dataesr/scientific-paragraphs-categorization huggingface dataset:
Quebec civil and parish registers In the course of the i-BALSAC project, the birth and death certificates for the Quebec population between 1850 and 1916 will be integrated into the BALSAC database. Highresolution images of these records were obtained thanks to a partnership with the Bibliothèque et Archives nationales du Québec (BAnQ). They amount to 1,995,579 digitized pages from 44,742 registers located in 1,985 different parishes. Ultimately, the goal is to process these images to identify and index the various entities contained in each record such as names and surnames, dates, places and occupations, as illustrated in Fig 1. Since the integration of data includes a linkage procedure to connect information from various events pertaining to the same individual or the same family, the quality of the information, particularly on names and surnames, is of the utmost importance.
It is estimated that the complete processing of these images will make it possible to add approximately six million new records to the database, more than doubling its current size. Such an amount of data, covering the whole Quebec territory over seven decades, also means great variations in terms of page structure, handwriting styles, and multiple particularities across registers. Disparities in the data also comes from religious affiliation: about 60% of the registers of the 1850-1916 period come from Catholic parishes, while the remaining 40% are composed of non-Catholic confessions, including Protestants and their various denominations as well as Jews and Orthodox. These figures are slightly misleading, however, since registers are not organized the same way across religions: comparing Catholics and non-Catholics in terms of number of images, the proportions change to 78% and 22%, respectively.
In addition, most Catholic registers are in French, while most non-Catholics are written in English. Such a diversity is a challenge for deep learning techniques that are known to require training data representative of the full target corpus in order to be effective. The selection and annotation of training data are described in Sect 3. 1. In this article, we introduce three main contributions: -A complete workflow for nominative information extraction in historical registers, from the image to the database integration ; -A comparison of multiple machine learning models at each step of the workflow, allowing us to identify the main challenges for information extraction; -Rules designed for content verification and standardization, and an unsupervised metric for quality estimation. The paper is organized as follows: First, an overview of the main approaches for information extraction from digitized documents is presented in Sect 2.
Text line detection. Many open-source models have been proposed to tackle text line detection from historical documents, mainly ARU-Net  [19] , dhSegment  [3] , DocExtractor  [30]  and Doc-UFCN  [6]. These models have been pre-trained on multiple datasets  [18, 40] , making them very efficient on a wide variety of historical documents. They can be used easily out-of-the-box, or fine-tuned on a specific set of documents.
Large-scale workflows for automatic document understanding The BALSAC project aims to extract information from millions of handwritten historical records. Beyond the scientific challenge of automatic information extraction, processing millions of pages is also a technical challenge with multiple dimensions: data management, computing resources and processing time. Few works have been dedicated to information extraction in historical documents at such a large scale. Some of them also implemented rules for unsupervised content verification. A first approach was introduced in 2013 to perform information extraction in real conditions and at a large scale on eight million pages from the 1930 US census  [32]. For this task, the authors report a 70% automation rate: 30% pages were manually processed as the automatic system was considered unreliable.
Creation of the ground-truth data To address the challenge of data variability, the decision was made to produce training data independently of religious affiliation and language. The ground truth for training the different models of the extraction workflow consists of 913 transcribed and annotated images picked from 74 manuscript registers. They have been selected in order for the sample to be representative, based on the BALSAC team's knowledge of the records. The statistics of the annotated dataset at different levels (page, act, lines, words) are given in Table  1. The transcription and annotation were done using Transkribus 3  in three steps: layout analysis, transcription, and entity tagging. The detection of text lines and text regions was performed using Transkribus's CITLab tool, with manual corrections and additions.
The "marginalia" zone was annotated with a special label. The acts were also manually annotated, as they are the key unit in these registers. Bounding boxes of the acts were drawn so that they cover the main text of the act as well as the marginalia. Since an act can be written over more than one page, we also tagged each act as either: act start, to indicate that the act ends on the following page; act center, to indicate that the act starts on the previous page and ends on the following page; act end, to indicate that the act starts on the previous page; full act, to indicate that the act starts and ends on the current page. Table  1  also describes the distributions of these elements in the annotated dataset. The text lines were manually transcribed by research assistants, who have also tagged the main named entities, as detailed in Table  2.
The named entity recognition (NER) task was limited to dates, names, occupations and declared residences because these entities are found consistently in these registers. Moreover, these entities are easier to retrieve than other pieces of information, as they span over only a few words. In contrast, complex entities such as attendance (whether a person mentioned in the record was physically present for the act's recording) or signature information are often part of complex sentences and can require interpretation. Automated named entity recognition is crucial for this project, as the entities help to locate events in time and in space, specify the role of individuals mentioned in the records, and provide socioeconomic information such as occupations, honorific titles and literacy. Furthermore, names, places and occupations can be linked to existing dictionaries containing all the variations previously identified in the BALSAC database over more than three centuries of records.
