The rise in artificial intelligence and natural language processing techniques has increased considerably in the last few decades. Historically, the focus has been primarily on texts expressed in prose form, leaving mostly aside figurative or poetic expressions of language due to their rich semantics and syntactic complexity. The creation and analysis of poetry have been commonly carried out by hand, with a few computer-assisted approaches. In the Spanish context, the promise of machine learning is starting to pan out in specific tasks such as metrical annotation and syllabification. However, there is a task that remains unexplored and underdeveloped: stanza classification. This classification of the inner structures of verses in which a poem is built upon is an especially relevant task for poetry studies since it complements the structural information of a poem. In this work, we analyzed different computational approaches to stanza classification in the Spanish poetic tradition. These approaches show that this task continues to be hard for computers systems, both based on classical machine learning approaches as well as statistical language models and cannot compete with traditional computational paradigms based on the knowledge of experts.
Recent advances in genomic and post-genomic technologies have provided the opportunity to generate a previously unimaginable amount of information. However, biological knowledge is still needed to improve the understanding of complex mechanisms such as plant immune responses. Better knowledge of this process could improve crop production and management. Here, we used holistic analysis to combine our own microarray and RNA-seq data with public genomic data from Arabidopsis and cassava in order to acquire biological knowledge about the relationships between proteins encoded by immunity-related genes (IRGs) and other genes. This approach was based on a kernel method adapted for the construction of gene networks. The obtained results allowed us to propose a list of new IRGs. A putative function in the immunity pathway was predicted for the new IRGs. The analysis of networks revealed that our predicted IRGs are either well documented or recognized in previous co-expression studies. In addition to robust relationships between IRGs, there is evidence suggesting that other cellular processes may be also strongly related to immunity.
The development of the network of ontologies of the ERC POSTDATA Project brought to light some deficiencies in terms of completeness in the currently available European poetry corpora. To tackle the issue in the realm of the Spanish poetic tradition, our approach consisted in designing a set of tools that any scholar could use to automatically enrich the analysis of Spanish poetry. The effort crystallized in the PoetryLab, an extensible open source toolkit for syllabification, scansion, enjambment detection, rhyme detection, stanza identification, and historical named entity recognition for Spanish poetry. We designed the system to be interoperable, compliant with the project ontologies, easy to use by tech-savvy and non-expert researchers, and requiring minimal maintenance and setup. Furthermore, we propose the integration of the PoetryLab as a core functionality in the tool catalog of CLARIN for Spanish poetry.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.