dataesr/scientific-paragraphs-categorization huggingface dataset:
The spectrum is computed by Fourier transformation of the measured interferogram. The design of the FTS is described in more detail in  Ade et al. (1999) ,  Dohlen et al. (2000) , and  Swinyard et al. (2010). This paper describes the pipeline processing steps necessary to convert the measured interferogram signal into a calibrated astronomical spectrum. The operation and calibration of the instrument is described in the SPIRE Handbook (2016) and by  Swinyard et al. (2014). This paper describes in detail the algorithms used for each pipeline step and provides a thorough description of the end-to-end processing of the FTS data. Section 2 briefly summarises the observing modes of the instrument, as they are relevant to the pipeline, Section 3 introduces the pipeline in terms of three data domains: the time domain (processing timelines from the detectors), the interferogram domain (signal in terms of OPD position), and culminates in the spectral domain.
Each observation consists of a number of simple operations, such as configuring the instrument, initialisation and science data taking. These operations are referred to as "building blocks". The science data building block is the one used by the FTS pipeline and is defined as a set of equal-length scans of the SMEC at a single pointing position of the Herschel telescope and BSM. The spectral resolution is determined by the maximum OPD, which is determined by the displacement of the SMEC. The spectral resolution options available to observers are shown in Table  1. 2 ). SSW detectors are represented by the small circles, SLW detectors are represented by the larger circles. The single large circle in each plot represents the 2 diameter field of view that is covered by the unvignetted detectors.
SPIRE SPECTROMETER PIPELINE The SPIRE spectrometer data processing pipeline consists of six major processing groups as shown in Fig. 2. (i) Common Photometer/Spectrometer Processing modules. These processing steps are common to both the SPIRE spectrometer and photometer pipelines  (Dowell et al. 2010). (ii) Modify Timelines. These processing modules perform operations on detector signals that are time-dependent. The descriptions of the modules in this category are presented in Section 3. 1. (iii) Create Interferograms. This processing step merges the timelines of the spectrometer detectors and spectrometer mechanism to produce interferograms. This step produces a Level-1 Spectrometer Detector Interferogram (SDI) product and is described in Section 3. 2. (iv) Modify Interferograms. The processing modules in this group perform operations on the spectrometer detector interferograms.
Interferogram Creation The pipeline modules listed to this point describe the operations on the Level 0. 5 timelines of the spectrometer detectors (Fig. 4a ). Three additional Level 0. 5 timelines are required for the next step in the common spectrometer data processing pipeline: the Spectrometer Mechanism timeline product (SMECT) (Fig. 4b ); the Nominal Housekeeping timeline product (NHKT) (Fig. 4c ); and the SPIRE Pointing timeline product (SPP) (see Fig. 2 ). A single building block of a SPIRE spectrometer observation in scanning mode consists of a series of scans of the spectrometer mechanism (SMEC) while the instrument is pointed at a given target. The sampling of the SPIRE spectrometer detectors and the spectrometer mechanism is not synchronized; the two subsystems are sampled at different rates and at different times.
Second level deglitching The second-level deglitching step is responsible for identifying and correcting any glitches that are not corrected by first-level deglitching (Section 3. 1. 1). This step relies on the principle that repeated FTS measurements of the same astronomical source should not deviate from one another beyond random noise. Glitches may then be identified for each spectrometer detector as those that, for a given OPD position, deviate by a given threshold from corresponding samples, taken at the same OPD, from all other scans in the same building block. Based on a combination of pre-launch and performance verification phase data  (Davis-Imhof et al. 2008) , the most effective statistical metric found for identifying second-level glitches is the windowed median absolute deviation (MAD) algorithm.
Remove Out of Band Flux This processing step removes, from each of the computed spectra, components corresponding to out of band frequencies. The lower and upper limits of the spectral passbands (νmin and νmax) were determined from performance verification data for each detector and are provided by a calibration product. V10-n-i(ν) = V9-n-i(ν)| ν max-i ν min-i (33) The equation for the total intensity of the radiation incident upon the spectrometer detectors shows that, in addition to radiation from the astronomical source, the detectors record a modulated signal from the Herschel Telescope, and a modulated signal arising from the emission of spectrometer instrument. The spectrum, V10-n-i(ν), for each detector i and SMEC scan n, can be expressed as: V10-n-i(ν) = V10-Source-n-i(ν) + V 10-Telescope-n-i (ν) + V10-Instrument-n-i(ν).
