dataesr/scientific-paragraphs-categorization huggingface dataset:
The reason to design a system capable to extract information from multiple ontologies is to represent several domains and/or decrease their lack of coverage. Thanks to ontologies, MatchingSem can create semantic networks that are matched using the Jaro-Winkler distance. I. M. P. A. K. T. (Tinelli et al. , 2017)  is a platform that allows HRM ranking candidates automatically and obtain the reasons of putting a résumé in a certain position. It is based on Relational database Management Systems which help in the creation of improved knowledge bases. As well, the platform allows defining which competences are required and which are only desired. I. M. P. A. K. T. offers to HRM information about conflicts or underspecified features found in a résumé. Another résumé ranker is the one detailed in our previous work  (Cabrera-Diego, 2015).
Data For this article, we used a set of 171 job postings which were processed (recruitment and selection) by a French human resources enterprise between November 2008 and March 2014. These job postings come from different professional domains (e. g. , chemistry, communications, physics and biotechnology) and position levels (e. g. , laboratory researcher, intern, project manager and engineer). These 171 job positions were chosen because they contain at least 20 unique résumés in French; at least 5 of them are relevant, and 5 are irrelevant. 14 In total, the corpus contains 14, 144 French résumés divided among these 171 jobs postings. All the job postings are composed of applications, and each application contains the documents associated with the recruitment and selection process. It is important to note that not all the documents located inside the applications corresponded to résumés; we could find motivation and recommendation letters, diplomas, interview minutes and social network invitations as well.
The first is that to determine whether an applicant will be hired implies the analysis of elements that are not present in a résumé (e. g. , interview results, expected salary, job location and withdrawal). The second one is that we do not want to replace humans with an automaton in the selection process. Instead, we want to assist humans during the most difficult part of the selection process, which is in discerning relevant and irrelevant applicants. And this can be achieved by ordering applicant's résumés in terms of how relevant are for the job posting. It is important to note that some applications from the corpus, although impossible to trace, started as Direct contact. This means that an HRM found, usually on the Internet or job seekers databases, the résumé of a person who fulfilled the person specification and decided to contact this person directly.
Job offers are pre-processed under the same parameters that the résumés, as explained in Section 4. Although a comparison with other methods or systems from the state-of-the-art would have been desired, to the extent of our knowledge, none of the systems or datasets have been released to the public. 21  In the case of the experiments with Relevance Feedback, we have restricted the feedback size to a range between 2 and 20 résumés. It should be noted that we never use more than 50% of the résumés for each job as feedback. In fact, the 171 job postings described in Section 4 were chosen because they had at least 20 résumés, from which at least 5 were from relevant applicants and 5 from irrelevant ones. When we use more than 10 résumés for the Relevance Feedback, we always verify that there is at least twice the résumés for the job posting, with more than 1/4 of them being relevant and no less than 1/4 irrelevant.
Thus, as this n-gram could not have been seen by the HRM during the Relevance Feedback, we must consider another n-gram as the one in the first place. This will be the first term seen in the Relevance Feedback that has the best squared probability and factor fc. For the following terms the rules are the same. 21 The only exception could be LO-MATCH, which provided a service through a website during a time. However, the software, per se, was never available to download for testing purposes. In the experiments related to the iterative application of Relevance Factor, we explore how rankings are affected when multiple and sequential Relevance Factor processes are done. In other words, we start by doing a Relevance Factor over 2 résumés. This process will rank the remaining résumés of the job posting in an improved way.
Despite these methods were created to be used in a particular database, where it was impossible to have access to every job offer, we believe that it can be used in any database of résumés, only if these are separated by job postings. Furthermore, the methods here presented do not make use of any kind of semantic resources, which can make them easier to implement in under-resources languages. There are still things that must be studied with this kind of methods. In the first place are the temporal aspects. We assumed in this article that all the résumés were present at the same time, but in real life this may not be true. On occasions, the process of recruitment and selection are done in parallel, i. e. , once a résumé arrives to a human resource manager, it is analyzed. We have to consider as well the evolution of the person specification over time.
