dataesr/scientific-paragraphs-categorization huggingface dataset:
Each pixel of this b/w image corresponds to a point in the medium: if a photon is scattered at this point, the brightness at this point will be increased by one. As more photons are scattered at this point, the brighter the pixel will be. Resolution was 5 m per pixel as it was sufficient to be compared with the experimental data, also the resolution could be changed from 2 m/p to 20 m/p. It should also be taken into account that the image of 32 bit color depth allows small brightness changes in a light field to be detected, so the derived distributions are saved with *. tif extension. Another self-developed program (TiffReader) allows processing files with *. tif extension (if the color depth is more than 32 bytes, then the image is processed without image quality loss). This program has the ability to measure the distance between two chosen points, thereby obtaining brightness profiles as well as 3D brightness distribution.
Furthermore, the photon path distribution is parallel to the wall and symmetrical to the plane which goes through the middle of the fibres. Secondly, to obtain the fluorescent light field distribution, optical filters were used (for 532 nm light excitation: Orange Glass-14 ( 50 ≈ 580 nm,  100 ≈ 620 nm), for 632. 8 nm: Red Glass-17 ( 50 ≈ 670 nm,  100 ≈ 700 nm)). These filters were placed between VideoScan camera and the transparent wall of the cuvette. In order to reduce the signal-to-noise ratio, 10 images of each field distribution were averaged. During the experiment, the distance between the sources was varied in the range 0. 5-7. 5 mm (Figure  3 ). Thirdly, the 3D light distributions were obtained by means of MClight program for the two excitation wavelengths (532 nm and 632. 8 nm). The geometry of the simulations replicates that of the real experiment and the optical properties of the simulated turbid medium were chosen according to the optical properties of Intralipid at the already mentioned wavelengths.
Determination of Anisotropy Factor. The anisotropy factor for  = 632. 8 nm was found to be 0. 83 ± 0. 03, the free path length 27±2 m, and for  = 532 nm 0. 85±0. 03 and 20±3 m, respectively, (Figure  4 ). These results are acceptable and can be used to model brain tissue and as input parameters for MC simulations. Firstly, the light field distribution was obtained by MClight and processed in TiffReader program (Figure  5 ). In this distribution, each color corresponds to its own brightness range. One can see that the  maximum brightness is 300 m deeper than the ends of the fibre tips because in these areas brightness is the sum of the diffusely scattered light and the light emitted from fibres, without interaction with medium.
Swedish Constructicon The Swedish Constructicon (SweCcn) 4  is an electronic database of Swedish constructions  (Lyngfelt et al. , 2012; Sköldberg et al. , 2013). Just as it is precursor the Berkeley Constructicon,  5  it builds on experiences from Construction Grammar and is historically, methodologically and theoretically closely related to Frame Semantics and FrameNet  (Fillmore et al. , 2012). While framenets map single lexical Figure  1 : A search for the noun flaska 'bottle' in SALDO shows that it only has one sense. We are also shown the lemma, the part of speech, the primary descriptor förvara 'store. v', the secondary descriptor hälla 'pour. v', and finally primary and secondary children, that is entries which have flaska as primary or secondary descriptor. units to the frames they evoke, a constructicon deals with the pairing of form and meaning in more complex linguistic units, typically (partially) schematic multiword units that cannot easily be referred to by either grammatical or lexicographic descriptions alone.
Swedish Constructicon Constructions are more complex linguistic units than words, they are common in use and difficult to ignore when working with authentic text. One way to enrich SweFN with more representative examples of how to express meaning in language is to include constructions as frame-evoking units in the database. Currently work is being done on systematically linking constructions in SweCcn with frames in SweFN  (Ehrlemark, 2014) , but the task is not as straight-forward as identifying which frame is evoked by a certain LU. First, not all constructions evoke frames, carrying little meaning from a semantic point of view. This includes such general patterns as constructions for modification, predication, passive voice or filler-gap constructions. Second, constructions that potentially correspond with frames do not always fit the distribution pattern of frame elements described in the target frame.
