dataesr/scientific-paragraphs-categorization huggingface dataset:
For example, some might attribute the ungrammaticality to a printing error; others would argue that "love are in we" is  Annotation is only one of two procedures supported by CATMA: in fact, its true potential lies in its seamless combination of annotation and the analysis of the text itself, of any text/annotation-combination, and even of the triple combination of text/annotation/meta-annotation. For more detailed information on CATMA's analytical function, see  Meister, 2020 ; on the CATMA query language, see https://catma. de/how-to/query-language/ (accessed April 24, 2020). merely a rhetorical trope (inversion) that, at the time the poem was written, was so overtraded among American authors that no contemporary reader would have bothered to bat an eyelid. And so forth. Let us collect all of this; moreover, let us ask annotators to provide, record, and discuss the rationale for their individual annotation decisionsin other words: get annotators to annotate and comment on their annotations (which is supported by CATMAsee Fig.
The dataset is available in public repositories, offered in plain text and TEI, and enriched with RDFa, a linked data format. The corpus was intended for research and teaching, as well as for non-specialist use. Some questions naturally emerge: How can we easily navigate this corpus and its rich metadata? How can we identify trends using its annotations? How can users not proficient in XML query languages and linked data benefit from such a dataset? To address these issues, we have created DISCOver, a user-friendly web interface that allows users to explore the DISCO corpus. It was conceived as a means to help students, researchers, and general readers, making the corpus accessible to a wider audience. The interface displays literary annotations in individual texts as well as quantitative data on user-defined subcorpora.
State of the art One fundamental difficulty for digital humanities studies of Spanish literature is the scarcity of digital resources  (Agenjo, 2015). This relates, firstly, to digital corpora for Spanish poetry but also to tools for automatically annotating Spanish poems with literary information and for utilizing the annotations. We will first  https://github. com/pruizf/disco (accessed April 20, 2022).  https://doi. org/10. 5281/zenodo. 1012567 (accessed April 20, 2022). cover existing work on digital resources (Section 2. 1) before addressing automatic annotation (Section 2. 2) and visualization (Section 2. 3).
This chapter will argue that the voices on these recordings can be studied as data, using computational means, and that these machine listenings can use large datasets to provide new readings of individual poems. The idea of a corpus or dataset is good place to get started. We generally tend to conceive of sets of cultural objects as archives, collections sheltered for posterity in the institutional domiciles that Derrida traced to the ancient Greek archons or magistrates (1998). PennSound, for example, is the world's largest archive of poet recordings  (PennSound, n. d. ). Now over 15 years old, it contains over 55,000 poetry recordings and 6,000 hours of audio of poets, going back to Apollinaire in 1913 and stretching all the way to contemporary recordings. In addition to those who use PennSound for close listening research or for personal edification, a set of scholars is beginning to consider this unprecedented sound collection as a dataset, pursuing questions on a scale that was heretofore impossible or approaching traditional literary historical questions via the new vector of digital sound.
There are multiple ways to approach a machine learning project like this. One way is by means of "unsupervised learning," which means, in a sense, letting your machine loose on a dataset and having it locate correlations. This is problematic in a project like this for a number of reasons. For one, variables can, statistically speaking, appear to be correlated even when there is no meaningful connection between them. When Tanya Clement and I were working on an early instantiation of her project, the machine found similarities between an Allen Ginsberg recording and a snippet that sounded like a refrigerator hum. Is there a correlation? Perhaps in some machinic episteme. Perhaps less so for literary studies. For this project, I am building toward a supervised learning model that begins with an impressionistic close listening.
