dataesr/scientific-paragraphs-categorization huggingface dataset:
Introduction Multiword expressions (MWEs) present groups of words in which the meaning of the whole is not derived from the meaning of its parts. The task of processing multiword expressions is crucial in many NLP areas, such as machine translation, terminology extraction etc. This paper describes the MUMULS system 1 which was evaluated through its participation to the PARSEME shared task on automatic identification of verbal MWEs 2 (VMWEs). The experimental data set of the shared task is the result of a massive collaborative effort that produced training and evaluation data sets, available in 18 languages. The subsequent corpus was built by experts of each of the languages who manually annotated all VMWEs. The training and test sets respectively consist of a total of about 4. 5 and 0. 9 1 MUltilingual MULtiword Sequences 2 http://multiword.
sourceforge. net/ sharedtask2017 million tokens, containing 52,724 and 9,494 annotated VMWEs. For most languages, a. conllu file provided morphological and syntactic information for each token. In addition, the training data set was indicating for each token, whether it belonged to an MWE, which one, and the type of that MWE. The MWE types are IReflV(inherently reflexive verb), LVC (light verb construction), VPC (Verb-particle construction), ID (idiomatic expression) and OTH -other types. The goal of systems is to identify the VMWEs from text and to recognize to what type they belong. The data set and full evaluation procedure is more extensively described in the overview paper of the PARSEME shared task  (Savary et al. , 2017). Since MUMULS did not make use of any other resources than those provided by the shared task organisers, the system participated in the "closed track" (as opposed to the open track, in which participants could make use of any external resources).
Experiment Results Table  1  presents the results of the MUMULS system for all the languages for which it produced non-zero results. Out of 18 available languages, MUMULS was experimented over 17. We found the bug that was introduced during data preprocessing for Czech language that caused recall issues, the re-trained model with very same setup as for other languages had higher score, which we additionally included in the result table. We did not include the languages for which we were not able to produce any predictions. Table  2  provides the accuracy in terms of fmeasure for the individual types of VMWEs. It can be seen that the system scored better in more 'syntactic' MWEs like IREflV, LVC or VPC, and generally (with the exception of French) the score for those categories is higher than for idioms.
Analysis of distinct types of MWEs We observe the following errors for several MWE types and for several languages: • not all the tokens of an MWE are marked. This entails the difference between MWEbased and token-based scores from the Table  2. Examples: -In Czech the verb is marked as reflexive, but the particle is not tagged as the continuation of the MWE -Some of the LVC part is not tagged, generally it is a predicative noun. E. g. in Polish mieć problem -'have problem' the word problem was not tagged. -The particular case is analytical tense formation, like e. g. future tense in Czech. In the MWE se bude hodit -'will be useful' mwe_tagger marked only the reflexive particle and the verb, but not the auxiliary verb bude -'will be' which has to be annotated according to the annotation guidelines, so it was also penalized by the evaluation script.
IReflV IREflV is the most frequent MWE tag, and it is relatively easy to identify reflexives in the text with the help of some rules. However, the mwe_tagger encountered several problems that we will demonstrate for a few languages: • it is hard for an algorithm to distinguish between inherently reflexive verbs and other very structurally similar "deagentive", passive or reciprocal constructions, more see  (Kettnerová and Lopatková, 2014) ,  (Bejček et al. , 2017)  or the guidelines manual 6. E. g. in Bulgarian, se ubedjat -'(they will) be convinced' was tagged by the mwe_tagger, but it was just passivisation from ubedjat -'convince', not the true reflexive verb. In Polish oblizuj ąc się -'licking (lips)' was also tagged, whereas it should not according to the guidelines definition. • For French, there are two forms that clitic takes -full and contracted (in case it comes before a vowel).
Conclusion We have presented the system MUMULS that participated in the shared task of identification of MWEs. MUMULS was a neural network deployed within the framework TensorFlow that learned to detect MWEs based on manually annotated corpora. Overall, the systems participating in the closed track for some languages have approximately the same F-score while for others it may vary. The results of the shared task might as well depend on the consistency and quality of the annotations of the training data. We are waiting for further details on other approaches so as to be able to better understand why our system outperformed other systems for some languages, and why it underperformed for some others.
Pigment identification A chromatographic and spectral library was created from standard pigments provided by DHI lab products (Denmark). The pigments identified with a pure standard were: chlorophyll b (Chl b), chlorophyll a (Chl a), pheophorbide a methyl ester (Pheide a methyl ester), ); CaCO were identified by comparison of chromatographic and spectral data recorded with standard pigments, using ChemStation software (Agilent). Unidentified pigments were also stored in the spectral library. Identification of pigments was realised according to software retention time and match factor result. The software compared retention times and aligned absorption spectra (from 210 to 800 nm), to calculate a match factor comprised between 0 and 1000, representing the degree of similarity between spectra.
