dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgments We congratulate our colleagues in the CERN accelerator departments for the excellent performance of the LHC and thank the technical and administrative staffs at  CERN  and at other  CMS institutes  for their contributions to the success of the CMS effort. In addition, we gratefully acknowledge the computing centers and personnel of the Worldwide LHC Computing Grid and other centers for delivering so effectively the computing infrastructure essential to our analyses. Finally, we acknowledge the enduring support for the construction and operation of the LHC, the  CMS detector , and the supporting computing infrastructure provided by the following funding agencies:  BMBWF  and  FWF (Austria) ;  FNRS  and  FWO (Belgium) ;  CNPq ,  CAPES ,  FAPERJ ,  FAPERGS , and  FAPESP (Brazil) ;  MES (Bulgaria) ;  CERN ;  CAS, MoST , and  NSFC (China) ;  MINCIENCIAS (Colombia) ;  MSES  and  CSF (Croatia) ;  RIF (Cyprus) ;  SENESCYT (Ecuador) ;  MoER ,  ERC PUT  and  ERDF (Estonia) ;  Academy of Finland ,  MEC , and  HIP (Finland) ;  CEA  and  CNRS/IN2P3  (France);  BMBF ,  DFG , and  HGF (Germany) ;  GSRT (Greece) ;  NK-FIA (Hungary) ;  DAE  and  DST (India) ;  IPM (Iran) ;  SFI (Ireland) ;  INFN (Italy) ;  MSIP  and  NRF (Republic of Korea) ;  MES (Latvia) ;  LAS (Lithuania) ;  MOE  and  UM (Malaysia) ;  BUAP ,  CIN-VESTAV ,  CONACYT ,  LNS ,  SEP , and  UASLP-FAI (Mexico) ;  MOS (Montenegro) ;  MBIE (New Zealand );  PAEC (Pakistan) ;  MSHE  and  NSC (Poland) ;  FCT (Portugal) ;  JINR (Dubna) ;  MON, RosAtom ,  RAS ,  RFBR , and  NRC KI (Russia) ;  MESTD (Serbia) ;  SEIDI, CPAN, PCTI , and  FEDER (Spain) ;  MOSTR (Sri Lanka) ;  Swiss Funding Agencies (Switzerland) ;  MST (Taipei) ; ThEPCenter, IPST, STAR, and  NSTDA (Thailand) ;  TUBITAK  and  TAEK (Turkey) ;  NASU (Ukraine) ;  STFC  ( United Kingdom );  DOE  and  NSF (USA).
Identified Gaps Limited Past Events and Data to Inform Hazard Models (S1) Completeness and quality of historical earthquake data, needed to constrain seismic source parameters, varies greatly depending on the history of the investigated geographical region  (Stucchi et al. , 2004; Albini et al. , 2014). Event catalogs are generally too short to account for the source frequency needed to model large average return periods in PTHA. The description of earthquake recurrence and of the tail of the frequency-magnitude distribution is highly uncertain  (Kagan, 2002; Geist and Parsons, 2014; Rong et al. , 2014; Bommer and Stafford, 2016). In the attempt of constraining this uncertainty, seismic source parameters have been estimated globally using seismic or geodetic data or both (e. g. ,  Bird and Kagan, 2004; Bird et al.
Identified Gaps Lack of Understanding and Likelihoods for Tsunamigenic Landslide Volumes (L1) For submarine landslides, we refer to the reviews of  Huhn et al. (2019)  and  Harbitz et al. (2014). The challenge can be attributed to several factors: • Limited or insufficient mapping of past landslide occurrences. Their characteristics and lack of dating prevent constraining the age of the sediments without excessive uncertainty ranges (e. g. ,  Geist et al. , 2013). The new global landslide database initiative  (Clare et al. , 2019 ) is a good starting point for standardizing, but not yet complete enough for feeding LPTHA. Good data coverage exists for certain regions such as the Mediterranean  (Urgeles and Camerlenghi, 2013) , Gulf of Mexico  (Pampell-Manis et al. , 2016)  and the US East Coast  (Chaytor et al.