Materials The present research was conducted using a corpus of rhymed English verse from the mid-nineteenth century. This corpus was derived from the Chadwyck-Healey English Poetry database, which aims to "encompass the complete published corpus by all poets listed" in the New Cambridge Bibliography of English Literature (NCBEL) (About, n. d. ). Its 183,000 poems thus represent canonical English literary history on a large scale. In its nineteenth-century selections, the English Poetry collection goes beyond the NCBEL by including writers from the British Empire as well as a substantial number of American poets. Its size means not only that many of its poets are unfamiliar even to specialists in nineteenthcentury literature but also that the poems included in the database range from well-known classics to texts that have been virtually ignored since the years of their first publication  (Karlin, n.
d. ). Although there are thousands of lesserknown poets not included in the Chadwyck-Healey database, its large array of texts makes possible a distant reading of English poetry that would not be possible for conventional human reading alone. To create the corpus used in this study, 32,233 rhymed poems were identified among the mid-nineteenth-century poems in the Chadwyck-Healey collection using a program that identifies rhymes by matching the rhyme words and syllables provided in John Walker's A Rhyming Dictionary  (Walker, 1824; Houston, forthcoming). This historical poetics approach uses nineteenth-century rules for rhyme and pronunciation to identify rhymes in nineteenth-century texts. For the present study, a poem over 100 lines long was defined as rhymed if over 88% of its end words were part of a rhyme pair or cluster.
A poem under 100 lines was defined as rhymed if over 57% of its end words were part of a Rhyme Frequency in Nineteenth-Century English Poetry rhyme pair or cluster. These percentages were manually confirmed as providing a threshold of reasonable certainty for rhymed verse in random samples taken from the corpus. It is possible that other rhymed mid-nineteenth-century poems exist in the larger corpus from which this study's subset was drawn, but in the absence of the human reading of every poem in the corpus, this threshold was deemed adequate for selecting the study's dataset. This dataset contains poems by 240 poets, 196 male and 44 female. Although women poets make up 18% of the poets included in the corpus, poems by women (8,164) constitute 25% of the poems (32,233). The mean number of poems by each poet is 134, although the median is 70.
I use the POS tagger of a language modeling program called spaCy. 8 It should be noted that the act of POS tagging itself is always a probabilistic venture. Even manual tagging is prone to error or different interpretations. In the case of modern POS tagging programs like spaCy, a model of language use is created and "trained" via neural networks (e. g. , machine learning). To a certain degree, the more data that flows into the networks the greater the possibility it has to learn which POS is most likely the correct one to assign words given their in-sentence context. The version of spaCy used below is trained on a corpus of over one gigabyte of digital  This use of spaCy accounts for proper nouns, punctuation, determinants, adjectives, nouns, adverbs, spaces, conjugations, verbs, participles, adpositions, numbers, pronouns, interjections, and symbolic characters.
Are they a patient with entries for each gene? Are they genes with entries that tally patients' medical data? Overlaying this metaphor on the text to POS problem  See "Probabilistic Nonnegative Matrix Factorization" at https://nimfa. biolab. si/nimfa. methods. factorization. pmf. html (accessed November 15, 2016; Zitnik and Zupan) for more detailed information on this particular variant method.  This is the metaphor employed by a set of researchers using the matrix factorization code library, Nimfa, used for the PNMF results below. See bibliography and http://nimfa. biolab. si/ (accessed November 15, 2016). Hayford's Duplicates: Cobbling a Model of Melville's Moby-Dick yields "latent sentences" or "latent POS. " In this case, the task is to identify latent sentences around which PNMF can group actual sentences from the novel as being somehow linked by their similar POS usage.
However, it should be noted that the stylistic marks of dictionaries often suffer from inaccuracy and much more subjectivity than the intuition of a modern native speaker, and are also archaic and usually do not take into account many new trends in the use of words  (Vekshin, Shilihina, 2017). Therefore, we draw from these sources with great care. In addition, to add typical poetic concepts and characters to the database, dictionaries of poetic language may serve as a support (Dictionary of the Language of Russian  Poetry, 2001-; Ivanova, 2004; Pavlovich, 2007)  as well as the most significant linguistic studies of poetry and authors' individual style. The most stylistically homogeneous texts or text fragments selected from the search results are further subjected to double processing. Firstly, units are extracted from them to form new stylesets and further replenish the corpus.