Low Resolution Correction Observations made in low resolution mode require an additional empirical correction to account for differences with the high resolution mode, on which all other calibration steps in the pipeline are based  (Swinyard et al. 2014 ). The low resolution correction consists of applying a detectordependent empirically derived  (Marchili et al. 2016 ) calibration curve to minimize the frequency-dependent artefacts in low resolution spectra. This correction applies only to point source calibrated data and only for the detectors in the SLW band.
ACKNOWLEDGMENTS SPIRE has been developed by a consortium of institutes led by  Cardiff University (UK)  and including  Univ. Lethbridge (Canada) ;  NAOC (China) ;  CEA ,  LAM (France) ;  IFSI ,  Univ. Padua (Italy) ;  IAC (Spain) ;  Stockholm Observatory (Sweden) ;  Imperial College London ,  RAL ,  UCL-MSSL ,  UKATC ,  Univ. Sussex (UK) ; and  Caltech ,  JPL ,  NHSC ,  Univ. Colorado (USA). This development has been supported by national funding agencies:  CSA (Canada) ;  NAOC (China) ;  CEA ,  CNES ,  CNRS (France) ;  ASI (Italy) ;  MCINN (Spain) ;  SNSB (Sweden) ;  STFC (UK) ; and  NASA (USA). The authors wish to acknowledge  Andres Rebolledo ,  Peter Kennedy ,  Zhaohan Weng ,  Yan He ,  Karim Ali ,  Yu Wai Wong ,  Alim Harji ,  Yufei Ren ,  David Sharpe , and  Jeremy Zaretski  for their contributions to the development of the SPIRE spectrometer data processing modules.
Moreover, statistical methods typically need a large number of examples to properly learn a mapping and generalize efficiently. While several resources have been successfully employed as training material for statistical NLG (see the related work section), they lack a direct link with world knowledge. Linked Open Data resources, in particular general knowledge bases such as DBpedia 1  , on the other hand, are not straightfoward to use as a basis for generation, while at the same time they are rich in extralinguistic information such as type hierarchy and semantic relations. Having the entities and concepts of an abstract meaning representation linked to a knowledge base allows a generator to use all the information coming from links to other resources in the LOD cloud. Such kind of input to a NLG pipeline is therefore richer than word-based structures, although its increased level of abstraction makes the generation process more complex.
Related Work Several resources exists have been used to train a statistical generator to learn lexicalizations for various types of representations The Surface Realization Shared Task  (Belz et al. , 2011) , for instance, provides a double dataset of shallow and deep input representations obtained by preprocessing the CoNNL 2008 Shared Task data  (Surdeanu et al. , 2008). Resources used for NLG include including the Penn Treebank  (Marcus et al. , 1993)  for Probabilistic Lexical Functional Grammar  (Cahill and Genabith, 2006)  and CCGBank  (Hockenmaier and Steedman, 2007)  for Combinatory Categorial Grammar syntax trees  (White et al. , 2007). More recently, the Groningen Meaning Bank  (Basile et al. , 2012)  has been proposed as a resource for NLG from abstract meaning representations, leveraging the fine-grained alignment between logical forms and their respective surface forms given by the Discourse Representation Graph formalism  (Basile and Bos, 2013).
The process of generating natural language from databases of structured information, including ones following Web standards, has been studied in the past, although often in specific application-oriented contexts. Bouayad-Agha et al. (2012)  propose an architecture as a basis for generation made of three RDF/OWL ontologies, separation the domain knowledge from the communication knowledge. Gyawali and Gardent (2014)  propose a statistical approach to NLG fro mknowledge bases based on tree adjoining grammars. WordNet is relatively less used for generation purposes. Examples of the use of Wordnet in the context of NLG include the methods to address specific NLG-related tasks proposed by  Jing (1998)  and the algorithm for lexical choice of  Basile (2014). 3 Aligning Text and Semantics  Basile and Bos (2013)  devise a strategy to align arbitrary natural languages expressions to formal representation of their meaning, encoded as Discourse Representation Structures (DRS,  Kamp and Reyle (1993) ).