Existing Methods Exposure data provide information about the characteristics and location of people and assets at risk. There are several techniques for the acquisition of exposure data, with different degrees of resolution and precision  (Pittore et al. , 2017). Data from governmental agencies are most commonly used, as they are open and available in most developed countries. These data often provide coverage for the entire building inventory (e. g. , physical assets) and are regularly updated for asset management (e. g. , national technical maps) and fiscal reasons (e. g. , cadastral data). Different exposure databases exist. The Global Exposure Database-GED  (De Bono and Mora, 2014; De Bono and Chatenoux, 2015)  developed for GAR13 and updated later for GAR15  (UNISDR, 2013; 2015)  provides a global dataset at 5 km grid resolution at inland and 1 km at coastal locations, including data for buildings, their use, and exposed value.
The 2013 and 2015 versions of the GED served as the exposure databases for the global risk model by the United Nations Office for Disaster Risk Reduction, which considered earthquakes, hurricanes, tsunamis and riverine floods as hazards. The DRMKC Risk Data Hub WebGIS tool  (Antofie et al. , 2019)  has been developed to provide access and sharing of EU-wide information relevant for disaster risk management. Initiatives such as the Open Exposure Data (OED) with roots in proprietary catastrophe modeling and reinsurance industry, provide the opportunity to generate exposure data, including those relevant to tsunami risk, with interoperability between different modeling tools. These databases mainly contain data from census or remote sensing. A recent interview-based approach, relying on local practicing engineers with knowledge of building features, has been adopted for the compilation of building inventories at regional scales  (Polese et al.
Identified Gaps Lack of Detail (E1) Most available exposure data have not been collected for the purpose of tsunami risk assessment and may be missing important information for modeling tsunami fragility or vulnerability. For instance, population cadastral data are often collected at the municipal, district or residential unit level, requiring extra assumptions to determine the geographical distribution. Tsunami hazard intensities can vary considerably between two nearby locations. Accurate geo-localization of the exposed assets and people is needed to obtain robust results, necessitating a minimum resolution level for the exposure databases. While main building construction characteristics are often known, tsunami relevant features (e. g. , building lateral load resistance, foundation) are missing  (Rivera et al.
Lack of Tsunami Exposure Model and Taxonomy (E3) Significant efforts have been made in the earthquake risk community to define a common exposure taxonomy (e. g. , GED4GEM,  Silva et al. , 2018a; METEOR, Huyck et al. , 2019). However, these taxonomies do not contain all the required structural attributes for estimating tsunami risk such as geomorphological, land use, and land cover datasets, or number and size of openings in buildings. A recent development is GED4ALL, a multi-hazard taxonomy  (Silva et al. , 2018b) , which considers tsunami as a hazard. GED4ALL also discusses multiple asset types like buildings, people, infrastructure systems and crops. Common taxonomy and attributes are fundamental to avoid heterogeneity, especially when considering multiple asset types.
Lack of a Tsunami Consequences Database (R3) There is a lack of tsunami-specific consequence databases accounting for casualties and losses  (Yamao et al. , 2015). These types of databases exist for disasters in general (e. g. , EM-DAT) and more specifically for earthquakes  (So et al. , 2012; Cardona et al. , 2018). They are useful not only to keep a consistent record of past events and the affected regions but to disaggregate the impacts of large events in terms of losses (direct and indirect) and casualties (fatalities and injured), besides assessing the consequences in particular sectors (e. g. , road networks, heritage sites, etc. ) at different resolution levels. The information included in the consequences databases provides valuable data to validate and calibrate different components of the models (e.
The need for covering vast geographical scales, source diversity and related uncertainties, render LPTHA extremely challenging. For VPTHA additional difficulties arise due to the complexity of tsunamigenic volcano sources and triggers, but they are constrained spatially. MPTHA may benefit from a large meteorological data network allowing for (prototypical) forecasting as well as PTHA applications, but sensitivity to source parameters is still unconstrained. While modeling and parameterization of individual phenomena are possible, they are often excessively computationally expensive or highly uncertain due to missing constraints on input parameters. The multiple scales involved in PTHA from far-field propagation over oceanic distances to the need to resolve small scale inundation features while capturing physics and resolving uncertainties still represent an open challenge.