Consistency checks and automatic extension of the data There is no gold standard to evaluate the quality of SweFN against as there is no other comparable resource. FrameNet-like resources for other languages are constructed with different foci and under different conditions. However, there is a constant assessment of the correctness of the resources built into the workflow and ongoing consistency checks to avoid inconsistency between resources. The Karp tool gives error messages, for example when SALDO entries are listed in more than one frame. Other types of checks are run with certain intervals, for example to see if there are annotation tags which do not follow the standard format. Confronted with different types of error messages the developers go back to the frames in question to revise the contents of the frame, such as which LUs are said to evoke the frame, or the choice of and annotation of example sentences.
Conclusions The building of one big macro-resource for Swedish language technology, where the individual resources interact with and enhance each other, provides a unique overview of the Swedish language. One search on a lexical expression results in a list of descriptions from all of the separate resources. The information derived is not only useful for the end user, but also for the continuing work on all parts of the linguistic macro-structure. We have here focused on how two language technology resources, SALDO and SweCcn, are exploited in the development of SweFN, but also on how these resources enhance each other and other resources. We mainly address the manual perspectives of the workflow, illustrating what data may derive from the different resources, how this data may be used to facilitate work, and how the contents of one resource may reappear in the contents of another.
Introduction In recent years a lot of initiatives have emerged towards the RDF based representation of language data and the hereby opened possibility to publish those data in the Linked Open Data (LOD) cloud 1. This development has been leading to the establishment of a specialized Linked Data (LD) cloud for language data. The actual diagram of this rapidly growing Linguistic Linked Open Data (LLOD) framework 2 reflects the distinct types of language data that already exist in LOD compliant formats, supporting their publication in the cloud and enabling their cross-linking and their linking to other knowledge objects available in the LOD context. And to further stress the importance of this development, the main conference in the field of language resources, LREC, has declared the LOD as one of the hot topics of its 2014 edition 3 and we can observe from the list of accepted papers and workshops/tutorials that indeed this is really a hot topic for the description of language resources.
, 2013)  onto LOD compliant representation formalisms. A benefit of such approaches is the fact that lexical data can be linked to meanings encoded in knowledge sources that are accessible via a URI, such as senses encoded in the DBpedia instantiation of Wiktioanry, and from there one can navigate to multilingual lexical equivalents, if those are available. As a concrete example, working on historical German text, we could link the old word form "Fegfeur" (purgatory) via its modern German lemma "Fegefeuer" not only to a lexical sense in the DBpedia instantiation of Wiktionary: http://wiktionary. dbpedia. org/page/Fegefeuer-German-Noun-1de, also with access to 7 translations of this sense, but also leading to the DBpedia page for "purgatory", one get additional semantic information, so for example that the word is related to the categories "Christian_eschatology", "Christianity_and_death" etc.
Pre-Processing of the lexical Data: Harmonization As the reader can imagine, all those resources were available in distinct formats and containing distinct types of features. Therefore, we first had first to define a pre-processing of the different lexical data for the purpose of their harmonisation. This point leads us to a general remark: It is by far not enough to transform the representation of the lexical data onto RDF and related languages for ensuring their semantic interoperability in the LOD cloud, but preliminary work has to be performed. Just to give an example of the outcome of this work, we present a harmonized entry in Figure  1  below: "fehler" => { # lemma "prov::GermanPC. lex" => { # provenance info "pos::N" => { # PoS info "pol_rank" => "0. 783019", # ranking in the orginal source "pol_val" => "NEG", # polarity feature in the orig souce }, }, "prov::GermanSentiSpin.
…… As the reader can see, such representation can link the lexical information to a wide range of related information, and what in the context of former infrastructures for language resources was represented by a set of external metadata can be incorporated here directly in the choice of classes and properties. In fact, we do not need to encode the information that the entry has PoS Noun, since this information is encoded in the details of the reference in Wiktionary/DBpedia we are pointing to.
