Automated Metric Analysis of Spanish Poetry: Two Complementary Approaches

Marco, Guillermo; Pérez, Javier de la Rosa; Gonzalo, Julio; Ros, Salvador Cardús i; González-Blanco, Elena

doi:10.1109/access.2021.3069635

Cited by 5 publications

(3 citation statements)

References 16 publications

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“…Their approach achieved considerable accuracy, although the corpus focused on was still limited to classical sonnets. Other researchers have also proposed methods for using artificial intelligence to study Spanish poetry, but this research was carried out from the perspective of linguistics, such as consideration of prosody or the rhythm of the poetry [22,23]. All the aforementioned studies have provided technical and ideological inspiration that has contributed to the development of the current research.…”

Section: Spanish Text Classificationmentioning

confidence: 99%

An Artificial-Intelligence-Driven Spanish Poetry Classification Framework

Deng,

Wang,

Wang

et al. 2023

BDCC

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Spain possesses a vast number of poems. Most have features that mean they present significantly different styles. A superficial reading of these poems may confuse readers due to their complexity. Therefore, it is of vital importance to classify the style of the poems in advance. Currently, poetry classification studies are mostly carried out manually, which creates extremely high requirements for the professional quality of classifiers and consumes a large amount of time. Furthermore, the objectivity of the classification cannot be guaranteed because of the influence of the classifier’s subjectivity. To solve these problems, a Spanish poetry classification framework was designed using artificial intelligence technology, which improves the accuracy, efficiency, and objectivity of classification. First, an artificial-intelligence-driven Spanish poetry classification framework is described in detail, and is illustrated by a framework diagram to clearly represent each step in the process. The framework includes many algorithms and models, such as the Term Frequency–Inverse Document Frequency (TF_IDF), Bagging, Support Vector Machines (SVMs), Adaptive Boosting (AdaBoost), logistic regression (LR), Gradient Boosting Decision Trees (GBDT), LightGBM (LGB), eXtreme Gradient Boosting (XGBoost), and Random Forest (RF). The roles of each algorithm in the framework are clearly defined. Finally, experiments were performed for model selection, comparing the results of these algorithms.The Bagging model stood out for its high accuracy, and the experimental results showed that the proposed framework can help researchers carry out poetry research work more efficiently, accurately, and objectively.

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Section: Spanish Text Classificationmentioning

confidence: 99%

An Artificial-Intelligence-Driven Spanish Poetry Classification Framework

Deng,

Wang,

Wang

et al. 2023

BDCC

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“…It is used as a basis in a wide range of studies and various linguistics-based tasks. It's commonly utilized in synthesizing [1] and recognizing speech [2] , [3] , converting text-to-phoneme [4] , estimating a speaking rate [5] , scoring the speaking proficiency [6] , classifying the speaker's emotion [7] , assigning lexical stress [8] , and analyzing metric [9] , among other things.…”

Section: Introductionmentioning

confidence: 99%

Augmented words to improve a deep learning-based Indonesian syllabification

Suyanto

Romadhony

Sthevanie

et al. 2021

Heliyon

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Recent deep learning-based syllabification models generally give low error rates for high-resource languages with big datasets but sometimes produce high error rates for the low-resource ones. In this paper, two procedures: massive data augmentation and validation, are proposed to improve a deep learning-based syllabification, using a combination of bidirectional long short-term memory (BiLSTM), convolutional neural networks (CNN), and conditional random fields (CRF) for a low-resource Indonesian language. The massive data augmentation comprises four methods: transposing nuclei, swapping consonant-graphemes, flipping onsets, and creating acronyms. Meanwhile, the validation is implemented using a phonotactic-based scheme. A preliminary investigation on 50k Indonesian words informs that those augmentation methods significantly enlarge the dataset size by 12.8M valid words based on the phonotactic rules. An examination is then performed using 5-fold crossvalidation. It reports that the augmentation methods significantly improve the BiLSTM-CNN-CRF model for 50k formal words and 100k named-entities datasets. A detailed investigation informs that augmenting the training set can reduce the word error rate (WER) coming from the long formal words and named entities.

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“…In particular, advances in machine learning-based language models have spurred investigations into various kinds of automated poetic analysis and synthesis. There has been recent work pursuing automated scansion ("scansion" denoting the marking of feet, accents, and caesuras or pauses in lines of poetry) (Agirrezabal et al, 2017a;Anttila and Heuser, 2016;de la Rosa et al, 2020;Marco et al, 2021;Greene et al, 2010;Hammond, 2013;McCurdy et al, 2015;Lau et al, 2018) and probing the ability of language models to encode rhyme (Abdibayev et al, 2021). Other work is devoted to the automatic classification of poetic kinds, like the haiku in English (Long and So, 2016) and free verse (Baumann et al, 2018).…”

Section: Introductionmentioning

confidence: 99%

Automating the Detection of Poetic Features: The Limerick as Model Organism

Abdibayev¹,

Igarashi²,

Riddell³

et al. 2021

Proceedings of the 5th Joint SIGHUM Workshop on Computational Linguistics for Cultural Heritage, Social Sciences, Humanities An

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In this paper we take up the problem of "limerick detection" and describe a system to identify five-line poems as limericks or not. This turns out to be a surprisingly difficult challenge with many subtleties. More precisely, we produce an algorithm which focuses on the structural aspects of the limerick -rhyme scheme and rhythm (i.e., stress patterns) -and when tested on a a culled data set of 98,454 publicly available limericks, our "limerick filter" accepts 67% as limericks. The primary failure of our filter is on the detection of "nonstandard" rhymes, which we highlight as an outstanding challenge in computational poetics. Our accent detection algorithm proves to be very robust. Our main contributions are (1) a novel rhyme detection algorithm that works on English words including rare proper nouns and made-up words (and thus, words not in the widely used CMUDict database); (2) a novel rhythm-identifying heuristic that is robust to language noise at moderate levels and comparable in accuracy to state-of-the-art scansion algorithms. As a third significant contribution (3) we make publicly available a large corpus of limericks that includes tags of "limerick" or "not-limerick" as determined by our identification software, thereby providing a benchmark for the community. The poetic tasks that we have identified as challenges for machines suggest that the limerick is a useful "model organism" for the study of machine capabilities in poetry and more broadly literature and lan-guage. We include a list of open challenges as well. Generally, we anticipate that this work will provide useful material and benchmarks for future explorations in the field.

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Automated Metric Analysis of Spanish Poetry: Two Complementary Approaches

Cited by 5 publications

References 16 publications

An Artificial-Intelligence-Driven Spanish Poetry Classification Framework

An Artificial-Intelligence-Driven Spanish Poetry Classification Framework

Augmented words to improve a deep learning-based Indonesian syllabification

Automating the Detection of Poetic Features: The Limerick as Model Organism

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