These representations are not the same, and this can infuse difficulty to some degree in the application of our methods. Put differently, a concept may be difficult to represent with an n-gram. Finally, it should be analyzed the economics and whether human resource managers will adopt these methods to make their tasks easier. Regarding the scalability of the methods here presented, we do not observe any particular problem. As we indicated in Section 5, the methods were called using the program GNU Parallel, meaning that each job posting was analyzed using different CPU threads. This indicate that multiple job postings can be processed at the same time without any collision. Furthermore, it is possible to parallelize the similarity between résumés, i. e. , to use several threads to calculate multiple Dice's Coefficient scores at the same time.
The Tracking Ultraviolet Setup (TUS) detector was launched on April 28, 2016 as a part of the scientific payload of the Lomonosov satellite. TUS is a pathfinder mission for future space-based observation of Extreme-Energy Cosmic Rays (EECRs, E > 5×10 19 eV) with experiments such as K-EUSO. TUS data offer the opportunity to develop strategies in the analysis and reconstruction of the events which will be essential for future space-based missions. During its operation, TUS has detected about 80 thousand events which have been subject to an offline analysis to select among them those that satisfy basic temporal and spatial criteria of EECRs. A few events passed this first screening. In order to perform a deeper analysis of such candidates, a dedicated version of ESAF (EUSO Simulation and Analysis Framework) code as well as a detailed modelling of TUS optics and detector are being developed.
Data Analysis of EECR mode During its operation, TUS detected about 8×10 4 events that have been subject to an offline analysis to select among them those satisfying basic temporal and spatial criteria of EECRs. A few events passed this first screening. In order to perform a deeper analysis of such candidates, a dedicated version of ESAF (EUSO Simulation and Analysis Framework)  [23]  as well as a detailed modeling of TUS optics and detector are being developed. The search for EECR signals in the data requires different steps. First of all, the signal selection in the ADC time track is performed. In one of the approaches, the signal significance is used. The signal in one packet of 256 time ticks is divided in five parts, and the average SNR value is determined in each time window. If there is a window with an average SNR > 2, the pixel is assumed to have a signal excess.
Simulation Developments ESAF is the simulation software developed in the framework of the ESA-EUSO mission. This software was developed as the mission simulation software to take care of the simulation of all the relevant processes from the shower simulation until the event reconstruction. ESAF is subdivided in two main parts: Simulation and Reconstruction. The simulation framework is meant to simulate all the physical processes which are related to the shower development, the light production and propagation, the detector and eventually the telemetry. Several shower simulators are implemented in ESAF, following parametric and Monte Carlo approaches. As parametric generator, the Greisen-Il'ina-Linsley (GIL) function  [30]  is used to reproduce the profile for hadronic EASs: N e = E 1. 45 • 10 9 eV • e t-t max -2•t•ln 2 1+ tmax t (1) whereas N e is the shower size, t = X 37.
The ESAF software was readapted for the JEM-EUSO instrument  [29]  and for all the other missions of the JEM-EUSO program. Currently, it is being updated to include also the response of the TUS detector. Several optics simulation approaches have been considered in ESAF. The simplest and fastest one is a parametric simulation module that calculates analytically the position of the photon on the FS and adds a Gaussian spread around this position. This is intended to be a fast working tool to test the features of the different optics designs in an approximated way. This is the one which is currently used for TUS as well. Second, a full ray-trace code used in the actual optics design. This is under development also for the TUS detector and will be employed in future. Once the photons reach the FS, they are transported through the filter and the optical adapter before reaching the photocathode.
Acknowledgments The work was done with partial financial support from the  State Space Corporation ROSCOSMOS, M. V. Lomonosov Moscow State University  through its  Prospects for Development program  and the  Russian Foundation for Basic Research  grant No. 16-29-13065  and  15-02-05498/17-a. The Italian group acknowledges financial contribution from the agreement ASI-INAF n. 2017-14-H. O and from the  Italian Ministry of Foreign Affairs and International Cooperation.