In many cases the data sets in the Linguistic Linked Open Data are redundant, repeating for example many times the lemmas of lexical entries in the different types of data set. We think that similar to the ISOcat data category we could aim at having a "centralized" repository for lemmas of one language, so that this lemma is not repeated for example in Wiktionary, Lexvo 24 and many other data sets in the LLOD. We are wondering if, in 23 See for example http://www. isocat. org/rest/dc/1333 for our selected ISOcat entry for the pos "noun". 24 See http://www. lexvo. org/ Figure  2 : The RDF, SKOS-XL and lemon representation of the entry, with a link to an ontological framework representing polarity information. The various polarities given by the various sources are represented as "OpinionObjects".
the precise context of the LODlinking lexical data to other data sets in the cloudit would not be possible to have exactly one lexical data set for reach language. Figure  3  below sketches our intended model, taking as example terminology in the field of financial reporting. Figure  3 : An example instantiation of the model we are aiming at: a unique (lemma) lexicon for one language (bottom right). Getting the full forms from a repository of such forms, including feature structures describing their morpho-syntactic information. Those are linking to occurrences of terms or labels that are used in knowledge objects (domain ontologies, taxonomies etc. ). This model allows to precisely linking information from the lexicon, the morpho-syntactic descriptions and potential grammatical patterns as those are used in labels, comments or definitions in the context of knowledge objects in the LOD data sets.
Conclusions In this short position paper, we presented some experiences done in the context of the emerging Linguistic Linked Open Data framework. This lead us to make some comments on the way we could go for a much more "compressed" distribution of semantically (using LOD compliant representation languages) encoded language data, which could be more easily re-used in the context of knowledge-based NLP applications. The result would be a set of language specific "centralised" repositories of lemmas and related full forms, all equipped with URIs, that are used in the context of knowledge objects present in the Linked Data framework.
, 2004; Tijerino et al. , 2005; Sanchez, 2010; Hao, 2010; Wang et al. , 2011). We intend to develop a linguistic knowledge base, i. e. a lexical database, in which the ontology schema will be integrated to process language on the basis of syntactic relations, i. e. formal grammars. This work is licensed under a Creative Commons Attribution 4. 0 International Licence. Page numbers and proceedings footer are added by the organisers. License details: http://creativecommons. org/licenses/by/4. 0/.
e. kylix a labbro risparmiato (spared-lip kylix), which stands for lip cup or cratere (crater)which stands for vaso (vase). 1Dictionnaires Électroniques du LADL (Laboratoire d'Automatique Documentaire et Linguistique). 2DELA electronic dictionaries are of two types: of simple words and of Multi-Word Expressions (MWE). 3http://www. iccd. beniculturali. it/index. php?it/240/vocabolari. 4In 4 we give an excerpt of the Italian Archaeological Electronic Dictionary. Besides, our additional resource has been created extracting terms from existing literature. Also, from ICCD unstructured data (i. e. the vocabulary of Coroplastics) Proper and Place Names have been retrieved, which are now entries of our dictionary.
Linguistic Linked Open Data (LLOD) Integration The LLOD is a project developed by the Open Linguistics Working Group (OLWG). It aims to create a representation formalism for corpora in Resource Description Framework/Web Ontology Language (RDF/OWL). The initiative intends to link LRs, represented in RDF, with the resources available in the Linked Open Data (LOD) 8 cloud. The LLOD goal is not only to provide LRs in an interoperable way, but also to use an open license and link LRs with other resources in order to combine information from different knowledge sources. According to the LOD paradigm  (Berners-Lee, 2006) , Web resources have to present a Uniform Resource Identifier (URI) for entities to which they refer to, and to include links to other resources. According to  Chiarcos et al. (2013a) , "linking to central terminology repositories facilitates conceptual interoperability".
