dataesr/scientific-paragraphs-categorization huggingface dataset:
Word Recognition Most of the time, document analysis (section 3) and word recognition have performed successively. Text analysis, as detailed previously, is considered as a prepossessing step where the main-text is located and lines are extracted. Then, other tasks, such as word recognition, can be applied. As explained in section 2, processing historical Arabic documents faces different challenges in spite of the conception of new methods like the Optical Character Recognition to modern printed documents. To overcome these challenges, researchers segment historical documents into words or sub-words instead of characters. Then, these words (sub-words) are recognized by matching them with known words (subwords) in the reference dataset; this is called word recognition. Despite the importance of this task, only few solutions are proposed in the literature, and can be roughly regrouped into two classes: iq model-based approaches and iiq model-free approaches.
In  [50] , HMM is used to model a multi-stream input. The authors proposed in  [78]  to use a CNN to estimate the n ´gram frequency profile of a word image. Then, the Canonical Correlation Analysis is used to match the estimated profile to the reference dataset. In  [9] , a CNN is used for feature extraction. Then, in the matching step, a HMM is used to measure a similarity between two word features. In  [38] , the authors investigate a Deep Belief Neural Network (DBNN) for Arabic word recognition. Regarding the model-free category, the authors of  [100]  proposed a lexicon reduction as a preprocessing step for word recognition. It consists of detecting dot descriptors. Then, a convolutional neural network is used to verify these dot candidates. Finally, the number and position of dots are used to eliminate unlikely lexicon candidates.
Discussion In the literature, the word recognition task is formulated as a classification problem, and mostly, model-based solutions are proposed. Moreover, the researchers are often driven to overcome other challenges when dealing with the word recognition task, namely iq extending the size of the training dataset and iiq recognizing the whole manuscript using only a part of the manuscript in training. Note that the word recognition task may be used for writer classification (section 4). For an unknown manuscript, each sub-word may be classified to one reference class associated with a known writer. The author of the query manuscript is then claimed as the writer associated with a maximum of reference words. Although deep learning based approaches have mostly been applied to non-historical documents, it is expected that they will become mainstream in the historical word recognition context.
Text Alignment Text alignment is one of the APHAD important tasks. It aims at determining the similarities and differences between two given manuscripts, mostly, two versions of the same manuscript. These dissimilarities between versions of the same manuscript can result from: iq details and notes added by readers on the original manuscript, iiq scribes inserted, replaced or omitted to adapt the content to geographical regions, etc. Why do we need to align manuscripts? To answer this question, recall that in most HAD datasets, the documents are saved as images, which makes searching and indexing the document a difficult task. In some cases, an ASCII transcription is provided together with the document's image, and the alignment task would be the task of mapping each word from the transcript to its corresponding word image in the original document's image.
The alignment task allows to iq accelerate searching and indexing in the document images and iiq generate an automatic ground truth for a document image, that can be used to evaluate the recognition algorithms (or other APHAD tasks). Text alignment algorithms can be regrouped into two categories: 1q model-based alignment and 2q model-free alignment. In the model-based alignment approaches,  [56] , a prior model is learned from a dataset of annotated manuscripts. In model-free alignment solutions, original document images and transcript images are matched without any prior information by estimating a mapping function (i. e alignment function)  [32, 80, 16]. As model-based alignment approach, the authors of  [56]  formulated the alignment task as a classification problem, using a Siamese Neural Network.
Alignment approaches Model-based approaches based on Machine Learning  [56]  Model-free approaches based on the estimation of a mapping function Mapping function Ñ Minimize the cost matching between manuscripts  [32] [80] Mapping function Ñ using usual distances  [16]   needs a large annotated dataset as input to create a reliable alignment model. To have such a dataset, several pages of the original database were annotated using the WebGT tool  [24]. Besides, to enrich the original dataset, data augmentation techniques were employed: 1q first, minor distortion and affine transformation were applied, 2q second, synthesis images (generated from the original ones) were added using the algorithm in  [54]. After data preparation, two sets of image pairs were generated: true-pair and false-pair.
Problem description Data Retrieval (DR) consists of searching/retrieving a specific information from a query document. The need for such application is increasing with the increasing number of documents available in the datasets. In many data retrieval systems focusing on non historical documents, document images are first converted to their text format (i. e. Electronic Representations) using the Optical Character Recognition algorithm, then DR techniques are applied on the text format to retrieve information. However, regarding the low quality of historical documents, converting the document images to their text format is still a challenging and complex task that is often avoided. In fact, it is generally more advantageous to explore the documents at the image level rather than the text format level.