DRSs are logical formulas comprising predicates and relations over discourse referents. For the English language, we are able to obtain DRSs for a given text using the C&C tools collection of linguistic analysis tools  (Curran et al. , 2007) , which includes Boxer  (Bos, 2008) , a rule-based system that builds DRSs on top of the CCG parse tree produced by the C&C parser. Boxer implements Neo-davidsonian representations of meaning, that is, formulas centered around events to which participant entities are connected by filling thematic roles. Figure  1  shows an example of DRS for the sentence "A robot is driving the car" as produced by Boxer. In this example the Neo-davidsonian semantics is evident: the ROBOT is the AGENT of the event DRIVE, while the CAR is the THEME. The alignment method proposed by  Basile and Bos (2013)  is based on a translation of format from DRS into a Discourse Representation Graph (DRG), where the semantic information is preserved but expressed in a flat, non recursive formalism.
Collecting Frame Lexicalizations We developed a pipeline of NLP tools to automatically extract instances of frames from the text. The pipeline comprises the C&C tools and Boxer, a module for word sense disambiguation and a module for entity linking. The two latter modules can be configured to use different external software to perform their task. The analysis of a text consists in the following steps: 1. Run the C&C tools and Boxer, saving both its XML and DRG output. The XML output of Boxer contains, for each predicates of the DRS that has been constructed, a link to the part of the surface form that introduced it. 2. Run the WSD and entity linking components, preserving the same tokenization. The software then uses the links to the text provided by Boxer to map the word senses and DBpedia entities to the DRS predicates.
3. The word senses corresponding to events are mapped to FrameNet frames, using the mapping provided by  Rouces et al. (2015). The VerbNet roles are converted into FrameNet roles using the mapping provided by  Loper et al. (2007). 4. The partial surface forms in the DRG output of Boxer are attached to the frames, semantic roles and frame elements. This pipeline is implemented in the KNEWS system, available for download at https://github. com/valeriobasile/ learningbyreading. In the following paragraphs we describe the internal details of the components of KNEWS.
Semantic parsing The semantic parsing module employs the C&C tools and Boxer to process the input text and output a complete formal representation of its meaning. The C&C pipeline of statistical NLP tools includes a tokenizer, a lemmatizer, named entity and part-of-speech tagger, and a parser that creates a Combinatorial Caregorial Grammar representation of the natural language syntax. Boxer builds a DRS on top of the CCG analysis. The predicates of a DRS are expressed over a set of discourse referents representing entities, concepts and events. Such structures contain, among other information, predicates representing the roles of the entities with respect to the detected events, e. g. , event(A), entity(B), agent(A,B) to represent B playing the role of the agent of the event A. Word sense disambiguation and Entity Linking KNEWS uses WordNet to represent concepts and events, DBpedia for named entities, and FrameNet's frames to represent events, integrating the mapping with the WordNet synsets provided by FrameBase.
The inventory of thematic roles used by Boxer is taken from VerbNet  (Schuler, 2005) , while KNEWS employs the mapping provided by SemLinks  (Palmer, 2009)  to link them (whenever possible) to FrameNet roles. KNEWS can be configured to use either UKB  (Agirre and Soroa, 2009)  or Babelfy  (Moro et al. , 2014)  to perform the word sense disambiguation, and DBpedia Spotlight  (Daiber et al. , 2013)  or Babelfy for entity linking. Output modes KNEWS's default output consists of frame instances, sets of RDF triples that contain a unique identifier, the type of the frame, the thematic roles involved in the instance, and the concepts or entities that fill the roles. The format follows the scheme of FrameBase, which offers the advantage of interoperability with other resources in the Linked Open Data cloud, as well as the possibility of using FrameBase's (de)reification rules to automatically generate a large number of binary predicates.
