dataesr/scientific-paragraphs-categorization huggingface dataset:
Moreover, PeakForce-QNM AFM datasets often consist of many pixels (up to 512 × 512), each containing many features (the corresponding force at any given level of indentation). Though accessing such an abundance of information for all pixels is highly beneficial to generalize the AFM mapping, handling so much data-some of which may be irrelevant or even misleading (due to either viscoelasticity or topography)-is a typical burden to the algorithm of built-in PF-QNM segmentation and classification. Therefore, the computational cost of analysis at the tip-surface force-indentation data calls for a robust and efficient method in finding out which features are the most relevant to understand the heterogeneous structure of the investigated sample without any prior knowledge or assumptions. Machine learning clustering analysis techniques (such as K-means analysis), in AFM post-analysis, have already been pioneered by  [19, 20].
However, the high dimensionality of the initial dataset (the forceindentation curves) poses great challenges to standard manifold learning. In the presence of many irrelevant features, learning models are prone to overfitting, thus becoming less powerful  [21]. In the previous work  [22] , proper orthogonal decomposition (POD  [23] , also known as SVD  [24]  or PCA  [25] ) was shown to be an efficient way to 'visually' uncover these "state variables" by offering a low-dimensional representation of the entire force-mechanical data. By selecting the most relevant POD modes as the subset to represent the initial AFM data, in the subspace generated by POD (usually 2D or 3D representations), the user can have an intuitive visual inspection of the corresponding data manifold, which is of great practical interest.
In this work, as a step forward, we attempt to go beyond visualization, through a so-called "machine learning process", to directly identify the underlying "state variables" governing the AFM force-indentation dataset. In general, the clustering analysis assumes that there exists a discrete set of behaviours that can be labelled from their centroids, whereby the latter needs to be interpreted. In the present work, we will deal with two popular approaches: • clustering analysis; • manifold reparametrization. In this context, many variants exist, either those based on a similarity classification in a Euclidean-metric space (e. g. , K-means  [26] , fuzzy c-means  [27, 28] ), or those using probabilistic modelling (e. g. , Gaussian mixture model  [29] ) which assume the presence of different datasets.
The present work presents the use of the above-mentioned "classical" algorithms, as available in different toolboxes, (Matlab was used in this study), to the novel context of voluminous data achieved from PF-QNM AFM measurements. After appropriate editing and formatting, the raw initial data is firstly compressed into a subset with a much lower dimensionality through proper orthogonal decomposition  [22]. The subset is then visually inspected inside the POD subspace. Different machine learning tools from those listed previously may be adopted depending on the dimensionality of the observed data manifold. The novelty and benefits of using the combined model reduction and machine learning algorithm are summarized as follows; Firstly, the entire force-indentation data are used instead of a particular material property dictated by one single point of the curve (e.
An Introductory Example About Pattern Recognition The segmentation of the dataset F(x, δ) is a typical one in pattern recognition problems. In a two-phase heterogeneous medium, there are two general classes (e. g. , the nature of phases) to be distinguished, say "black" and "white". Based on the nature of the selected materials, the contrast between different phases can be seen as the difference in stiffness, adhesion, or both of them (the definition of "black" or "white"). Moreover, the class of each object may be subdivided into sub-species based on other higher-order parameters. For instance, a specific class (phase) can be further divided into two different topography states: smooth or rough. In this regard, pattern recognition may be characterized as an information labelling process ("a data classifier").
g. , POD) are used to achieve a low-dimensional representation from the initial dataset, where the data manifold can be visually inspected, allowing the user to "observe" the underlying state variables. The AFM data are considered as a force-indentation matrix, F(x, δ), with size N x × N δ , where N x is the total number of pixels inside the ROI and N δ = 512 stands for the total number of sampled δ values for each pixel. Note that in practice, the number of pixels is much larger than that of sampling δ values, N x N δ. The force matrix is thus decomposed into N δ POD modes as shown in Equation  (7). Each POD mode is written as a multiplication of its eigenvalue λ (n) , the associated eigen-force-indentationresponse W (n) (δ) and corresponding spatial modulation U (n) (x), such that F(x, δ) = N δ ∑ n=1 λ (n) U (n) (x)W (n) (δ).