In fact, each component is defined by a polygon with k vertices, then, geometry features, related to angles and length between the different vertices are extracted. Finally, these features are used as inputs to the matching step. Two classifiers were investigated: Hidden Markov Models (HMM) and Dynamic Time Warping (DTW). HMM is a generative probabilistic model that models a set of temporal observations  [82]. Dynamic Time Warping is an unsupervised algorithm for measuring similarity between two sequences which may vary in length  [88]. In  [87] , the authors evaluated their approach in a larger historical dataset. Moreover, the solution proposed in  [84]  was enhanced by slightly modifying the DTW to include different costs for substitution, insertion and deletion of segments from the compared sequences.
Model-free approaches In model-free approaches, image features are matched without any prior informations. Matching techniques used in the literature can be classified into two categories: iq Data structure-based matching and iiq Direct features matching. • Data structure-based matching: the reference dataset is transformed into a data structure. It can be a hash table, a dictionary or other data structure. • Direct features matching: features are matched directly without any prior information or data structure. In the data structure-based matching category, a dictionary-based approach was proposed in  [53]. The approach is segmentation-based as the input document is segmented into word-parts. Next, two steps are applied: iq features extraction based on Radial descriptor and iiq features matching.
Each word-part image is described by a set of local features representing the dominant features. These features are associated to the pixels having a high variance according to their response to Radial descriptor  [53]. Then, each word-part is represented by a Bag of Word (BoW)  [93]  descriptor. The descriptor is based on dictionary representation. The dictionary is constructed by clustering all reference dataset features into k classes and a pattern is represented by its occurrence probability histogram according to the constructed dictionary. The matching step is based on usual distance metrics such as the cosine distance and the χ 2 distance. The same authors proposed in  [55]  a graph structure-based approach. The main idea is to take into account the position of features in the image.
The word spotting task was formulated as a problem of finding GHT transformation parameters. Given a query keyword image (described by its GHT transformation parameters), the goal is to find the transformation's parameters that map the query to the reference images. The approach consists of two steps: iq Hash table construction and iiq Spotting. First, word's GHT transformation parameters of the reference dataset are saved in a hash table, then at the spotting step, the query parameters are compared to reference images using usual distances. In the direct features matching category, a DTW-based approach is proposed in  [105]. It consists of three steps: iq word segmentation, iiq features extraction and iiiq word matching. The system takes as input a binary image and segments it into words via the "Run Length Smoothing Algorithm (RLSA)"  [99].
The framework consists of two steps: iq Letter Connectivity Map (LCM) Construction and iiq Synthesizing words. To construct the LCM, multiple instances of each letter various shapes are needed (since an Arabic letter shape varies by its position in the word). The LCM is constructed from the training dataset. Once the letter connectivity map is available, the framework can synthesize the pictorial representation of any Arabic word or sentence from their text representation. The synthesized words can be used in word spotting task and many other historical document applications to enrich the training dataset.
Discussion Compared to the others APHAD tasks, the word spotting was well investigated in the literature. In fact, retrieving information from documents is very useful for users and specially, for scientists, historians, etc. The performance of the word spotting process depends on other preliminary tasks such as word segmentation. To reduce the impact of such tasks, the researchers propose to use the sliding window technique or a segmentation-free approach. In our opinion, word segmentation and word spotting tasks are independent and should be investigated separately. Moreover, to properly compare the performance of two word spotting approaches, the same segmentation method should be used. Note that the word spotting task becomes more interesting when the keyword searching is performed on a large dataset.
As shown in Tab. 7, the used datasets have a small size. It would be more interesting to evaluate the word spotting task performance on larger and more various datasets (printed, handwritten, degraded, low quality, low resolution, etc). Moreover, the absence of large annotated datasets limits the use of advanced machine learning methods such as Deep Learning. To overcome this problem, recent research works  [54]  aim to augment the size of the training dataset using synthesis techniques. In this context, the data augmentation step should be investigated further.
Datasets To evaluate computer vision algorithms, researchers need datasets. In the literature, only few historic Arabic document datasets exist. Existing datasets can be roughly regrouped into two categories: iq Degraded datasets  [91, 92]  and iiq Not-degraded datasets  [3, 74, 52, 71, 59]. In this section, we present the characteristics of each dataset and the APHAD applications that can be evaluated using that datasets. Figure  5  shows samples from datasets.
WAHD Dataset It is an Israeli dataset  [3] , published in 2017 to evaluate writer classification task. It is composed of N " 353 different manuscripts collected from two sources: iq the Islamic Heritage Project (IHP) (333 manuscripts) and iiq the National Library in Jerusalem (NLJ) (20 manuscripts). • The 333 IHP manuscripts were written by 302 different writers: 23 of them are known writers and the remaining writers are unknown. Each scribe wrote one manuscript (single writer) or more (multiple writer). In more detail: -Eleven scribes are multiple writers. They wrote 42 manuscripts consisting of 2313 pages. -The remaining twelve scribes are single writers and wrote 2108 pages. The remaining 279 manuscripts, written by unknown writers, were considered, all, as single writers. These manuscripts composed of 32,548 pages.