Informed written consent was obtained from each participant after explaining the nature of the experimental procedures. The research followed the principles of the Declaration of Helsinki and was approved by the Ethics Committee of Eye Research Center, Tehran University of Medical Sciences. Three monthly bilateral injections of bevacizumab (Avastin; Genentech, South San Francisco, CA) 1. 25 mg/0. 05 mL were performed for all patients. The enrolled patients received a therapeutic regimen of topical dorzolamide hydrochloride 2% and timolol maleate 0. 5% (Zilomole; Sina Darou, Tehran, Iran) twice per day in the right eye during the study period. To run the statistical analysis, Snellen acuities were converted to logarithm of the minimum angle of resolution (log-MAR) equivalent values. The gleaned data were analyzed with SPSS16 (SPSS, Inc.
The NA61/SHINE measurements for T2K The NA61/SHINE experiment took data for T2K in 2007, 2009 and 2010. In 2007 and 2009, measurements were performed with a 30. 92 GeV/c proton beam and a 2-cm-thick carbon target (4% of a nuclear interaction length, λ i ) to measure hadron multiplicities (π ± , K ± , p, K 0 s , Λ ) and the production cross section  [5] [6] [7] [8]. Production events represent the fraction of inelastic events in which at least one new hadron in the final state is produced. By using these measurements, the uncertainty on the neutrino flux in T2K was reduced to about 10%  [4]. However, the hadron production component of the uncertainty still dominated the total uncertainty due to the insufficient precision of the production cross section measurements and re-interactions inside the target and aluminum horns, which cannot be directly constrained by the measurements of the primary protoncarbon interactions.
In other words, vertices inside the target are not reconstructed. Tracks measured in a detector are only extrapolated to the target surface. Essentially, the target is treated as a black box and measured hadron yields are the sum of yields coming from primary interactions and re-interactions. In addition, T2K requires that the longitudinal position along the target surface is binned in at least six bins: five longitudinal bins 18 cm in size and the downstream target face as a sixth bin  [13]. Since the T2K target is inserted in the first focusing horn, hadrons coming from different parts of the target will have different paths through the magnetic field and they will be focused differently by the horns. The previous NA61/SHINE measurements with 2007 and 2009 data have already proven that the NA61/SHINE setup is suitable for measuring hadron yields on the T2K replica target surface, although the target is placed outside of the tracking system.
NA61/SHINE experimental setup The NA61/SHINE setup during the T2K replica target data-taking in 2010 consisted of five Time Projection Chambers (TPCs), three Time-of-Flight walls (ToF-F, ToF-L and ToF-R), three Beam Position Detectors (BPD-1, BPD-2, BPD-3), a 90-cm-long graphite target, five scintillator counters and two Cherenkov detectors. Two of the vertex TPCs (VTPC-1 and VTPC-2) are inside the magnetic field created by two superconducting magnets. For the study presented here the magnetic field of the dipole magnets was set to a bending power of 1. 14 T m (standard magnetic field), while a small subset of data was also taken with the full magnetic field of 9 T m. A schematic overview of the setup is presented in Figure  1. More details can be found in Ref. [1]. The coordinate system is defined as follows: the z-axis is in the nominal direction of the beam, the x-axis in the horizontal plane is such that positively charged particles are bent in the positive x-direction, and the y-axis is perpendicular to the horizontal plane and points upward.
This allowed the recording of beam protons that pass through the target without interacting and the measurements of their parameters in the TPCs. However, all secondary hadrons below 6 GeV/c were bent out of the TPCs and did not reach the ToF-F wall. The analysis of data recorded with the maximum magnetic field is a subject of an independent publication. In this paper, the data taken with the standard magnetic field configuration (89. 3% of triggers) were analysed, while the high magnetic field subset was used for alignment between the BPDs and TPCs (see Subsection 3. 1).