Structure All the data is stored in the SQLite database file. The data is stored in the "question" table. Each text is described with 23 features from "field0" to "field22. " Each categorical feature has its own description in the appropriate table. To speed up computation, each text is stored in its own separate file. The filename is stored in the "question" table in the "field6" column. In the event   1900 1900-1917 1917-1940 1941-1959 1960-1980 1990-2000  (a) that a single text has multiple labels for some features, these labels are separated by a comma.
The third word was classified correctly by the first two approaches, and the third gave us a rather uncertain result. In the fourth word, there was an error, and neither the second nor the third model was able to overcome it. However, the first classifier made the correct decision. The first classifier interpreted the fifth word incorrectly, but the second one was not able to give any result. This is because the required form of the word was not presented in the dataset. Therefore, stemming generally gives us the worst result among all the classifiers. We can only apply it to classify the words occurring in the different forms, but it may lead to errors. The second approach performs rather well, but only with words it has encountered before. The first approach gives us some misinterpretations, but it can work with words it has never encountered before.
As for the annotation software CATMA, it too exemplifies how a new, scalable practice of digital text analysis can bring together "close" and "distant" reading as two parts of one continuum and bring an empirical perspective on poetic texts and poetry into fruitful contact with their conceptualization. This call to bring together qualitative and quantitative approaches is nothing new, but it makes our time a very exciting one to be a part of and our endeavor to combine computation and hermeneutics a laudable challenge. Indeed, and Meister stresses this, we need to address textual objects at both a holistic and an analytical level, blend systematic and intuitive reasoning routines, navigate linear and iterative processes, and, of course, integrate distant and close reading. Helena Bermúdez Sabel, Pablo Ruiz Fabo, and Clara Martínez Cantón present something that sounds like a little rover exploring some faraway Disco planet: DISCOver.
This is a well-thought-out web interface that builds on their previous work, DISCO, the DIachronic Spanish Sonnet COrpus. DISCO is not a planet but a large dataset, comprising 4,085 sonnets written between the fifteenth and nineteenth centuries, including canonical and lesser-studied authors from both Spain and Latin America, with detailed author metadata, metrics, rhyme scheme, and enjambment annotations. DISCO had already been made available on public repositories in plain text and TEI, enriched with the linked data format RDFa. Yet some audiences who could have been interested in using it had been prevented from doing so for lack of an easier navigation system. So, the authors constructed DISCOver, for which the user needs no prior knowledge of XML or linked data. The impressive technical apparatus of DISCO is both accessible and hidden in DISCOver.
Acknowledgments: The interface was developed thanks to a  Josef Dobroský Fellowship  funded by the  Akademie věd České republiky (Czech Academy of Sciences ; 2018) and hosted at the  Institute of Czech Literature AS CR in March 2019 , as well as a  Zdeněk Pešat Fellowship during its Spring 2019 program , also hosted at the  Institute of Czech Literature AS CR  during the same period. Acknowledgment: I would like to thank  Benoît de Cornulier  for his accurate and meticulous reading of this chapter. I am particularly grateful for his thoughtful remarks, which have significantly improved it. Acknowledgment: This study was made possible through my collaboration with the  Literary Lab at Stanford University. Acknowledgments: This writing and all of its figures, data, and code are available for viewing and download in the " Hayford's Duplicates "  GitHub  project at: https://github.
The work has been carried out within the framework of the  St. Petersburg University  research project, " Study of Vladimir V. Nabokov's Literary Heritage in an Interdisciplinary Perspective Using Information Technology Methods" (ID 72828386 ). This project was supported by  RFBR , grant no. 17-04-00421 :  Linguistic Development and Creation of the Electronic Russian Live Stylistic Dictionary. Acknowledgment: The work in the project QuaDramA is being generously funded by the  Volkswagen Foundation. Acknowledgements: This project has been partially supported by project  CANTOR (Ref PID2019-108927RB-I00 ), funded by the  Spanish Ministry of Science and Innovation. The work reported on in this chapter was heavily influenced by discussions with  Thierry Poibeau  and  Pablo Ruiz Fabo  during my visit to the  Lattice Laboratory at École Normale Supérieure, Paris , in January 2020.