VML-HD Dataset It is an Israeli dataset  [52] , published in 2017 for word spotting and word recognition tasks. The dataset is composed of five manuscripts; each manuscript had been written by one distinct scribe, from the year 1088 to 1451. The dataset contains 680 pages fully annotated on the sub-word level. It contains a total of 121,636 sub-word appearances from 1,731 classes. The variation in a word may be any combination of noise, writing style and font size.
HADARA80P dataset It was proposed in 2014 by a Germany Institute  [74]. It is composed of 80 pages of the historical book "Taaun". These pages correspond to the book cover and the first 79 first pages. In addition to the images, XML files containing the ground truth are available. The HADARA80P dataset contains 16,720 annotated words. It also includes a selection of 25 pre-defined keywords. The number of occurrences of keywords full match in the dataset is between 5 occurrences and 349 occurrences.
IBN-SINA Dataset This dataset  [71]  was proposed, in 2010, by a Canadian University and provided by the Institute of Islamic Studies (IIS). The dataset contains 51 folio of one manuscript, the "Kitab Kashf al-tamwihat sharh al-Tanbihat". The dataset was manually annotated on sub-words level, to extract 20,722 shapes (connected components). The shapes can correspond to words, sub-words or letters. Based on clustering algorithms, the dataset can include more than 1,000 basis connected components (keywords). The dataset can be used for word recognition/spotting tasks.
BADAM Dataset The public BADAM dataset  [59]  contains 42 manuscripts from four digital collections of the Arabic and Persian languages. It contains 400 annotated pages from different domains and time periods. From each manuscript, 10 pages were chosen, except for four shorter manuscripts containing only 3 to 7 pages. [66]. In more detail: • Paper translucency: is an internal degradation and appears when a document is written on both sides of translucent paper. • Stains: are external noises that may occur during the printing process. • Readers annotation: sometimes readers add notes and highlight sentences in the document. Readers annotation can produce noise. • Worm holes: are the dig tunnels in old documents. For information, the dataset was selected from the library of Qatar University.
Discussions There are few software proposed to manipulate historical Arabic documents and exploit its contents. Mostly, authors collect theirs approaches, proposed for different tasks, in one tool by adding a graphical interface to manage inputs/outputs. These softwares are essentially related to document annotation and preprocessing. Most of these software are private and not available for users. This non-availability of tools not allows us to confirm their performances on recent HAD datasets. The annotation tool, WebGT, is free and widely used by several researchers to generate ground truth.
Conclusion This paper presented a survey on existing approaches to analyze and understand the content of historical Arabic documents. Four major applications were identified: iq Document analysis, iiq Writer classification, iiiq Document classification and ivq Data retrieval. For each task, a review of existing solutions was presented and recommendations were suggested. Moreover, the public datasets used to evaluate existing approaches were summarized. our future extensions of this work is to evaluate tasks on datasets include degraded documents. Moreover, our research direction is to investigate the data augmentation step that facilitate the use of advanced machine learning techniques such as Deep Learning.
7. 1. 6. 6 MHDID Dataset: Multi-distortion Historical Document Image Database The MHDID dataset [91] contains 335 historical document images with resolution of 1024x1280 pixels. It includes various distortion types that can be roughly classified into four categories: iq Paper translucency (88 images), iiq Stain (113 images), iiiq Readers annotations (61 images) and ivq Worn holes (73 images).
Scientists are harnessing their multi-disciplinary expertise and resources to fight the COVID-19 pandemic. Aligned with this mindset, the Covid-on-the-Web project aims to allow biomedical researchers to access, query and make sense of COVID-19 related literature. To do so, it adapts, combines and extends tools to process, analyze and enrich the "COVID-19 Open Research Dataset" (CORD-19) that gathers 50,000+ full-text scientific articles related to the coronaviruses. We report on the RDF dataset and software resources produced in this project by leveraging skills in knowledge representation, text, data and argument mining, as well as data visualization and exploration. The dataset comprises two main knowledge graphs describing (1) named entities mentioned in the CORD-19 corpus and linked to DBpedia, Wikidata and other BioPortal vocabularies, and (2) arguments extracted using ACTA, a tool automating the extraction and visualization of argumentative graphs, meant to help clinicians analyze clinical trials and make decisions.
1 Bringing COVID-19 data to the LOD: deep and fast In March 2020, as the Coronavirus infection disease (COVID-19) forced us to confine ourselves at home, the Wimmics research team 1 decided to join the effort of many scientists around the world who harness their expertise and resources to fight the pandemic and mitigate its disastrous effects. We started a new project called Covid-on-the-Web aiming to make it easier for biomedical researchers to access, query and make sense of the COVID-19 related literature. To this end, we started to adapt, re-purpose, combine and apply tools to publish, as thoroughly and quickly as possible, a maximum of rich and actionable linked data about the coronaviruses. In just a few weeks, we deployed several tools to analyze the COVID-19 Open Research Dataset (CORD-19)  [20]  that gathers 50,000+ full-text scientific articles related to the coronavirus family.