Initialization Select the range of penetration, δ , to construct the initial force-penetration matrix F(x, δ) F(x, δ) = n=Nδ 1 λ (n) U (n) (x)W (n) (δ) Select first N POD modes to present initial dataset The previous general methodology is applied and further discussed using examples in the following sections. The first case (PS-LDPE sample) is a test case for the approach since it deals with a well-separated two-phase nano-surface. The second example is used to reveal the efficiency of the proposed approach in a substantially more complex composite material, that of PDMS comprising carbonyl-iron particles (CIP). The segmentation algorithms are presented in detail in the first example and directly applied in the second. F N (x, δ) = n=N 1 λ (n) U (n) (x)W (n) (.
In this initial dataset, after the POD analysis, the first three modes are retained. Their mechanical responses are shown in Figure  8b. Their contribution amounts to τ 1 = 0. 73 > τ 2 = 0. 11 > τ 3 = 0. 09, describing approximately 94% of the energy (L2-norm) of the original Fδ data, as shown in Figure  8c. Let us emphasize that the first mode U (1) (Figure  8d ) already allows for a clear distinction between the PDMS matrix (light blue) and the CIP inclusions (darker spots), where manifested topography is not present at all. In particular, aggregates of several particles exceeding 10 µm (i. e. , 3-4 times the particle size) are clearly visible. Figure  8d  illustrates the difference in sensitivity of the AFM-POD analysis compared with the SEM imaging, the former probe depths of the order of particles or clusters of particles, while the latter reveals microstructures at the scale of the electronic penetration depth.
Conclusions The recently popularized PF-AFM mode greatly improves the scanning frequency of AFM mapping and thus permits the recording of large datasets containing many features in a short acquisition time. Though harvesting such an abundance of data is highly beneficial to locally characterize the observed specimen, handling the measurement of well-defined mechanical properties individually at each pixel location becomes a progressively more challenging problem. Furthermore, the validity of the modelling used to fit the data may be questioned, especially when assessing viscoelastic and non-linear polymer materials. To circumvent these difficulties, the proposed combined model reduction and manifold learning algorithm allows for identifying generalized common patterns and interpretation of the mechanical properties in both samples analysed in this study.
First, the efficiency of the POD model reduction technique in achieving a low-dimensional representation of the initial data has been demonstrated. The model reduction process can seamlessly separate the nature of phases from other higher-order parameters (topography or viscosity) in the POD subspace, allowing the analyst to visually inspect the data manifold, which is of great practical interest for robust qualitative analysis. Second, the underlying state variables can be fully uncovered through clustering analysis or manifold learning. Proper adapted manifold learning tools can be pre-determined accordingly based on the dimensionality of the observed data (modal amplitudes) during the previous "visual inspection" step. Specifically, the proposed manifold learning algorithm is a conceptual approach to hyper-reduction applied to PF-QNM AFM data.
We present version 1. 3 of the PARSEME multilingual corpus annotated with verbal multiword expressions. Since the previous version, new languages have joined the undertaking of creating such a resource, some of the already existing corpora have been enriched with new annotated texts, while others have been enhanced in various ways. The PARSEME multilingual corpus represents 26 languages now. All monolingual corpora therein use Universal Dependencies v. 2 tagset. They are (re-)split observing the PARSEME v. 1. 2 standard, which puts impact on unseen VMWEs. With the current iteration, the corpus release process has been detached from shared tasks; instead, a process for continuous improvement and systematic releases has been introduced.
Introduction The difficulty in automatically identifying multiword expressions (MWEs) in texts has been acknowledged for a while  (Sag et al. , 2002; Baldwin and Kim, 2010) , and confirmed through results of experiments, many of which conducted as part of shared tasks  (Schneider et al. , 2016; Savary et al. , 2017; Ramisch et al. , 2018 Ramisch et al. , , 2020)). MWEs, especially verbal ones (VMWEs), have been the focus of the PARSEME community since the homony-mous COST Action took place 1 and are now paid further attention, in correlation with syntactic annotation and language typology, within the UniDive COST Action 2. Training, tuning, and testing the systems that are able to identify VMWEs in texts need corpora annotated with such expressions. Within PARSEME, guidelines for annotating VMWEs were created and then improved with feedback provided during annotation.