Determination of pigment communities among phytoplankton strains The inter-strain comparison of pigment composition was studied and graphically represented using the open source Gephi communities' network software (http://gephi. github. io/) as recommended in  [15]. All strains and pigments (whether identified or not) were entered in the software and pigments were associated with their respective strains only by a qualitative link (presence of the pigment in the strain). The software grouped the strains together in communities sharing common pigments. Circles with a high diameter represent strains, while circles with a small diameter represent pigments. The validation of the communities was illustrated by the central circles of beta-carotene and chlorophyll a (including Chl a epimer), which were common to all strains.
Pigment composition of the 37 phytoplankton strains The pigment chromatograms recorded at 436 nm are presented in Supporting Information S2 Fig. Chlorophyll a ( and epimer ) and β,β-carotene were common to all strains. In addition, each strain contained approximately 10 major and minor pigments, identified using the standard pigment database. All pigments detected among the biodiversity of the 37 strains were classified according to their HPLC retention times (Table  2 ). This analysis revealed that, in addition to the 26 standard pigments, 98 molecules corresponding to unidentified pigments or standards derivatives were present in the chromatograms. According to their spectral properties (presence or absence of absorption bands I, II and III), 57 pigments were classified as carotenoid derivatives (Car), 35 as porphyrin derivatives (P) and six pigments with spectral properties of both were named PCar.
In this case, it is important to keep a watchful eye on wavelength maxima and the injection of a standard mix between a couple of samples and/or add a non-natural internal standard to each natural sample. Generally, we can consider that retention time requires a loose tolerance in the first 10 min of the elution with the Van Heukelem and coauthors method  [13]  and identification should rely more on spectra similarities. Finally, a data exploration can be based on different strategies that are more or less sophisticated and computationally managed  [44, 45]. For example, results can be managed in clusters  [46]. Non-taxonomic interpretations of pigment datasets can be realized by pigment indices and by ecological similarity indices  [47]. To compare UV-visible spectra, we worked with the algorithm provided by ChemStation (Agilent Technologies) to provide identification scores based on the shape of the spectrum and on relative intensity of the lambda max.
Perspectives for future research Major pigments of microalgae are well known and easily identified by UV-DAD detector in standard chromatographic conditions. However, when biologists or oceanographers conduct field studies on phytoplankton samples, they are confronted with a large chemodiversity including minor or variable pigments, pigments present in inconsistent quantities, and trace pigments. Using all this data in a rational way to understand the dynamics of phytoplankton populations remains a challenge. In this study, we proposed a guide to several specific chemotaxonomic markers that may be used to explore microalgae pigment diversity/complexity, and pinpointed important factors that can modulate data interpretations. Ninety-eight pigments were new or undescribed carotenoid or porphyrin derivatives with original spectral properties, but whose planar structure, relative structure and absolute configuration remain to be established.
Acknowledgments The authors thank  Prof. Claire El-Fakir  ( University of Orleans, France ) and  Josephine Ras (Oceanographic laboratory of Villefranche-sur-Mer, France)  for their plentiful advice about pigment analysis. We thank also  Dr. Patrick Lassus  and  Dr. Andrea Contreras  ( Laboratory of phycotoxins-IFREMER Nantes ) for kindly providing the Alexandrium strains. We wish to thank our colleagues  Marie-Lise Picquet ,  Catherine Rouxel  and  Nathalie Schreiber  from the  laboratory of Physiology and Biotechnology of Algae (IFREMER Nantes)  for their assistance in the cultivation of several microalgae strains.
This research was supported by a doctoral fellowship grant from  Region Pays de la Loire for BS's PhD  ( N˚2008-09351 ), and by  French National Agency for Research  ( ANR-08EBIO-0016 ) for  PHOTOMER  project. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. UTEX  FW-Walne's medium Scenedesmus obliquus UTEX 1450 So UTEX FW-Walne's medium Haematococcaceae Haematococcus pluvialis CCAP 34/7 Hp CCAP FW-Walne 's medium Dunaliellaceae Dunaliella sp. CCAP 19/19  Dsp CCAP SW-Walne's medium Dunaliella salina SAG 19-3 Ds SAG SW-Walne's medium Trebouxiophyceae Chlorellaceae Chlorella autotrophica CCMP 243 Ca CCMP SW-Walne.
Background: Data collected in EHRs have been widely used to identifying specific conditions; however there is still a need for methods to define comorbidities and sources to identify comorbidities burden. We propose an approach to assess comorbidities burden for a specific disease using the literature and EHR data sources in the case of autoimmune diseases in celiac disease (CD). Methods: We generated a restricted set of comorbidities using the literature (via the MeSH® co-occurrence file). We extracted the 15 most co-occurring autoimmune diseases of the CD. We used mappings of the comorbidities to EHR terminologies: ICD-10 (billing codes), ATC (drugs) and UMLS (clinical reports). Finally, we extracted the concepts from the different data sources. We evaluated our approach using the correlation between prevalence estimates in our cohort and co-occurrence ranking in the literature.