Analysis The reconstruction and simulation procedures used for the replica target analysis are described in earlier publications  [9, 10]. The analysis of the data taken with the standard magnetic field configuration was performed with the so-called to f -dE/dx method, which makes use of the TPC measurements of the specific energy loss (dE/dx) and the time-of-flight measurements  [9, 10]. The specific energy loss of a track is calculated as a truncated mean  [16]  of the charges of the clusters (points) on the track traversing the TPCs. After event and track selection, selected tracks were binned in momentum (p), polar angle (θ ), both measured in the laboratory frame, and longitudinal position of the exit point on the target surface (z), as required by T2K. The different hadron yields were estimated by fitting a two-dimensional dE/dx-m 2 to f distribution for every bin.
Target position and alignment The target upstream face x and y positions with respect to the BPDs can be determined just by plotting the T 2 beam profile at the target upstream z position. The position of the target centre was obtained by maximising the number of entries in the circle with radius equal to 1. 3 cm. This was only possible because S 3 counter, which was part of the trigger, had the same radius as the target and it was placed just upstream of the target. The target upstream z position was measured by surveyors before the data-taking. Once the relative alignment of the target and BPDs was known, the alignment of the TPCs with respect to the BPDs could also be determined. To do so, the data taken with the high magnetic field configuration were used to select events with only one high momentum track corresponding to the beam track.
Details of the currently used re-weighting procedure based on NA61/SHINE thin-target measurements are presented in Ref. [4]. The weights are simply calculated as the ratio of the data yields and the FLUKA2011 yields for each (p, θ , z) bin as follows: W i jk = 1 N POT n i jk ∆ p i jk ∆ θ i j data / 1 N POT n i jk ∆ p i jk ∆ θ i j MC (8) These weights are applied to all neutrinos that have a hadron ancestor exiting the target surface in the phase space covered by NA61/-SHINE. As previously mentioned, hadron yields coming from the surface of the long target depend on the width and position of the incoming proton beam hitting the upstream face of the target. Since it was shown that this effect is mostly geometrical, there is the possibility that the re-weighting factors are invariant under the beam profile change.
To test this hypothesis, FLUKA2011. 2c. 5 was used as input for data and the NUBEAM physics list from GEANT4. 10. 03 as input for the Monte Carlo. Around 40 × 10 6 protons on target were simulated for NA61/SHINE T 2 and T 3 beam profiles using both models. Re-weighting factors were calculated for both beam profiles and compared. Examples of the ratios for π + and p are shown in Fig. 55. Some differences can be noticed for the upstream z bins, while for other bins any differences are mostly below 2%. It is important to note that the differences between the T 2 and T 3 beam profiles are very large in comparison to differences between the T 2 and T2K beam profiles. Thus, one can use the measured NA61/SHINE double differential hadron yields in the T2K flux simulation in the following way: (i) Run the simulation of interactions inside the target for the NA61/-SHINE T 2 beam profile.
Acknowledgements We would like to thank the  CERN EP, BE  and  EN Departments  for the strong support of NA61/SHINE. This work was supported by the  Hungarian Scientific Research Fund  (Grants  NKFIH 123842-123959 ), the  János Bolyai Research Scholarship of the Hungarian Academy of Sciences , the  Polish Ministry of Science and Higher Education  (grants  667/N-CERN/2010/0 ,  NN 202 48 4339  and  NN 202 23 1837 ), the  Polish National Center for Science  (grants  2011/03/N/ST2/03691 ,  2013/11/N/ ST2/03879 ,  2014/13/N/ST2/02565 ,  2014/14/E/ST2/00018 ,  2014/15/B/ST2/02537  and  2015/18/M/ST2/00125 ,  2015/19/N/ST2 /01689 ,  2016/23/B/ST2/00692 ,  UMO-2014/15/B/ST2/03752 ), the  Russian Science Foundation , grant  16-12-10176 , the  Russian Academy of Science  and the  Russian Foundation for Basic Research  (grants  08-02-00018 ,  09-02-00664  and  12-02-91503-CERN ), the  Ministry of Science and Education of the Russian Federation , grant No.