A frameelement represent a role in the frame instance. The concept tag contains a DBpedia or Wordnet resource (depending on the output of the disambiguation module), a lexicalization of the role filler (the conceptlexicalization tag), and the incomplete surface form obtained by composing the surface forms of the role filler and the frame. In the next section we describe an automatically built resource created by parsing text with this configuration of KNEWS. KNEWS has also an additional output mode: First-order Logic. With this output mode, KNEWS is able to generate first-order logic formulae representing the natural language text given as input. The symbols for the predicates are Wordnet symbols, allowing the output of KNEWS to be integrated with a reasoning engine, e. g. , to select background knowledge in a much more focused manner, as proposed in  Furbach and Schon (2016).
Evaluation In order to test our approach to knowledge extraction, we parsed a corpus of short texts, taken from the ESL Yes website of material for English learners. 2 We find this data particularly apt in the more general context of extracting general knowledge from text, being made of short, clear sentences about simple and generic topics. The corpus comprises 725 short stories, that we divided into 14,140 sentences. Parsing the ESL Yes corpus with KNEWS we collected 30,217 frame instances (420 unique frames), 1,455 concepts (1,201 Word-Net synsets and 254 DBpedia entities) filling in 41,945 roles (161 unique roles). 29,409 role instances could not be mapped to FrameNet, so they are expressed by one of 18 VerbNet roles. We evaluate the information extraction methodology by assessing the quality of this automatically produced resource.
g. "He picked up his clothes") or adverbs, which are analyzed by Boxer using the relation manner between the event and the adverb or proposition, thus like in the previous case no lexicalization is found for all the discourse referents. Finally, 3,779 instances failed the test due to a variety of reasons, e. g. , failure of the entity linking module or wrong syntactic analysis. Table  1  summarizes the findings exposed so far, also broken down by the number of frame elements in the frame instances. When increasing number of frame elements per frame instance, the issues with subordinate constructions dramatically decreases: they amount to 24% of the cases with one frame elements, 3% and 1% with two and three frame elements respectively. Conversely, wrong realizations due to representation misalignments tends to get worse, involving from 30% of the instances with one frame elements to 56% with two, to 39% with three.
Conclusion and Future Work In this paper we introduced a novel methodology to extract knowledge from text and encode it in formal structures compatible with the standards of the Web. Such structures are essentially instances of frames with their frame elements linked to concepts in Wordnet or DBpedia. This methodology is implemented in the freely available software package KNEWS. Next, we presented a collection of frame instances aligned with natural language, automatically created by parsing text for English learners. Finally, we propose a pilot study on how to use this resource to generate natural language from new frame instances. In terms of future direction for this work, the low hanging fruit is the enlargement of the resource, which will lead to a higher number of "good" instances to use for direct generation (as shown in Section 6) and more data to use for a statistical approach to generation.
Since the resource is produced automatically by parsing raw text with KNEWS, and natural language is abundant on the Web, this is a direction we intend to take in the foreseeable future. The approach to NLG based on the collection of lexicalized frame instances introduced in NLG is at the preliminary work stage, and many refinements can be made to the algorithm. Given a new frame instance to generate, its frame elements could be matched to the lexicalization in the resource with more sophisticated methods, e. g. , using distributional similarity. As a possible extension to the resource, information such as lemma and number could be included in the lexicalization of concepts. With such information in place, the NLG algorithm could be interfaced with the SimpleNLG surface realization library  (Gatt and Reiter, 2009)  to produce more fluent lexicalizations.
RBscore&NBench combines a web server, RBscore and a database, NBench. RBscore predicts RNA-/DNA-binding residues in proteins and visualizes the prediction scores and features on protein structures. The scoring scheme of RBscore directly links feature values to nucleic acid binding probabilities and illustrates the nucleic acid binding energy funnel on the protein surface. To avoid dataset, binding site definition and assessment metric biases, we compared RBscore with 18 web servers and 3 standalone programs on 41 datasets, which demonstrated the high and stable accuracy of RBscore. A comprehensive comparison led us to develop a benchmark database named NBench.