New languages We have two new languages on board: Arabic and Serbian. The previous dataset for Arabic was created by Hawwari in PARSEME 1. 1  (Ramisch et al. , 2018). However, this corpus has never been published under an open license, being restricted to the Shared Task participants. The Arabic corpus in PARSEME 1. 3 is a new corpus created from scratch. More than 4,700 VMWEs have been annotated in about 7,500 sentences taken from the UD corpus Prague Arabic Dependency Treebank (PADT)  (Hajic et al. , 2004) , containing newspaper articles. This new annotated corpus is already available in the PARSEME repository under the CC-BY v4 license. The Serbian language was not represented in the previous versions of the PARSEME corpus. The first step in preparing the Serbian PARSEME corpus consisted of the preparation of the large set of examples required for the guidelines.
Enlarged corpora Three of the languages already present in previous editions were further enhanced with new annotated data: Greek, Swedish, and Chinese. In the first edition of the PARSEME corpus, the Greek (EL) dataset was rather small and we have been committed since to adding new data in view of ultimately providing a corpus of adequate size. The new dataset comprises newswire texts (c. 26K sentences) also from sources that are characterized as bearing an informal register, lifestyle magazines, and newspapers, in order to account for new types of VMWEs. Only a fraction of the Greek dataset bears manual annotations at the lemma, POS, and dependency levels, namely the one originating from the UD initiative; the rest was completed automatically using UDpipe. VMWEs annotation was performed by two annotators; during the annotation process, extensive discussions were aimed at manually correcting common errors and avoiding inconsistencies.
The Swedish data set is expanded in comparison to PARSEME release 1. 2. The Swedish annotations now cover the complete UD Swedish-Talbanken treebank, increasing the total size from 4,304 to 6,026 sentences. The Swedish corpus includes the manual morphosyntactic annotations from UD, now updated from version 2. 5 to version 2. 11. The new annotations were done in connection to the PARSEME 1. 2 annotation campaign, by two trained annotators. As an extra decision support, the annotators were given access to the report from the consistency check for Swedish PARSEME 1. 2, which both annotators reported as being very useful. In this edition, the Chinese data includes 9,000 newly annotated sentences from the CoNLL 2017 Shared Task  (Zeman et al. , 2017). The columns were updated with the new UDPipe model to make the data consistent with the standard of UD 2.
Enhancements of the existing data The Croatian PARSEME annotations were, long overdue, transferred to the source hr500k dataset  (Ljubešić et al. , 2016)   9. Sentences in hr500k that were annotated with PARSEME annotations are those that are annotated with gold UD linguistic annotation. With the PARSEME annotation transfer into hr500k, we enabled the gold UD annotations, which are being continuously improved, to be transferred back to the Croatian PARSEME dataset. The percentage of sentences that went through some change is rather staggering: from 3828 sentences, only 374 (9. 8%) stayed identical as in PARSEME version 1. 1, while the remaining sentences went through some sort of improvement in the linguistic annotation, either UD error correction or UD standard enhancement. The Romanian corpus contained annotation of the VID, LVC.
The number of morphological features changed in the features field is 6451. For two languages, Czech and Maltese, PARSEME corpora were released in version 1. 0 only. The 1. 0-to-1. 1 upgrade of the PARSEME annotation guidelines 10  involved a few major changes, including a redesigned set of VMWE categories. Thus, upgrading 1. 0 corpora to version 1. 1 requires some manual intervention. Further upgrades to versions 1. 2 and 1. 3 were minor and mostly automatically applicable. For the present release, we could achieve a partial upgrade from version 1. 0 to 1. 3 in Czech and Maltese. Future work includes manual annotation of the LVC. cause category, which emerged in v 1. 1.