Considering the goals of the Sendai Framework for Disaster Risk Reduction and acknowledging the vast number of challenges outlined in the sections before, a concerted interdisciplinary effort to close the most pressing gaps is required. Attempts to gather expertize, facilitate exchange and development, and coordinate community efforts are represented by the Global Tsunami Model (GTM, 2020) and the COST Action AGITHAR. A thorough consolidation of available sources of information in openly accessible databases, documentation of standard workflows, unification of terminology and metrics, as well as information hubs need to be established.
TOPAZ4 uses the ensemble Kalman filter method (EnKF;  Sakov and Oke, 2008)  to assimilate remotely sensed sea level anomalies, sea surface tempera-ture, sea ice concentration, sea ice thickness and Lagrangian sea ice velocities (the latter two in winter only), as well as temperature and salinity profiles from Argo floats and icetethered profilers. Data assimilation is performed weekly. To force neXtSIM, we use the following daily-averaged variables from TOPAZ: the sea surface (0-3 m) ocean velocity, temperature and salinity (SST and SSS, respectively), and the mixed layer depth (MLD). We give more details of how they are used in Sect. 3. 1 below.
Sea ice drift from OSI SAF We use this product for evaluation of both our free run and our forecasts. It is not assimilated. To produce it, lowresolution ice drift datasets are computed on a daily basis from aggregated maps of passive microwave (e. g. SS-MIS, AMSR-E) or scatterometer (e. g. ASCAT) signals (all channels are used) using the continuous maximum crosscorrelation method (CMCC:  Lavergne et al. , 2010; Lavergne and Eastwood, 2010; Lavergne, 2010). Daily 48 h ice drift vectors can be obtained at a spatial resolution of 62. 5 km. As part of our evaluation we apply a filter on the uncertainty given in the product to remove less accurate observations. We usually take the maximum allowed 2 d drift uncertainty to be 20 km, which allows a reasonable sample size of vectors to compare the model to, and right throughout the year.
Initialisation of the model fields Before the model can be run it has to be initialised. (Note this is only done once and is not to be confused with the daily assimilation, which modifies the initial conditions for each new forecast). First, the triangular mesh for the destination domain is generated with Unref (a component of the open-source mesh-generation library GMSH: Geuzaine and Remacle, 2009) and using the Global Self-consistent, Hierarchical, High-resolution Shoreline Database (GSHHS)  (Wessel and Smith, 1996). Then model variables like the ice concentration, the ice and snow thicknesses, and the temperature and salinity of the slab ocean are initialised. We use the CS2SMOS product (Sect. 2. 5) to set the ice concentration and thickness, and we use the simulated sea surface temperature and salinity from TOPAZ4 (Sect.
Acknowledgements. We would like to acknowledge  Véronique Dansereau  for interesting discussions on the MEB and BBM rheology;  Abdoulaye Samaké , who parallelised the neXtSIM code; and  Mika Malila , who contributed to our evaluation scripts. Finally, we would like to thank  Sylvain Bouillon  and  Philip Griewank  for their original contribution to building a forecasting system around the neXtSIM sea ice model. Computing and storage resources were provided by  UNINETT Sigma2 -the National Infrastructure for High Performance Computing and Data Storage in Norway  (projects  NN2993K  and  NS2993K ). Financial support. This research has been supported by the  Norwegian Research Council (neXtWIM  (grant no. 244001 ) and  Nansen Legacy  (grant no. 276730 )), the  European Commission (SWARP  (grant no.