Using multimodal MRI images for glioma subtype classification has great clinical potentiality and guidance value. In order to further improve the glioma subtypes prediction accuracy in clinical applications, we propose a Multimodal MRI Image Decision Fusion-based Network (MMIDFNet) based on the deep learning method. Inspired by image fusion  (27) , the proposed method uses a linear weighed module to assemble the models trained with a single modality together for mining their complementary predictive capabilities. To evaluate the effectiveness of our proposed method, we compared the classification performance between our method and recent state-of-the-art methods. Additionally, since radiomics-based methods are also commonly used in recent brain tumor classification studies, we also implemented a radiomics-based method as the benchmark for performance comparison with our MMIDFNet.
Study Cohort Our experimental data were obtained from the CPM-RadPath challenge 2020 dataset 1 , which is supported by Medical Image Computing and Computer-Assisted Intervention Society. The dataset classified patients into three subtypes based on the WHO-CNS pathomorphological classification criteria, named Glioblastoma (abbreviated as "G"), Astrocytoma (abbreviated as "A"), and Oligodendroglioma (abbreviated as "O") separately  (25, 26). Each patient contained preoperative 3D MRI images and pathological whole slide images. MRI images comprise four different modalities of T1-weighted (T1), T2-weighted (T2), post-contrast T1-weighted (T1ce), and fluid-attenuated inversion recovery (FLAIR). Considering the purposes of our study, we just used MRI images to predict pathological subtypes. According to the dataset description, MRI images were obtained from multi-parametric MRI scans in routine clinics with 1T to 3T MRI scanners in multi-center institutions and stored in NIfTI format.
All four MRI modalities were preprocessed with bias field correction, skull stripping, and co-registration into the same anatomical structure template  (24). The volume size of each MRI modality data is 240 × 240 × 155, where 155 indicates the number of slices. The cohort in our experiments consisted of 221 patients collected from the original dataset, in which there were 133, 54, and 34 samples provided for subtype "G", "A", and "O", respectively. To overcome the bias caused by a particular selection for the pair of training and testing sets, a 3-fold crossvalidation strategy was used in this work. Specifically, the dataset was split into 3 smaller sets, the model was trained using 2 of the folds as training data, and then the trained model was validated on the remaining part of the data.
MMIDFNet Architecture To improve the accuracy of glioma diagnosis using multimodal MRI images, we designed the MMIDFNet for glioma subtype classification, as shown in Figure  1. The MMIDFNet architecture includes two parts: one is the tumor segmentation module using a pre-trained model, and the other is the two stages of unimodal model training and multimodal image decision fusion inferring module. In the training stage, we used the images of each single modality to train the network to obtain its best accuracy on the testing set. In the present work, we used images from four MRI modalities. Thus, we have four pre-trained models. In inferring stage, a decision fusion strategy was used; in other words, a linear weighted module was applied to assemble the predicted probabilities of the above four pre-trained models for each modality.
Tumor Segmentation Accurate segmentation of brain tumors from MRI images is of enormous potential value for improved diagnosis  (28). It can be done automatically to cope with the time-consuming disadvantage of manual segmentation  (29, 30). Considering that the MRI images in our study were also used in the BraTS challenge and that the ground truth of tumor segmentation for patients in our cohort are not all available, we used the pretrained model on the BraTS challenge 2019 to delineate the regions of tumor lesions, which achieved the accuracy of 90. 45% on the validation set  (31). In the BraTS 2019 dataset, all the samples in the training set are provided with four ground truth labels for 4 regions: background (label 0), necrotic and nonenhanced tumor (label 1), peritumoral edema (label 2), and enhanced tumor (label 4).