Biomedical literature and its metadata can also be mined to extract knowledge for various purposes  [27] : precision medicine and drug repositioning, pathway extraction, gene function prediction, data integration, pharmacogenomics, toxicology. As the biomedical literature also explores comorbidities associated to diseases, its metadata could provide information on relevant comorbidities for a given disease. The present study aimed to show how a workflow based on both literature and EHRs information can help identifying relevant autoimmune comorbidities in CD and to phenotype for these autoimmune comorbidities the population of adult CD patients followed up in Georges Pompidou European Hospital (HEGP). We assessed its performance in this context by assessing quantitatively whether literature-based knowledge was correlated to knowledge extracted from EHRs regarding autoimmune CD comorbidities.
Overview We first selected the list of autoimmune diseases from MeSH® terminology. We then restricted this list to the most frequent autoimmune diseases associated with CD in the literature, based on the number of co-occurrences of MeSH® terms in MEDLINE®. We mapped selected autoimmune disorders to different terminologies to identify concepts and terms for data collection. Then we included CD patients from a local registry and completed by querying the hospital clinical data warehouse (CDW). Finally we identified status for selected autoimmune diseases for these patients by reviewing their EHR for mapped concepts.
MeSH® mapping to drugs and diagnoses terminologies We used the Unified Medical Language System (UMLS) to map autoimmune disorders MeSH® concepts to other terminologies used in the CDW. To leverage diagnosis codes we used the International Classification of Diseases version 10 (ICD-10). We used the Anatomical Therapeutic Chemical Classification System (ATC) whenever a drug was specific to an autoimmune disease. These terminologies (MeSH, ICD-10 and ATC) also provided terms to constitute a catalog of words for helping textual data review.
Study population The HEGP hospital has a specialized consultation for CD patients with about 50 new patients recruited per year in the last decade. The gastroenterology department has maintained a list of patients with CD for research purposes. The study has been approved and data access granted by the following commissions: the "Commission Nationale de l'Informatique et des Libertés" (declaration #174350) and the HEGP institutional review board (registration #00001072). To complete the list of patients maintained by the gastroenterology department, we also extracted patients fulfilling the three following criteria: (i) at least one encounter in the 2000-2014 period of time (ii) presence of at least one ICD 10 code for CD (K90) in billing claims; (ii) one or more hospitalization stay or consultation in the gastroenterology department and (iii) at least one text document (discharge or letter) containing the term 'celiac disease' or one of its synonyms.
Autoimmune diseases phenotyping We queried the CDW to identify for each patient the presence/absence of the selected autoimmune diseases. We used the selected ICD-10 diagnosis codes to query billing data and selected ATC codes to query Computerized Prescription Order Entry data. We extracted every narrative report including discharge summaries, outpatient reports, multi-disciplinary expert meeting summaries and letters from all departments of the hospital and reviewed them manually to extract selected autoimmune disease status for each patient. To facilitate the manual review, we developed a browser-accessible software, FASTVISU  [29] , interfaced with the CDW to display and explore all the documents for a given patient. FASTVISU highlights terms with an entity recognition module based on a list of regular expressions (e.
Moreover, our study benefited from the coverage of the CDW, which includes a long follow-up and, therefore, increases the probability of mentioning an associated autoimmune disease. The quality of this longitudinal source of information was measured by Sperrin's coefficient, which demonstrates a broad coverage of text documents during the follow-up period. In contrast, prevalence studies based on questionnaires  [14, 15, 21]  may underestimate prevalence, e. g. , due to memory bias. It would have been of interest to extract diagnosis date, but as we expected many missing data and approximations due to early childhood diagnoses, we did not extract this information. Text reports were more sensitive than ICD codes and medications for detecting autoimmune comorbidities In this study, most diagnoses were ascertained through text reports.
Automatable and RECORD statement compliant workflow The workflow of this study, i. e. comorbidities selection from the MeSH® co-occurrences file, mapping from MeSH® to other terminologies, and case identification through text and coded data mining, can be automated to estimate comorbidities burden in other EHR-based studies. Our workflow is in line with RECORD statement  [32] , in particular reporting a complete list of codes and algorithms for each comorbidities. Moreover, we demonstrated that manual review could be performed easily using text visualization tools integrated with the CDW, even for non-English language based EHR  [29]. Phenotyping quality is sometimes considered as a limit of EHR reuse. In the proposed workflow, we reinforce phenotyping quality by manual phenotype extraction by two readers in reasonable time thanks to assisted extraction using a visualization tool, FASTVISU.