In this paper, we focus on epidemic event extraction in multilingual and low-resource settings. The task of extracting epidemic events is defined as the detection of disease names and locations in a document. We experiment with a multilingual dataset comprising news articles from the medical domain with diverse morphological structures (Chinese, English, French, Greek, Polish, and Russian). We investigate various Transformer-based models, also adopting a two-stage strategy, first finding the documents that contain events and then performing event extraction. Our results show that error propagation to the downstream task was higher than expected. We also perform an in-depth analysis of the results, concluding that different entity characteristics can influence the performance. Moreover, we perform several preliminary experiments for the low-resourced languages present in the dataset using the mean teacher semi-supervised technique.
Introduction The ability to detect disease outbreaks early enough is critical in the deployment of measures to limit their spread and it directly impacts the work of health authorities and epidemiologists throughout the world. While disease surveillance This work has been supported by the European Union's Horizon 2020 research and innovation program under grants 770299 (NewsEye) and 825153 (Embeddia). It has also been supported by the French Embassy in Kenya and the French Foreign Ministry. has in the past been a critical component in epidemiology, conventional surveillance methods are limited in terms of both promptness and coverage, while at the same time requiring labor-intensive human input. Often, they rely on information and data from past disease outbreaks, which more often than not, is insufficient to train robust models for extraction of epidemic events.
Epidemic event extraction from archival texts, such as digitized news reports, has also been applied for constructing datasets and libraries dedicated to tracking and understanding epidemic spreads in the past. Such libraries leverage the technology advantage of digital libraries by storing, processing, and disseminating data about infectious disease outbreaks. The work presented by Casey et al. [5]  is an example of such an initiative aiming at analyzing outbreak records of the third plague pandemic in the period 1894 to 1952 in order to digitally map epidemiological concepts and themes related to the pandemic. Although the authors used semi-automatic approaches in their work, the discovery of documents related to epidemic outbreaks was done manually and the entity extraction was largely performed through manual annotation or the use of gazetteers, which have their own limitations.
The challenge is more pronounced when dealing with neural network-based methods  [18] , where massive amounts of labeled data play a critical role in reducing generalization error. Another specific challenge for the extraction of epidemic events from news text is class imbalance  [19]. The imbalance exists between the disease and lo-cation entities, which when paired characterize an epidemic event. The large difference in the number of instances from different classes can negatively impact the performance of the extraction models. Another challenge relates to data sparsity where some languages in the multilingual setup have few annotated data  [15] , barely sufficient to train models that achieve satisfactory performance. In this study, we use a multilingual dataset comprising news articles from the medical domain with diverse morphological structures (Chinese, English, French, Greek, Polish, and Russian).
In this dataset, an epidemic event is characterized by the references to a disease name and the reported locations that are relevant to the disease outbreak. We evaluate a specialized baseline system and experiment with the most recent Transformer-based sequence labeling architectures. Additionally, error propagation from the classification task that affects the event extraction task is also evaluated since the event extraction task is a multi-step task, comprising various sub-tasks  [22, 13, 30]. The classification task filters the epidemic-related documents from the large collection of online news articles, prior to the event extraction phase. We also perform a detailed analysis of various attributes (sentence length, token frequency, entity consistency among others) of the data and their impact on the performance of the systems.
Thus, considering the aforementioned challenges, our contributions are the following: -We establish new performance scores after the evaluation of several pretrained and fine-tuned Transformer-based models on the multilingual data and by comparing with a specialized multilingual news surveillance system; -We perform a generalized, fine-grained analysis of our models with regards to the results on the multilingual epidemic dataset. This enables us to more comprehensively assess the proposed models, highlighting the strengths and weaknesses of each model; -We show that semi-supervised learning is beneficial to the task of epidemic event extraction in low-resource settings by simulating different few-shot learning scenarios and applying self-training. The remainder of this paper is organized as follows.
Related Work Several works tackled the detection of events related to epidemic diseases. Some approaches include external resources and features at a sub-word representation level. For example, the Data Analysis for Information Extraction in any Language (DAnIEL) system was proposed as a multilingual news surveillance system that leverages repetition and saliency (salient zones in the structure of a news article), properties that are common in news writing  [26]. By avoiding the usage of language-specific NLP toolkits (e. g. , part-of-speech taggers, dependency parsers) and by focusing on the general structure of the journalistic writing genre  [21] , the system is able to detect key event information from news articles in multilingual corpora. We consider it as a baseline multilingual model.