Benefits of LLOD are also identified in linking through URIs, federation, dynamic linking between resources  (Chiarcos et al. , 2013b). Besides, data structured in RDF format can be queried by means of the SPARQL language. Indeed, if RDF triples represent a set of relationship among resources, than SPARQL queries are the patterns for these relationships. One of the most relevant LLOD resources are stored in and presented by DBpedia (www. dbpedia. org). DBPedia is a sample of large Linked Datasets, which offers Wikipedia information in RDF format and incorporate other Web datasets. Therefore, we have referred and will refer to DBPedia Italian  In order to reuse such prescriptions, we adopt a Finite State Transducer-based system which merge specific matching URIs with electronic dictionary entries.
Clematide and Klenner Clematide and Klenner (2010) manually curated a lexicon 1  of around 8000 words, based on the synsets in GermaNet, a WordNet-like database  (Hamp and Feldweg, 1997). A semi-automatic approach was used to extend the lexicon, first generating candidate polar words by searching in a corpus for coordination with known polar words, and then presenting these words to human annotators. We will refer to this resource as the C&K lexicon.
Unspecified scores Some lexical items in the PolarityClues dataset were not assigned a numerical score, only a polarity direction. In these cases, the task is not to normalise the score, but to assign one. To do this, we can first normalise the scores of all other words, as described above. Then, we can consider the words without scores, and calculate the root mean square polarity of these words in the other sources, and assign them this value, either positive or negative.
In another work  (Bilgin et al. , 2004) , authors propose a methodology, on exploiting morphosemantic information in languages where the morphemes are more regular. They perform a case study on Turkish, and propose application areas both monolingually and multilingually, such as globally enriching WordNets and auto detecting errors in WordNets. In a similiar work  (Mititelu, 2012) , morphosemantic information is added to Romanian WordNet and the proposed application areas in  Bilgin et al. (2004)  are examined and shown to be feasible. Previous studies based on building Basque PropBank focus on the building process of Basque Prop-Bank, rather than analysis of the regularity of case markers and the relation between semantic roles and case markers. Furthermore, the study related to building Arabic Morphological Pattern Net, aims to build a seperate dataset and map it to other resources such as Arabic VerbNet, WordNet and PropBank.
This can Turkish is not among rich languages by means of computational resources as discussed before. Turkish Language Association (TDK) is a trustworthy source for lexical datasets and dictionaries. To run this pilot study, we have used the list of Turkish root verbs provided by TDK and the TNC corpus 4. The interface built for searching the TNC corpus gives the possibility to see all sentences that were built with the verb the user is searching for (Aksan and Aksan, 2012). The senses of the verbs and case marking of their arguments are decided by manually investigating the sentences appear in search results of the TNC corpus. Then, the arguments of the predicates are labeled with VerbNet thematic roles and PropBank argument numbers, by checking the English equivalent of Turkish verb sense.
The immediate extension of this work will cover wikification, i. e. the linking of all nodes in the AMR representation of our dataset to some ontology: events are already covered, internally defined relations are already annotated, too (such as named entity types, dates, quantities, etc. ), but external links remain to be added. We will not only use Wikipedia (as the term "wikification" might suggest), but we will extend this idea also to other sources, such as DBpedia or BabelNet, keeping all links in parallel if possible. This should allow for deep comparison of the two languages also content-wise. We should then be able to better answer the question of annotation unification which does depend on these links rather than on the annotation guidelines themselves. Parallel AMR-annotated data will be used at the JHU 2014 Summer Workshop, where technology for AMR-based parsing, generation and possibly also MT will be developed, allowing also technological insight into the AMR scheme across languages.
This type of large coverage resource is not available for almost all languages, with the exception of the CELEX database  (Baayen et al. , 1995)  for English, German and Dutch, a paid resource distributed by the LDC. The paper is organized as follows. Section 2 reports related work on semantic and morphological acquisition from MRDs. In Section 3, we describe how we converted Wiktionnaire, the French language edition of Wiktionary, into a structured XML-tagged MRD which contains, among other things, definitions and morphological relations. In Section 4, we explain how we used Wiktionnaire's morphological sections to create a lexicon of morphologically related words. The notion of morphological definitions and their automatic identification are introduced in Section 5. In Section 6, we show how these definitions enable us to acquire new derived words and enrich the initial lexicon.