Statistical Analysis Age being the only available clinical factor in the CPM-RadPath challenge dataset (6), we converted it from days to years for simplicity before analysis. The differences in age and glioma subtypes between the training and testing sets were assessed using the Mann-Whitney rank-sum test. The statistical quantifications of the performance metrics were demonstrated with 95% CI, when applicable. All statistical analyses were carried out with the Scipy module (version 1. 3. 1), and p-value <0. 05 indicated a significant difference.
Unimodal Prediction Models After carefully tuning the parameters of unimodal models with our MMIDFNet method, we obtained the best pre-trained prediction models for each single modality in the training stage in turn. The ROC curves of each pre-trained unimodal model for each fold are plotted in Figure  3. We found that the prediction performance for each modality among different validation folds was not significantly different, which validates that the dataset selection has no significant influence on the prediction performance of our method. In addition, the performance of unimodal prediction models using radiomics and our proposed MMIDFNet method on three validation folds is summarized in Table  1. We noticed that the split of the training and validation sets indeed influenced the prediction performance of both radiomics and our models, but not significantly.
Multimodal Prediction Models Using our proposed MMIDFNet method, through repeatedly adjusting the weights of each unimodal prediction model in inferring stage, we obtained the best multimodal prediction accuracy. After tuning the parameters of our radiomics model iteratively, we also obtained the best prediction using the fused features of multimodal images. In this paper, the multimodal prediction methods obtained with radiomics and MMIDFNet were named as radiomics model and decision fusion model, respectively. Specifically, considering our designed network also supports multi-channel input in the training stage, through inputting four modalities into four-channel input of our MMIDFNet simultaneously, we trained and obtained another multimodal prediction model (named as data fusion model) based on data fusion strategy for comparing the predictive performance between data fusion strategy and decision fusion strategy in our MMIDFNet method.
Here, the data fusion strategy means that the multiple modal images were concatenated as input. The ROC curves of the radiomics model, data fusion model, and decision fusion model using multimodal MRI images on each validation fold are illustrated in Figure  4. We found that for the glioma subtypes "G", and "A", the prediction performance of all the methods was not greatly influenced by the splitting of the training and validation sets. However, for the glioma subtype "O", the data splitting strategy had significant effects on the predicted AUC. In addition, the overall prediction of our decision fusion model for a multi-class predictive task is more balanced than the other two multimodal models. To further compare the prediction performance of different fusion strategies, Table  2  summarizes the 3-fold cross-validation performance of each method on the multimodal dataset.
837. Besides, as for our MMIDFNet method, the accuracy of the data fusion model (0. 846) is 3. 2% lower than that of the decision  fusion model (0. 878) via 3-fold cross-validation. This is mainly caused by the limitations of the data-level fusion strategy that does not fully take advantage of the features underlying each modality data and does not deal with how to fuse the features from the multimodal MRI images  (33, 43, 44). However, in our decision fusion model, we used the weighting manner to ensemble the unimodal models in inferring stage. Theoretically, the fraction of each modality should be positively related to its contribution. From Table  1 , we noticed that T1ce was the most useful modality for the prediction, followed by Flair, T2, and T1. In our fusion model, the weights for T1ce, Flair, T2, and T1 modalities are 2, 1, 0.
Although the segmentation result is not as accurate as the ground truth, we minimized the adverse effects caused by inaccurate segmentation by adopting the central clipping manner to segment out the whole tumor regions. Although encouraging, our method has several limitations. First, as a retrospective study, the sample size of the dataset used in the present study was limited, which has an adverse effect on the robustness of our designed model. Therefore, the few-shot learning method may be a better choice to handle the problem. Second, we used only MRI modalities in the present study without considering other types of data, especially pathological whole-slide images. To further improve the performance of glioma subtype classification, in the future, we could try to combine MRI, pathology images, molecular genetic information, and other clinical data to conduct a multi-omics clinical study.