Outlier detection results Two main layouts can be found in the corpus, depending on the scanning method: portrait (single page) and landscape (double page). We found that a model trained on both portrait and landscape orientations was unable to work properly, since the features learned on portrait images were "stretched" for landscape images. Therefore, a model was trained only on single images, as landscape pages can be easily split into two single pages. We used 739 single page images from the annotated dataset to train the outlier detector. To evaluate the models trained in an unsupervised manner, 20 images containing acts and 20 images without any act were annotated from unseen data. Grid search over different feature matrix/vector sizes and some hyperparameters for Isolation Forest and Local Outlier Factor was used to find the best model.
Text line detection model For the detection of text lines, we compared three state-ofthe-art systems: Doc-UFCN  [7] , dhSegment  [3]  and ARU-Net  [19]. They have been selected for their high performances on various historical document segmentation tasks. Doc-UFCN has been chosen since it has yielded good results on various historical datasets for the text line detection and for its ability to be trained on a reduced amount of training images. It aims at a general and flexible approach: depending on the input data, it can address, for example, text line extraction or complete layout analysis. Doc-UFCN is a fully convolutional network (FCN) comprised of (1) a contracting path composed of dilated convolutions that allow to learn an input image representation while augmenting the receptive field, and (2) an expanding path that is trained to output pixel probability maps.
dhSegment has also shown high performances on multiple historical document segmentation tasks. dhSegment is also a convolutional neural network (CNN) that follows an encoder-decoder architecture, where the encoder is a ResNet  [21]  model pre-trained on natural scene images  [14]. The decoding path is similar to Doc-UFCN's one. ARU-Net has been chosen for its good performance mainly for the baseline detection task. ARU-Net is also the text line detection model integrated into Transkribus  [23] , the most popular platform for historical document processing. It is an extended version of the standard U-Net that comprises spatial attention and a residual structure. The three systems have been trained on a wide set of diverse historical public and private datasets, including annotated BALSAC data, with the train/validation/test sets exposed in Table  1.
Text line detection results The trained models are evaluated using the Intersectionover-Union (IoU) metric, which compares the areas of the expected ground truth zones and the predicted ones. In addition, the predictions are evaluated at object level using average precision (AP) values. Based on a IoU threshold, the AP allows knowing the amount of correctly predicted text lines. Table  5  presents the IoU results as well as AP values for two thresholds (50% and 75%) and the mean AP (thresholds ranging from 50% to 95%). The IoU values obtained by Doc-UFCN and ARU-Net, higher than 85%, are better than what we usually get using diverse datasets for training. When looking at the AP values, we see that Doc-UFCN model clearly outperforms the two other systems. Indeed, the AP values obtained by Doc-UFCN show that the predicted objects are well predicted: a good localization and really low number of predicted merged lines.
Handwritten text recognition models The handwritten text recognition (HTR) is performed on the text lines detected by the Doc-UFCN model and outputs  -The Kaldi toolkit 7 , which is based on a deep neural network-hidden Markov model (DNN-HMM). -PyLaia 8 , which is a DNN model introduced in  [35] , that we combine with an external N-gram language model to improve performance. Kaldi model is similar to the one proposed in  [4]. It is composed of two standard components: first, an optical model, which is trained on text line images, learns the different shapes of sub-words defined using the BPE approach  [38]. During the decoding phase, it predicts sub-word probabilities in the text line using a sliding window segmentation. Second, a language model, which is trained on the text of the target documents, models linguistic information based on the frequencies of sequences of sub-words.
In our experiments, we used an optical model composed of 6 layers of convolutions with ReLU (Rectified Linear Unit) and batch normalization; 4 layers of TDNNs with ReLU and batch normalization; and an output layer with softmax. The language model is a 3-gram with Witten-Bell smoothing trained on the transcribed data only. A sub-word level 7 https://kaldi-asr. org/. 8 https://github. com/jpuigcerver/PyLaia. language model can deal with short text lines in which the words are often split, such as we have in the BALSAC parish records, and can also predict out-of-vocabulary words, such as names, as sequences of sub-words. PyLaia model is similar to the "expert" model presented in  [28]. First, images are resized to a fixed height of 128 pixels. Data augmentation is applied on the fly and consists of random affine transformations.