Typical Use Case The sanitized release must support exploration, analysis and scientific studies. The very first and popular processing of sanitized release is to search and filter tabular data by means of point queries and window queries. A point query retrieves the records where the predicate matches the exact value e. g. , find an employee whose SSN is "3456". In other words, point query relates to an exact point in an n-dimensional space. A window query or range query retrieves all of the records where some value is between an upper and lower boundary e. g. , list of all employees with age between 23 to 25. The result of a window query is a set of points matching a specified range or window. Indeed, regular database records can be geometrically interpreted as points in a multidimensional space where each dimension is a column of the raw table.
Point coordinates are then defined by the attribute values. Transformation is obvious for numerical and ordinal variables. Categorical variables could also be equipped with a total ordering, except that without any native ordering, the process is driven by the application domain and background knowledge. Thus, database queries are transformed into queries against a set of points. Once the microdata is sanitized, its records become hyper-rectangles in a multidimensional space, where each dimension is a field in the set of quasi-identifiers. For instance, sanitized records from Table  1  are cuboids in the three-dimensional (3D) space (Age, Zipcode, Gender) as shown in Figure  1. As a point query example Q 1 , user would filter data to retrieve possible patient's record designated by Age = 62 AND Zip Code = 44120 AND Gender = F.
There exist many efficient algorithms and data structures  [2]  to compute such orthogonal range queries against the spatial representation of the anonymous database. Furthermore, since any orthogonal range query can be decomposed into several one-dimensional (1D) range queries, it is easy to manage filters on the tabular representation of the public release within a basic spreadsheet or web-client technologies as well. Query Q 2 over Table  1  gives an example of such straightforward decomposition. These practical features are very useful in lots of iterative exploration processes that would support analysis and scientific studies. Then, we argue that the axis-parallel rectangular coding of anonymous records is a strong requirement for a generic Privacy Preserving Data Publishing PPDP task.
Usually, the Mondrian approach  [8]  is thought of as the baseline algorithm since it has many interesting properties that we could expect from such algorithms: local recoding and multidimensional partitioning. Mondrian iteratively operates a binary partitioning of the data space until every block contains between k and 2k -1 data points. Actually, Mondrian builds a kd-tree over the raw data and publishes bounding boxes of the leaves as equivalence classes of the anonymous release. Construction has time complexity O(N log(N)), where N is the number of records in raw data. Following the geometric representation of the data, Iwuchukwu et al. [9]  propose using a bulk-loading implementation of an R + -tree, one of the most popular spatial access methods for databases, to compute the k-anonymous release.
Therefore, many good features of the R + -tree approach makes it the reference algorithm for k-anonymization up until now. Many works also proposed point partitioning structures in low dimension (2-3D) for privacy preserving location-based queries  [10] [11] [12] [13]. In this application domain, privacy is related to instant location of users and queries as well. Popular approaches design an anonymizer that dynamically provides a Cloaking Region to the Location-Based Service. For that purpose, Gruteser et al. [11]  implements a kd-tree, whereas Mokbel et al. [13]  uses a variant of a Point Region (PR) quadtree in Casper. Ghinita et al. [10]  accommodate partitioning structures from kd-tree and R-tree to hash a database of Points Of Interest (POI) and answer approximate nearest-neighbor queries in a Privacy Information Retrieval (PIR) approach (a PIR protocol allows a user to fetch a tuple from a database while concealing the identity of the tuple from a database server)  [14].
For instance, the root of the BANG tree directory from Figure  6  contains two entries: D and A!. D is the sub-space spanned by the left child node, whereas A! = A -D is the sub-space spanned by the right child node. Note that the second A! of the tree is defined as follows: A! = A -(B ∪ D). (5) The partitioning algorithm is simple yet efficient. As any kd -B-tree, its time complexity is O(NlogN). For external data, I/O cost still remains in O( N B. log N B ) with B the disk block size. It performs incremental insertion of data points in a top-down manner. The enclosing grid region identifier is first computed thanks to the mapping scheme discussed before. The all path up to the root (the entire space) is also retrieved. Then, the BANG directory is searched for the smallest recorded region that encloses the data point.
It is then assigned to the corresponding bucket. When data bucket overflows, the algorithm operates splitting to balance the distribution of points between buckets. When a bucket A overflows, the value space is split and a new bucket B is created, and those records that lie in one half of the space are moved from the old bucket to the new one. Splitting is done by iteratively halving the space spanned by points in the bucket until the best balance is achieved. It gives birth either to a buddy region or to a newly enclosed region. Further details on splitting strategies can be found in  [34]. The iterative halving strongly differs from the R + -tree splitting strategy. Indeed, the BANG file operates from the entire space to blocks (top-down), whereas the R + -tree operates from points to blocks (bottom-up).