Named entity recognition (NER) is widely used in natural language processing applications and downstream tasks. However, most NER tools target flat annotation from popular datasets, eschewing the semantic information available in nested entity mentions. We describe NNE-a fine-grained, nested named entity dataset over the full Wall Street Journal portion of the Penn Treebank (PTB). Our annotation comprises 279,795 mentions of 114 entity types with up to 6 layers of nesting. We hope the public release of this large dataset for English newswire will encourage development of new techniques for nested NER.
Introduction Named entity recognition-the task of identifying and classifying entity mentions in text-plays a crucial role in understanding natural language. It is used for many downstream language processing tasks, e. g. , coreference resolution, question answering, summarization, entity linking, relation extraction and knowledge base population. However, most NER tools are designed to capture flat mention structure over coarse entity type schemas, reflecting the available annotated datasets. Focusing on flat mention structures ignores important information that can be useful for downstream tasks. Figure  1  includes examples of nested named entities illustrating several phenomena: • Entity-entity relationships can be embedded in nested mentions. For instance, the location of the 'Ontario Supreme Court' is indicated by the embedded STATE mention 'Ontario'; • Entity attribute values can be embedded in nested mentions.
To facilitate ongoing research on nested NER, we introduce NNE-a large, manuallyannotated, nested named entity dataset over English newswire. This new annotation layer over the Wall Street Journal portion of the PTB includes 279,795 mentions. All mentions are annotated, including nested structures with depth as high as six layers. A fine-grained entity type schema is used, extending the flat BBN  (Weischedel and Brunstein, 2005)  annotation from 64 to 114 entity types. We are publicly releasing the standoff annotations along with detailed annotation guidelines and scripts for knitting annotations onto the underlying PTB corpus. 1 Benchmark results using recent state-of-the-art approaches demonstrate that good accuracy is possible, but complexity and run time are open challenges. As a new layer over the already rich collection of PTB annotations, NNE provides an opportunity to explore joint modelling of nested NER and other tasks at an unprecedented scale and detail.
The NNE dataset Annotation Scheme: BBN  (Weischedel and Brunstein, 2005)  is a pronoun coreference and entity type corpus, annotated with 64 types of entities, numerical and time expressions. We use its flat entity schema as a starting point to design our schema. We analyzed existing BBN annotations to develop and automatically apply structured preannotation for predictable entity types. Additional fine-grained categories and further structural elements of entities, inspired by  Sekine et al. (2002)  and  Nothman et al. (2013) , are used to augment the BBN schema. We adhere to the following general principles when annotating nested named entities in the corpus: • Annotate all named entities, all time and date (TIMEX) and numerical (NUMEX) entities, including all non-sentence initial words in title case, and instances of proper noun mentions that are not capitalized.
• Annotate all structural elements of entities. These elements could be other entities, such as 'Ontario' (STATE) in 'Ontario Supreme Court' (GOVERNMENT), or structural components such as '40' (CARDINAL) and 'miles' (UNIT) in '40 miles' (QUANTITY:1D), as well as the internal structure induced by syntactic elements, such as coordination. • Add consistent substructure to avoid spurious ambiguity. For example, the token 'Toronto', which is a CITY, would be labeled as part 1 https://github. com/nickyringland/nested named entities of an ORG:EDU organization span 'University of Toronto'. We add layers of annotations to allow each token to be annotated as consistently as possible, e. g. , [University of  [Toronto]  CITY ] ORG:EDU. • Add additional categories to avoid category confusion. Some entities are easy to identify, but difficult to categorize consistently.
Sections 00 and 23 were doubly annotated, and section 02 was annotated by all four annotators. An additional 17 hours was used for adjudicating these sections annotated by multiple annotators. Dataset Analysis: The resulting NNE dataset includes a large number of entity mentions of substantial depth, with more than half of mentions occurring inside another mentions. Of the 118,525 top-level entity mentions, 47,020 (39. 6%) do not have any nested structure embedded. The remaining 71,505 mentions contain 161,270 mentions, averaging 2. 25 structural mentions per each of these top-layer entity mentions. Note that one span can be assigned multiple entity types. For example, the span '1993' can be annotated as both DATE and YEAR. In NNE, 19,144 out of 260,386 total spans are assigned multiple types.