Named-entity recognition For the named-entity recognition step, we compared the performance of three open-source NER libraries: Stanza, Flair and spaCy. Stanza (v1. 1. 1) 10 [36] was created by the Stanford NLP group. It provides different NLP tools that can be used in a pipeline (e. g. , tokenization, NER) and pre-trained neural models supporting 66 different languages, including English and French for named-entity extraction. Flair (v0. 7) 11 [2] provides, besides state-of-the-art NLP models, contextual word and character embeddings, a feature that is now also supported by Stanza. Moreover, Flair allows stacking embeddings, on top of each other, a configuration that we adopted in this paper. spaCy (v2. 3. 5) 12 [22] is explicitly designed to ease deploying models into production. The library comes with out-of-the-box support of multiple languages, including French and English.
For each library, the pre-trained French model was finetuned using the annotated BALSAC data for named-entity recognition using the default values for the hyperparameters since they are usually the optimal values. The evaluation of the named-entity extraction model on the BALSAC test set using both the manual transcription and the automatic transcription is presented in Table  8. The metric used for the evaluation of the NER models is computed as follows: the automatic transcription is first aligned with the ground truth at character level, by minimizing the Levenshtein distance between them. Each entity in the ground truth is then matched with a corresponding entity in the aligned 10 https://stanfordnlp. github. io/stanza. 11 https://github. com/flairNLP/flair. 12 https://spacy. io. transcription, that is with the same entity tag, or an empty character string if not found.
Challenges A total of 44,742 files were received at BALSAC for integration in the database. Each file corresponds to a register. As mentioned before, linkage is the central step of this procedure as the construction of the database is based on linkage of civil records. Linkage is attempted on each new record using nominative information to connect it to an individual ID within the database. For example, a birth record contains information on three people: the subject of the event, the child, and his/her parents, father and mother. Each of these individuals is linked to an ID in the database, and the birth, is entered as an event, linking those three individuals. If no ID for an individual can be found in the database, it indicates that no event involving him or her was previously entered in BALSAC and a new ID is created.
It is important to note that the BALSAC database already contains Quebec Catholic marriages up to 1965. Therefore, for the linkage to work, the name of each member of the couple represents the key information to obtain in order to connect an act to the right family. Every type of record mentions a couple, whether as parents in a birth or in the death of a single individual (child or adult) or as a couple at a marriage or at the death of one of the two spouses. In addition, other elements such as the name of the parish or the date of the event can provide helpful information to choose between homonym couples. For BALSAC, all these elements, up to now, were detected by human eyes. A workflow consisting of research assistants reading the act and extracting the material selected for entry (and linkage) meant that some of the validation and the standardization operations were performed while manually entering the information.
Named entity extraction and consistency checks One of the essential indicators on the. xml documents is the flag Act_Type. Detection of this indicator starts a new step in the workflow by classifying each record among the three types of events that can possibly be recorded in the registers, i. e. , birth, marriage and death. In the case of acts composed of the combination of two text entities, the type is taken from the first part or from the second part if the first one was undefined Table  11  gives the distribution of the acts according to type. As mentioned above, all Catholic marriages up to 1965 are already entered and linked in the database. Therefore, linkage efforts are concentrated on births and deaths, which represent 79% of all records in our dataset. The undefined category contains acts with a shape and size that could correspond to a valid act but that could not be classified in a type.
Standardization The next step is the standardization of the information. Since the HTR system conveys the information literally, there is a need to translate the information into the format employed in the database. Date is a perfect field to exemplify the necessity of standardization: the. xml outputs the information in words, as it appears in the original source, but it needs to be entered into the database as a date field (numeric field). Thus, an algorithm changes each word detected into a number. This algorithm needs to be flexible and comprehensive as French language has a complex linguistic structure to write numbers (see  Fig 6). For instance, the year 1899 written in full would be dix huit cent quatre-vingt-dix-neuf, which would literally be numerically translated into: 10-8-100-4-20-10-9.
It uses this information to separate or join (when on two lines) the words included in the name field. Once the words have been separated, each one is compared to an existing thesaurus comprising more than 1,8 million names. Each of these names appears at least once in the database and, based on their appearances as first or last name, a probability of being one or the other is calculated for each one of them. This tool is used to classify each word as a first or last name. However, it should be noted that the first word in a sequence of names is always treated as a first name. In addition, the Act_Type can provide some cues on the way name will be written in the source. For example, the subject of a birth act will normally be named without its last name, but with all its given first names.