On the one hand, we adapted the ACTA platform 2 designed to ease the work of clinicians in the analysis of clinical trials by automatically extracting arguments and producing graph visualizations to support decision making  [13]. On the other hand, our expertise in the management of data extracted from knowledge graphs , both generic or specialized, and their integration in the HealthPredict project  [9, 10] , allowed us to enrich the CORD-19 corpus with different sources. We used DBpedia Spotlight  [6] , Entity-fishing 3 and NCBO BioPortal Annotator  [12]  to extract Named Entities (NE) from the CORD-19 articles, and disambiguate them against LOD resources from DBpedia, Wikidata and BioPortal ontologies. Using the Morph-xR2RML 4 platform, we turned the result into the Covid-on-the-Web RDF dataset, and we deployed a public SPARQL endpoint to serve it.
)  [2]. What researchers need is tools to help them get convinced that some hypotheses, treatments or explanations are indeed relevant, effective, etc. Exploiting argumentative structures while reasoning on named entities can help address these user's needs and so reduce the number of controversies. The rest of this paper is organized as follows. In Section 2, we explain the extraction pipeline set up to process the CORD-19 corpus and generate the RDF dataset. Then, Section 3 details the characteristics of the dataset and services made available to exploit it. Sections 4 and 5 illustrate the current exploitation and visualization tools, and discuss future applications and potential impact of the dataset. Section 6 draw a review of and comparison with related works. This section describes (Fig.
Building the CORD-19 Named Entities Knowledge Graph The CORD-19 Named Entities Knowledge Graph (CORD19-NEKG), part of the Covid-on-the-Web dataset, describes NEs identified and disambiguated in the articles of the CORD-19 corpus using three tools: -DBpedia Spotlight  [6]  can annotate text in eight different languages with DBpedia entities. Disambiguation is carried out by entity linking using a generative model with maximum likelihood. -Entity-fishing 6  identifies and disambiguates NEs against Wikipedia and Wikidata at document level. It relies on FastText word embeddings to generate candidates and ranks them with gradient tree boosting and features derived from relations and context. -NCBO BioPortal Annotator  [12]  annotates biomedical texts against vocabularies loaded in BioPortal. Patterns are identified using the Mgrep method.
Annotator+  [19]  extends its capabilities with the integration of a lemmatizer and the Context/NegEx algorithms. To ensure reusability, CORD19-NEKG leverages well-known, relevant terminological resources to represent articles and NEs in RDF. Below, we outline the @prefix covidpr : < http :// ns. inria. fr / covid19 / property / >. @prefix dct : < http :// purl. org / dc / terms / >. @prefix oa : < http :// www. w3. org / ns / oa # >. @prefix schema : < http :// schema. org / >. [] a oa : Annotation ; schema : about < http :// ns. inria. fr / covid19 / f74923b3ce82c. >; dct : subject " Engineering " , " Biology "; covidpr : confidence " main concepts of this RDF modeling. More details and examples are available on the project's Github repository. 7  Article metadata (e. g. , title, authors, DOI) and content are described using DCMI 8 , Bibliographic Ontology (FaBiO) 9 , Bibliographic Ontology 10 , FOAF 11  and Schema.
Component Detection. This is a sequence tagging task where, for each word, the model predicts if the word is part of a component or not. We combine the BERT architecture 16   [7]  with an LSTM and a Conditional Random Field to do token level classification. The weights in BERT are initialized with specialised weights from SciBERT  [1]  and provides an improved representation of the language used in scientific documents such as in CORD-19. The pre-trained model is fine-tuned on a dataset annotated with argumentative components resulting in. 90 f 1 -score  [14]. As a final step, the components are extracted from the label sequences. Relation Classification. Determining which relations hold between the components is treated as a three-class sequence classification problem, where the sequence consists of a pair of components, and the task is to learn the relation between them, i.
e. support, attack or no relation. The SciBERT transformer is used to create the numerical representation of the input text, and combined with a linear layer to classify the relation. The model is fine-tuned on a dataset for argumentative relations in the medical domain resulting in. 68 f 1 -score  [14]. PICO Element Detection. We employ the same architecture as for the component detection. The model is trained on the EBM-NLP corpus  [17]  to jointly predict the participant, intervention  17  and outcome candidates for a given input. Here, the f 1 -score on the test set is. 734 [13]. Each argumentative component is annotated with the PICO elements it contains. To facilitate structured queries, PICO elements are linked to Unified Medical Language System (UMLS) concepts with ScispaCy  [16]. Argumentative knowledge graph.