5 syllables, there are 2 possible weights; therefore, 2^5 = 32 sequence candidates can be generated.

As the quantitative meters have more detailed and are harder to compose, we first examine the lines of the poem for detecting a quantitative pattern. In this step, for each line, we compute the Levenshtein edit distance of each syllables weight sequence candidate with 27 common meter patterns (presented in Table 5). For example, if a line of the poem has 32 weight sequence candidates, we must calculate 32×27 = 864 edit distances. Since the strings are less than 20 characters long and contain only two characters (˘ and ˉ), these calculations are done quickly.

For each line, we only store candidate-pattern pairs that have the smallest distances below a maximum acceptable distance (given 4). For example, for /ger nebexşê merhemî wesłî birînim karîye/, among 864 pairs, only 62 pairs are acceptable, and one of these 62 pairs is:

The meter classification of a Kurdish poem, just by one or two lines is not correct at all the times, because:

Therefore, in our proposed method, we consider all lines of the poem together. For each acceptable pair from the previous step, we add up to the score of the corresponding pattern for the whole poem. Eventually, there is a score for each common meter pattern. For example, if the pattern ˘ˉˉˉ/˘ˉˉˉ/˘ˉˉ has a small distance with most of the lines, its score will be higher. The pattern with the highest score is the most probable quantitative pattern of the poem; i.e., we define: P=argmaxpj∈M∑i=1n(MaxDist−Dist(pj,wi)) (1)

In which, P is the most probable quantitative pattern of the poem, M is the set of common metrical patterns, n is the number of lines of the poem, Dist(p_j,w_i) is the edit distance of a pattern (p_j) and weight sequence of a line (w_i), MaxDist is the maximum acceptable edit distances (given 4).

Sometimes the score of the winner pattern is close to another one and the victory is not decisive. Therefore, the most probable pattern should be regulated by a confidence criterion, according to the following formula: Confidence=HighestScoreMaxDist×LinesCount (2)

The poem must have the following conditions to be recognized as a “quantitative verse”:

If a poem fulfills only the first condition, the proposed method assigns it as a “syllabic verse” of the statistical mode of syllables count of lines. Else, if none of the above conditions are fulfilled with the poem, it will be classified as a “free verse”.

We evaluated our proposed method on a dataset consisting of 1,154 Central Kurdish poems (979 quantitative, 130 syllabic, and 45 free verses) from available poems of “VejinBooks” (available at https://books.vejin.net). This website is a growing free online corpus of Kurdish literary texts in different dialects of Kurdish. The type and meter of all poems in this corpus are specified manually. VejinBooks also has statistics about the frequency of each meter available in the corpus. Among the available texts on the website, we chose only poems with more than three couplets from 12 well-known poets of Central Kurdish. Table 6 shows the overall statistics of the dataset. The dataset is available on GitHub at https://github.com/AsoSoft/Vejinbooks-Poem-Dataset (reference number 4079471).

The use of Arabic or Persian phrases (like Arabic Quranic Verses) within the text is a common convention in Kurdish poetry. Since our method is based on Central Kurdish phonology, this can be a problem for the evaluation. Fortunately, in the VejinBooks corpus, non-Kurdish phrases are tagged. We removed all the lines with a non-Kurdish phrase inside the test dataset.

We evaluated our method in type (quantitative, syllabic, or free) and pattern classification. The evaluation metrics are precision, recall, and F1-score. In Table 7, we show the results of the poem-type classification.

Table 8 indicates the test results for pattern classification. It shows the efficiency of the proposed method for each pattern. The recall for patterns that have “فعلاتن” (˘˘ˉˉ) or “فعلن” (˘˘ˉ) feet, like “فعلاتن مفاعلن فعلن”, is low. The method often classifies the poems of these patterns as syllabic. It causes a lower classification precision for the syllabic type, as shown in Table 7. Maybe the reason is that finding and matching words in the poem with two adjacent light syllables at the start of feet is hard in Kurdish. Therefore, poets consider using poetic licenses to preserve the meter.

Fig 2 shows the test results for metrical pattern classification, separated by authors. It can be speculated how much a poet complies with the patterns and uses fewer poetic licenses. For example, Herdî is known for having few but admirable poems. The lower accuracy for the poems of Hêmin and Ḧacî Qadir is due to using patterns with “فعلاتن” (˘˘ˉˉ) or “فعلن” (˘˘ˉ) feet and using more poetic licenses.