The third group mostly focuses on death acts recorded in immigrant boats. Since the family is most likely not existing in the database, linkage would not work either. Thus, for now, those three categories of acts are flagged as special cases and are put aside until we create a specific linkage algorithm taking into account the specific features of their content. At the end of the process, all acts (or more precisely text entities) that do not comply with the validation criteria are not submitted to linkage and are classified as invalid or fusion or special cases. Those acts are problematic either because they were not detected properly, had segmentation issues, or because the text in itself is not an act or is too unique to be processed automatically. Some of them could be processed again later as work is ongoing to improve aspects of the process such as act segmentation.
Others will necessitate manual intervention in order to correct or complete the transcription before they can be processed again. Table  13  shows the distribution of the acts according to their validity status. It indicates that 74% of records are deemed valid, meaning that linkage operations can be undertaken to integrate them in the BALSAC database. For now, the linkage has a fully automatic phase and a computer-assisted manual phase. One of the objectives of the next few years at BALSAC will be to develop an AI-based algorithm for linking the civil records.
Conclusion In this article, we have described a complete workflow designed for automatic information extraction in Quebec parish records at large scale. A succession of machine learning models were used for page classification, textline detection, handwritten text recognition, named entity recognition, and act detection and classification. We designed an unsupervised metric for quality estimation to reject incomplete or invalid extracted records before adding them in the BALSAC database. Working on this project has brought to light three main challenges and opened new questions. While setting up the complete workflow, we found out that the main steps (text line detection, handwritten text recognition, named entity recognition) were not the most difficult steps: these tasks are well studied in our field, and many offthe-shelf models are available.
However, the difficulties lie in more specific tasks, such as act detection. During the validation procedure, we learned that unsupervised quality estimation is a challenge. We believe that it should be addressed more frequently in the field of automatic document recognition. This also raises the issue of how to benefit from this metric while training the different machine learning models. On the BALSAC side, it also raises challenges for the integration of data from multiple versions of transcription results while preserving the integrity of the database as well as previously added data and linkage results. Working with AI opens up exciting new possibilities for the development of historical databases and for their users, but it also brings new kinds of limitations or biases in the data that translate into new challenges for users.
As mentioned above, many steps in the BALSAC workflow result in the removal, at least temporarily, of part of the data. This means that users work with datasets that are incomplete for different reasons which in many cases have more to do with the form than the content of the data and which are quite complex to pin down and define. In the case of i-BALSAC, this situation applies in the context of the transcription of parish records where the rejected data is randomly distributed over the range of years and regions it covers. These limitations certainly do not outweigh the benefits, but they should be taken into account when considering the contribution of this technology to database development.
(2023). The southern Asian campaign of the high-altitude research aircraft M55 Geophysica  (Stefanutti et al. , 1999)  focused on detailed observations of atmospheric transport and physical-chemical processes which dominate the input of air and aerosols into the (sub-)tropical stratosphere. In the present work, we make use of data from seven flights performed on 29 and 31 July and on 2, 4, 6, 8 and 10 August 2017. During these flights, the airplane penetrated cirrus clouds several times for nearly 6 h of observations in clouds over approximatively 35 flight hours. Most of the measurements during StratoClim were performed at temperatures < 205 K, corresponding to potential temperatures > 355 K and altitudes > 14 km, i. e. , in the TTL. Krämer et al. (2020)  report a description of the clouds observed during the campaign.
The latter are associated with low values of IWC and number concentration, which makes them less influential in regulating the IWC, which is, on the contrary, substantially influenced by planar ice particles of liquid origin. The difference in ice number concentration across habits can be up to 4 orders of magnitude, with aggregates occurring in much smaller numbers. We verify the ability and limits of optical modeling in reproducing the results of remote sensing optical measurements from the concomitant in situ microphysical ones, thus assessing the compatibility of the two datasets. Closure studies between aerosol light-scattering coefficients and aerosol PSD with optical modeling are common in atmospheric science, and they often use measurements from in situ nephelometers and Mie theory applied to particle spectrometer data  (Wex et al.
Instruments and data We have compared and used data from the two dualinstrument cloud spectrometers that flew aboard the aircraft Geophysica, the NIXE-CAPS and the CCP. Both instruments (see Sect. 2. 2) are developments of droplet measurement technologies (DMTs) and are nearly identical in their technology, the sensors used for pressure and temperature, and the flow measurement technology (Prandtl's pitot tube system). The IWC has been derived from the total water hygrometer FISH and the water vapor hygrometer FLASH (see Sect. 2. 3); the cloud backscattering coefficient and depolarization ratio at 532 nm have been derived from the in situ backscattersonde MAS (see Sect. 2. 1). Data from MAS are also compared in some cases of interest with the 532 nm elastic lidar MAL.