Dataset Due to the lack of dedicated datasets for epidemic event extraction from multilingual news articles, we adapt a freely available epidemiological dataset 1  , referred to as DAnIEL  [26]. The corpus was built specifically for the DAnIEL system  [26, 28] , containing articles in six different languages: English, French, Greek, Russian, Chinese, and Polish. However, the dataset is originally annotated at Today marks one year since the last case of polio was recorded in India when the virus paralysed an 18-month-old girl in Howrah, near Kolkata. If pending test results return absent of the virus in coming weeks, India will be removed from the list of endemic polio countries. But India still remains at serious risk of fresh outbreaks if the virus is brought back into the country from overseas, and polio experts say the country's massive immunisation regimen must be maintained.
the document level. We annotate the dataset to token-level annotations  [31] , a common format utilized in research for the event extraction task. The token-level dataset is made freely and publicly available 2. Typically in event extraction, this dataset is characterized by class imbalance. Only around 10% of the documents are relevant to epidemic events, which is very sparse. The number of documents in each language is rather balanced, except for French, having about five times more documents compared to the rest of the languages. More statistics on the corpus can be found in Table  1. In this dataset, a document generally talks about an epidemiological event and the task of extracting the event comprises the detection of all the occurrences of a disease name and the locations of the reported event, as shown in Figure  1.
Experiments Our experiments are performed in two setups: 1. Supervised learning experiments: -Our first experiments focus on the epidemic event extraction utilizing the entire dataset. -Next, like most approaches for text-based disease surveillance  [22] , we follow a two-step process by first applying document classification into either relevant (documents that contain event mentions) or irrelevant (documents without event mentions) and then performing the epidemic event extraction task through the detection and extraction of the disease names and locations from these documents.
Semi-supervised learning experiments: -For these experiments, we simulate several few-shot scenarios for the low-resourced languages in our dataset, and we apply semi-supervised training with the mean teacher method in order to assess the ability of the models to alleviate the challenge posed by the lack of annotated data. Models We evaluate the pre-trained model BERT (Bidirectional Encoder Representations from Transformers) proposed by  [11]  for token sequential classification  3. We decided to use BERT not only because it is easy to fine-tune, but it has also proved to be one of the most performing technologies in multiple NLP tasks  [11, 9, 38]. Due to the multilingual characteristic of the dataset, we use the multilingual BERT pre-trained language models and fine-tune them on our epidemic-specific labeled data.
Evaluation The epidemic event extraction evaluation is performed in a coarsegrained manner, with the entity as the reference unit  [29]. We compute precision (P), recall (R), and F1-measure (F1) at the micro-level (error types are considered over all documents). We chose DAnIEL  [26]  as a baseline model for epidemic event extraction. This is an unsupervised method that consists of a complete pipeline that first detects the relevant documents and then extracts the event triggers. The system considers text as a sequence of strings and does not depend on language-specific grammar analysis, hence can easily be adapted to a variety of languages. This is an important attribute of epidemic extraction systems for online news text, as the text is often heterogeneous in nature. Figure  2  presents the full procedure for the supervised learning experiments.
Holistic Analysis We now present the results of the evaluated models, namely the DAnIEL system and the Transformer-based models. We first observe in Table  2  that all the models significantly outperform our baseline, DAnIEL. As it can be seen in Table  2 , under relevant and irrelevant documents (1), when the models are trained on the entire dataset (i. e. , the relevant and irrelevant documents), the BERT-multilingual-uncased model recorded the highest scores, with a very small margin when compared to the other two fine-tuned models, the cased BERT and XLM-RoBERTa-base. In Table  2 , under ground-truth relevant documents (3), when evaluating the ground-truth relevant examples only, the task is obviously easier, particularly in terms of precision, while, when we test on the predicted relevant documents in Table  2 , under predicted relevant documents (2), the amount of errors that are being propagated to the event extraction step is extremely high, reducing all the F1 scores by over 20 percentage points for all models.