Semantic typing of the morphological definitions The next experiment aims to demonstrate that morphological definitions could easily and quite accurately be typed semantically. We focus on a particular semantic type, namely definitions of process nominals such as (6) because they can be evaluated with respect to the Verbaction database  (Hathout and Tanguy, 2002). Deverbal nominals have been extensively studied in linguistics  (Pustejovsky, 1995)  and used in a number of tools for various tasks. One of their distinctive feature is that they almost have the same meaning as their base verb. For instance, in (7) the noun and verb phrases are paraphrases of one another. Verbaction contains 9,393 verb-noun pairs where the noun is morphologically related to the verb and can be used to express the act denoted by the verb (e.
2. We extract all the n-grams of consecutive nodes in these paths. 3. Each n-gram yields 3 features: the sequence of the node's lemmata, the sequence of the nodes POS, and the sequence of syntactic dependency relations. We obtained an accuracy of 97% for the semantic typing of the 200 definientia of the test set. The most immediate application of the classifier is the enrichment of Verbaction. Running the classifier on all the definitions with a nominal definiendum and a verbal morphosemantic head will provide us with new couples that could be added to the database. The classifier could also help us type process nouns that are not morphologically derived such as audition 'hearing' which is defined with respect to the verb entendre 'hear'. Similar typing could be performed for other semantic types such as agent nouns (in -eur or -ant), change of state verbs (in -iser or -ifier) or adjectives expressing possibility (in -able), etc.
Automatic Rule-based TDA-MSA Machine Translation We have developed a rule-based translation system that is able to translate any social media text in TDA into MSA. This rule-based translation system can be coupled with any statistical machine translation system from MSA to another language to provide a translation of original Tunisian dialectal sentences of social media from TDA to that other language. Figure  5  shows the different steps used in the translation of any social media text from TDA into MSA. First, for each word in the TDA social media text, we proceed by searching in the TDA-MSA lexicon database for the corresponding translation of the TDA word. Mostly, TDA nouns and imported words from other languages than Arabic were included in the lexicon. Second, we proceed by searching in the database of mapping rules for the source verb in TDA and its corresponding MSA translation, as shown in Figure  4.
Introduction This paper describes the collection of a database of paradigmatically related word pairs in German which was compiled via human judgement experiments hosted on Amazon Mechanical Turk. While paradigmatic relations (such as synonymy, antonymy, hypernymy, and hyponymy) have been extensively researched in theoretical linguistics and psycholinguistics, they are still notoriously difficult to identify and distinguish computationally, because their distributions in text tend to be very similar. For example, in The boy/girl/person loves/hates the cat, the nominal co-hyponyms boy, girl and their hypernym person as well as the verbal antonyms love and hate occur in identical contexts, respectively. A dataset of paradigmatic relation pairs would thus represent a valuable test-bed for research on semantic relatedness.
For the compilation of the relation dataset we aimed for a sufficiently large amount of human-labelled data, which may both serve as seeds for a computational approach, and provide a gold-standard for evaluating the resulting computational models. This paper describes our efforts to create such a paradigmatic relation dataset in a two-step process, making use of two types of human-generated data: (1) human suggestions of semantically related word pairs, and (2) human ratings of semantic relations between word pairs. Furthermore, we are the first to explicitly work across word classes (covering adjective, noun and verb targets), and to incorporate semantic classes, corpus frequency and polysemy as balancing criteria into target selection. The resulting dataset 1 consists of three parts: 1. A representative selection of target lexical units drawn from GermaNet, a broad-coverage lexicalsemantic net for German, using a principled sampling technique and taking into account the three major word classes adjectives, nouns, and verbs, which are balanced according to semantic category, polysemy, and type frequency.