Compatibility with Universal Dependencies Syntactic and semantic properties of MWEs are deeply intertwined. 11  Therefore, the PARSEME corpus has, since its beginnings, been released with annotations for both VMWEs and morphosyntax for most languages. The morphosyntactic annotations have not been produced by PARSEME annotators but rather extracted from existing treebanks or generated by parsers. To this end, we have been increasingly relying on the UD framework (de Marneffe et al. , 2021), treebank collection  (Nivre et al. , 2020)  and UD-Pipe parser  (Straka, 2018) , as PARSEME largely shares UD's objectives and principles of universality and diversity. Since edition 1. 1, the PARSEME corpus uses the. cupt format, which extends the UD' CoNLL-U format with a VMWE annotation layer. 12 Since edition 1.
2, we have strongly advocated compatibility with UD version 2. This objective has been finally achieved in the current 1. 3 edition. In 11 languages, we have at least partly manual morphosyntactic annotations. When those stem from UD treebanks, we synchronised them with the most recent UD release (2. 11 from November 2022) 13. In 16 languages, at least part of the morphosyntactic data had been automatically generated and we updated them using the most recent UDPipe models (mostly v 2. 10). 14  Whenever several models per language existed, tagging/parsing performances and the genre of the training corpus were used as choice criteria. As a result, all 26 language corpora now use the UD-2 tagsets (most often in the 2. 11 version) for POS, morphological features, and dependency relations. 15 The README files were updated with details of the above updates and a change log now documents the history of releases.
Corpus re-split The PARSEME Shared Task edition 1. 2 involved dividing the annotated corpora provided by the task organizers into three subsets: training, development, and test (train/dev/test). The training data is used to train the MWE identification systems, the development data is used to perform model selection and fine-tuning, and the test data is used to evaluate the performance of the final models. Since new languages were added and others updated, we decided to follow the 1. 2 standard  (Ramisch et al. , 2020)  to re-split the annotated corpus for each language participating in the 1. 3 release. This splitting method is based on two key parameters: the number of unseen VMWEs in the test data compared to the combined train and dev data, and the number of unseen VMWEs in the dev data compared to the 12.
Annotation guidelines One important aspect of the PARSEME guidelines is the database of examples in multiple languages. Currently, the guidelines feature 232 example identifiers, each covering up to 28 languages. However, not all languages have examples for all example identifiers: we have a total of 1,980 examples, whereas, in theory, we could include up to 232 × 28 = 6, 496 examples. In edition 1. 2, the guidelines contained 1,801 examples; the newly added examples concern mostly Serbian and Arabic, i. e. the languages for which new corpora have been created for this release. Figure  1  shows a histogram with the number of examples per language, ranging from 188 for Spanish to only 1 example for Turkish, Hebrew, and Lithuanian. 17 The examples in the guidelines are complex, including their form in the original language, lexicalised components in bold, literal, and idiomatic translations, as well as explanations, comments, negative counter-examples, etc.
Versioning, documenting and querrying In order to help the maintenance of the different corpora, a new infrastructure was set up. All existing corpora, gathered from different previous releases were put in the same GitLab group 18  , with each language having its own repository. Now, all  new updates on treebanks and new data are stored in this unique place. A rich collection of Wiki pages, available from the same GitLab space, gathers rich documentation of the PARSEME corpora and shared task initiatives, the corresponding tools and procedures, etc. The Grew-match  (Guillaume, 2021)  tool has a new instance  19  which gives access to the PARSEME corpora. With this tool, it is possible to make graph-based queries to observe the annotated data; both PARSEME annotations and the underlying UD annotations can be used in queries.
Data from each release is available. Moreover, thanks to a continuous integration system, data synchronized with the current development state of each of the 26 corpora (i. e. the data available on the master branch of each GitLab repository) can be accessed in Grew-match and the corresponding consistency checks web page is updated automatically when data changes 20. As an example of Grew-match usage, a simple Table  1  provides a comparison of the performance of Seen2Seen and of the multi-task version fo MTLB-STRUCT in identifying VMWEs, including their precision, recall, and F-measure scores across 14 languages of the Shared Task edition 1. 2 and 26 languages of the new release 1. 3. For Seen2Seen, the F-score significantly increased in edition 1. 3 for Basque, Hebrew, Hindi, and Swedish.
