Poetry Assistant

2013

We present SPARSAR, a system for the automatic analysis of poetry(and text) style which makes use of NLP tools like tokenizers, sentence splitters, NER (Name Entity Recognition) tools, and taggers. Our system in addition to the tools listed above which aim at obtaining the same results of quantitative linguistics, adds a number of additional tools for syntactic and semantic structural analysis and prosodic modeling. We use a constituency parser to measure the structure of modifiers in NPs; and a dependency mapping of the previous parse to analyse the verbal complex and determine Polarity and Factuality. Another important component of the system is a phonological parser to account for OOVWs, in the process of grapheme to phoneme conversion of the poem. We also measure the prosody of the poem by associating mean durational values in msecs to each syllable from a database and created an algorithm to account for the evaluation of durational values for any possible syllable structure. Ev...

Sci

This paper presents a quantitative approach to poetry, based on the use of several statistical measures (entropy, information energy, N-gram, etc.) applied to a few characteristic English writings. We found that English language changes its entropy as time passes, and that entropy depends on the language used and on the author. In order to compare two similar texts, we were able to introduce a statistical method to asses the information entropy between two texts. We also introduced a method of computing the average information conveyed by a group of letters about the next letter in the text. We found a formula for computing the Shannon language entropy and we introduced the concept of N-gram informational energy of a poetry. We also constructed a neural network, which is able to generate Byron-type poetry and to analyze the information proximity to the genuine Byron poetry.

In this paper, we focus on large scale poetry classification by meter. We repurposed an open source poetry scanning program (the Scandroid by Charles O. Hart-man) as a feature extractor. Our machine learning experiments show a useful ability to classify poems by poetic meter. We also made our own rhyme detector using the Carnegie Melon University Pronouncing Dictionary as our primary source of pronunciation information. Future work will involve classifying rhyme and assembling a graph (or graphs) as part of the Graph Poem Project depicting the interconnected nature of poetry across history, geography, genre, etc.

Proceedings of the Demonstrations at the 14th Conference of the European Chapter of the Association for Computational Linguistics, 2014

We present SPARSAR, a system for the automatic analysis of poetry(and text) style which makes use of NLP tools like tokenizers, sentence splitters, NER (Name Entity Recognition) tools, and taggers. In addition the system adds syntactic and semantic structural analysis and prosodic modeling. We do a dependency mapping to analyse the verbal complex and determine Discourse Structure. Another important component of the system is a phonological parser to account for OOVWs, in the process of grapheme to phoneme conversion of the poem. We also measure the prosody of the poem by associating mean durational values in msecs to each syllable from a database of syllable durations; to account for missing syllables we built a syllable parser with the aim to evaluate durational values for any possible syllable structure. A fundamental component for the production of emotions is the one that performs affective and sentiment analysis. This is done on a line by line basis. Lines associated to specific emotions are then marked to be pronounced with special care for the final module of the system, which is reponsible for the production of expressive reading by a TTS module, in our case the one made available by Apple on their computers. Expressive reading is allowed by the possibility to interact with the TTS.

PoeTryMe is a poetry generation platform under development that intends to help the automatic generation of meaningful poetry according to a given semantics. It has a versatile architecture that provides a high level of customisation where the user can define features that go from the base semantics and sentence templates to the generation strategy and the poem configuration. A prototype using PoeTryMe was implemented to generate Portuguese poetry. The results are interesting but there is still a long way for improvement, so we devise ideas for future work.

Sci, 2020

This paper presents a quantitative approach to poetry, based on the use of several statistical measures (entropy, informational energy, N-gram, etc.) applied to a few characteristic English writings. We found that English language changes its entropy as time passes, and that entropy depends on the language used and on the author. In order to compare two similar texts, we were able to introduce a statistical method to asses the information entropy between two texts. We also introduced a method of computing the average information conveyed by a group of letters about the next letter in the text. We found a formula for computing the Shannon language entropy and we introduced the concept of N-gram informational energy of a poetry. We also constructed a neural network, which is able to generate Byron-type poetry and to analyze the information proximity to the genuine Byron poetry.

Pattern Analysis and Applications, 2006

Literature is one of the most significant forms of human culture. It represents a high level of intellectual activity. Humans write, read, and enjoy poems in different cultures. The purpose of this work is to initiate research in the field of poem recognition, not to understand them by electronic devices, but to provide humans with modern tools to search for and work with lyrics. This study is concerned to illuminate the challenges of poem recognition from different perspectives. In this paper, various measures of poetry are introduced and their effectiveness are investigated in different case studies using fuzzy logic, bayesian approach, and decision trees.

1st Seminar of Art, Music, Creativity and Artificial …, 2009

This paper is about the automatic generation of creative text, more precisely the automatic generation of poetry. It starts by presenting two possible categorisations for systems that aim generating poetry and then makes a brief overview on the existing attempts to this subject based on what can be found in the literature.

We employ a corpus-based approach to generate content and form in poetry. The main idea is to use two different corpora, on one hand, to provide semantic content for new poems, and on the other hand, to generate a specific grammatical and poetic structure. The approach uses text mining methods, morphological analysis, and morphological synthesis to produce poetry in Finnish. We present some promising results obtained via the combination of these methods and preliminary evaluation results of poetry generated by the system.

Poems are particular well-crafted formulations of certain messages that satisfy constraints on their form as well on their content. From an engineering point of view, the ability to optimize the combination of form and content is a very desireable feature to be able to model. The present paper reviews a number of efforts at modelling poetry generation computationally, and it brings together insights from these efforts into a computational model that allows integration of a number of AI technologies combined together according to control structures compatible with observed human behaviour. The relation of this computational model with existing cognitive models of the writing task, and with models of computational creativity is discussed.