Besides, RBscore demonstrates a high-level accuracy with a high stability in the accuracy on all DBP and RBP datasets. It guarantees also a weighted arithmetic mean of Area Under ROC Curve (wAUC) above 0. 81, which cannot be achieved by other predictors. Since the running of the RBscore web server starting from June 2014, it has handled more than 5000 jobs submitted by more than 35 users from 11 different countries. With many strategies to predict RNA-or DNA-binding sites on protein reported every year, fair and rigorous benchmarking is a laborious necessity. However, many programs were only assessed by cross-validation in small-scale datasets and did not fully demonstrate their predictive abilities. Current assessments of binding site prediction programs differ in: (i) the definition of nucleic acid binding sites by distance cutoffs; (ii) the training and test datasets that can induce dataset bias and (iii) the criteria to measure prediction performance.
The comparisons in many of the reported works were only based on single cutoff, binary criteria and small-scale datasets validations, which may include bias toward certain methods and lead to dangerous conclusions. Together with RBscore, we assessed 18 web servers and 3 stand-alone programs, 25 different approaches in total, on 41 different datasets, including more than 5000 protein chains derived from 3D structures of protein-nucleic acid complexes. The results demonstrate: (i) dataset bias and distance cutoff bias in binding site definition exist in some methods; (ii) DBP and RBP appear to follow similar driving forces which have been captured by some of the methods; (iii) the predictors have been greatly improved over the years, but there is still room for sorting out the essential mechanisms of binding for sequence based approaches.
According to the results, RBscore also rank as a top-level predictor, which shows high but stable accuracies on all datasets regardless of distance cutoff used to define binding sites. This assessment work led us to build a database, NBench  (5) , to benchmark all the prediction programs and to provide all the prediction result data to the scientific community. Hopefully, later development of new nucleic acid binding site prediction programs can take the advantage of NBench database and make direct comparison with the existing 25 approaches to avoid unnecessary bias of dataset, binding site definition or assessment metrics.
The program DMS (  16 ) is used to generated the surface grid around the protein surface and to define the residue level neighboring interaction network. A total of 104 weighing factors were trained in the model. Details of the RBscore scoring function were described previously  (2). Besides the structure-based binding site predictor RBscore, we also provide a sequence-based predictor RBscore SVM, which exploits the facility of machine learning approach, when no structure information is provided. RBscore SVM first searches a small sequence database of Uniprot  (17)  for homologous sequences to derive Position Specific Scoring Matrix (PSSM) with PSI-BLAST  (18) , and then uses a slide-window approach to generate input feature vector for support vector machine  (19)  (SVM) to build the prediction model.
Input The web server integrates structure-based predictor RBscore and sequence-based predictor RBscore SVM. When the protein structure is available, the prediction accuracy is generally better than only sequence information as well as more information can be visualized. The input of protein structure can either be a four-letter PDB code or by uploading a PDB formatted file. When only protein sequence is available, users need to input FASTA formatted sequences or upload a FASTA file of sequences. More than one sequence in the same FASTA file is possible and the prediction results will be in the same order. There are optional parameter settings for the RBscore SVM model: (i) to set the specificity or sensitivity of the prediction to determine the cutoff value used in binary prediction of binding sites.
By default, the specificity is set to 85%; (ii) SVM models used in prediction. The models are derived from RBP or DBP alone or both RBP and DBP. According to the results in NBench, the model trained on both RBP and DBP dataset achieves highest accuracy. Finally, users can input an email address to receive the results after the job finish. Otherwise, the web page is automatically refreshed to show the running log until the prediction results are generated.
NBench DATABASE It is a non-trivial task to compare a new binding site predictor with existing ones to demonstrate its effectiveness. Such a comparison is prone to dataset bias, binding site definition bias and assessment metric bias, as well as the detailed treatments of the datasets. For example, PRBR  (23)  does not predict binding sites of the N-terminal and C-terminal residues, and may lead to an unfair comparison with other programs who predict on the whole sequence. To minimize the possibility of biased conclusions, NBench contains prediction results of 25 different approaches on 41 datasets to directly benchmark all the programs at the same level.