In the case of Basque and Hindi, where no new VMWE annotations were added, this enhancement is certainly due to re-annotating the corpora with a recent version of UDPipe, which must have enhanced the quality of lemmas, used by Seen2Seen to extract VMWE candidates. In Swedish, the corpus size significantly grew, while in Hebrew its quality improved with consistency For MTLB-STRUCT, the evaluation of the release 1. 3 models for Irish, Croatian, Hungarian and Romanian could not be performed for technical reasons. Among the other 10 languages covered both in release 1. 2 and 1. 3, the increase of the global F-measure is the most significant in Swedish. Also Basque, French, Portuguese and Turkish benefit from the data enhancements. For other languages, the F-measure is lower than in version 1. 2, likely due to switching to the multi-task version of the model.
The primary focus of Figure  3  is to showcase how the F-score changes as the number of VMWE tokens in the training corpus varies between releases 1. 2 and 1. 3. By analyzing the F-scores of different languages, we can observe the effect of the number of VMWE tokens in the training corpus on the performance of the Seen2Seen and MTLB-STRUCT systems. For instance, the increase of the Swedish (SV) and Basque (EU) datasets brought about a higher F-score. Conversely, the F-score for Chinese (ZH) significantly decreased despite the increase in the number of VMWE annotations. This might be attributed to the increased number of unseen VMWEs in the larger corpus. Interestingly, the Turkish dataset decreased in edition 1. 3 but the global F-score for both systems increase, which might stem from the higher quality of the 1.
3 release data. For Seen2Seen, a large increase of the dataset brings a significant decrease of the F-score, which might indicate a biased nature of the 1. 2 release, balanced in version 1. 3. Note that we restrict our comparison to edition 1. 2. It would be less meaningful to compare the scores of editions 1. 0 and 1. 1 with the current version since the splitting methods used in those editions did not prioritize unseen VMWEs. But, even restricted to releases 1. 2 and 1. 3, the comparison may not be fully reliable, since: (i) each corpus was re-split into train, dev and test sets, i. e. the systems are not trained and evaluated with the same data partitions, (ii) only teh multi-task version of MTLB-STRUCT is examined for release 1. 3.
Future work This paper summarises the first release of the PARSEME corpora out of the context of a shared task. This fourth release (v. 1. 3) is the first one to cover the union of all the languages included in the previous three releases. Moreover, 2 new languages were included, a significant amount of additional data was added for 3 languages, and annotations for many languages were enhanced in various ways. The future of the PARSEME corpus collection relies on the interests and availability of its volunteer contributors for each language. From the infrastructure perspective, we would like to consolidate the release methodology so that future yearly releases can smoothly integrate and make available the upgrades performed throughout the year by language teams. This includes further automation of procedures, in the spirit of CI/CD 22  , including updates of the UD morpho-syntactic annotations, validating the file formats and MWE annotations, and checking the README.
md documentation. Another important goal of PARSEME is the extension of its guidelines to (a) non-verbal MWEs, (b) verbal MWEs not covered in the current guidelines, and (c) improved cross-lingual account of phenomena that are currently biased by the set of languages covered in the corpora. Finally, we envisage synergies with the UD community so that the MWE layer and the morphosyntactic annotations become gradually even more compatible. The challenges to achieving this goal include reaching compatible tokenisation decisions, unified terminology, reduction of redundancy (e. g. MWEs annotated as subrelations of syntactic dependencies), and syntactic connectiveness of annotated MWEs. Two other initiatives which contributed to the outcomes presented here are the CA21167 COST action UniDive (Universality, diversity and idiosyncrasy in language technology) and the Dagstuhl Seminar 21351 (Universals of Linguistic Idiosyncrasy in Multilingual Computational Linguistics).