dataesr/scientific-paragraphs-categorization huggingface dataset:
Stem Cells International The second analysis was performed on samples of dGSCs ( = 6) and compared to ASCs ( = 6) after osteogenic differentiation. Expression level of RUNX2 was assessed on different time points: day 0 (D0), day 7 (D7) and day 14 (D14) after osteogenic differentiation, confirmed by the microscopic observation of mineral nodules formation and Alizarin Red S staining (Supplementary Material 3). The normalization was performed against the two most stable reference genes and compared to the least stable one as shown in Figure  8. For dGSCs, the normalization to SDHA and TBP showed a significant increase of RUNX2 expression at D7 and D14 when compared to the relative gene expression (Figure  8(a) ). When data were normalized with RPS18, the least stable reference gene for dGSCs, there was a decrease in the expression of RUNX2, with a statistically significant difference when compared to SDHA and TBP.
Consequently, in our study, despite a slight dissimilarity in the ranking of HKGs among the four algorithms, results showed a highly homogenous reproducibility; indeed, a similar tendency was found for the three most stable genes and an overall comparable order of genes. ΔCt comparative method and GeNorm were used for intergene study; ΔCt comparative method identifies the most reliable reference gene as the one that varies the less when compared to all the other genes among all the sample. This method needs no high-level mathematical methodology and is ideal for the nonspecialist to determine the best reference genes  [13]. GeNorm software not only defines the optimal combination of genes or the ideal "pair of genes" with low variability, but also proposes the minimal number of stable HKGs.
Let X = {X i } and Y = {Y i } be two results computed with the CESTAC method, (i) X = Y if and only if X -Y = @. 0, (ii) X > Y if and only if X > Y and X -Y = @. 0, (iii) X ≥ Y if and only if X ≥ Y or X -Y = @. 0. Discrete Stochastic Arithmetic (DSA) is the combination of the CESTAC method, the concept of computational zero, and the discrete stochastic relationships  [2]. The CADNA 4  software  [1]  is a library which implements DSA with N = 3 and β = 0. 05. In contrast to interval arithmetic, that computes guaranteed results, the CADNA software provides, with the probability 95% the number of exact significant digits of any computed result. CADNA allows to use new numerical types: the stochastic types. In practice, classic floating-point variables are replaced by the corresponding stochastic variables, which are composed of three perturbed floatingpoint values.
When a stochastic variable is printed, only its exact significant digits appear. Because the library contains the definition of all arithmetic operations and order relations for the stochastic types, the use of CADNA in a program requires only a few modifications: essentially changes in the declarations of variables and in input/output statements. During the execution, CADNA can detect numerical instabilities, which are usually due to the presence of numerical noise. When a numerical instability is detected, dedicated CADNA counters are incremented. At the end of the run, the value of these counters together with appropriate warning messages are printed on standard output.
Dedicated system control software The dedicated Thermoguide® software (Image Guided Therapy, Pessac, France; see Fig. 1d ) was installed on the ultrasound console. It allowed driving remotely the transducer and the motors by setting up trajectories and then sending them for execution to the FUS electronics. A trajectory is composed of segments defining straight displacements. Ultrasound can be shot along these segments. On the mechanical trajectory panel (Fig. 1d ), one could manually draw any arbitrary trajectory or define it more accurately by entering the coordinates of the segments. The motor speed can be tuned along each segment. The electronic trajectory panel (Fig. 1d ) allowed controlling every ultrasound parameter (frequency, shot duration, pulse repetition frequency, amplitude) and the steering in depth if possible.
Real-time data transfer and monitoring A pipeline filter implemented on the MRI console running Paravision® software (Bruker, Germany) allowed real-time reconstruction and visualization of acquired images directly on the ultrasound console (Fig. 1e ). During image acquisition, the filter intercepted the raw K-space data and sent them along with a header summarizing all acquisition parameters to the ultrasound console through a TCP/IP protocol. This real-time pipeline was successfully tested for all standard Bruker MRI sequences. Specific data processing could be applied in real time to the MR images, such as a temperature estimation or thermal dose measurement for thermometry experiments. In addition, a specific plug-in for the Thermoguide® software was developed to select the target location for BBB permeabilization on MR anatomical images.
It has to be noted that the whole system was designed to be highly modular, both on the hardware side (several compact MR-compatible transducers available, several beds) and on the software side (real-time MR data export and display, dedicated plug-ins for automatic focal spot repositioning). The system could be further improved by integrating more sensitive and more homogeneous RF coils, by further miniaturizing the transducers or by reducing small mechanical vibrations during scans. It also needs to be noted that the water flow in the balloon was stopped and the transducer/motors were disconnected as often as possible during imaging to avoid artifacts. The coil tuning and matching as well as the shimming in the brain did not change much with the transducer position though. In the first in vivo experiment, we demonstrate our capability to transiently permeate the BBB at any arbitrary brain location previously chosen on MRI anatomical images, with high reproducibility.
As the location of the disruption site can be modified thanks to the motors without taking the animal out of the magnet, the system is time-saving compared to iterative manual positioning. Moreover, the dedicated Thermoguide® software, which controls the ultrasound shots, allows planning 3D trajectories of BBB disruption with both the mechanical displacement and the electronic beam steering. Such a system should be of great interest to study the dependency of BBB disruption with the targeted brain region, for example, the difference between white and gray matters or the influence of vasculature. Our results demonstrate that this system can achieve delivery of large amounts of MRI contrast agents along virtually any arbitrarily chosen brain areas. In the second in vivo proof of concept, this system was used for hemispheric BBB disruption, while the other hemisphere was kept as a control, or for BBB disruption over the whole brain.
In order to give a better accessibility to the data, the corpus is made available to the public through The Language Archive (TLA), a collection of language resources hosted by the Max Planck Institute of Nijmegen (Nederlands). 1 All projects hosted by TLA must adopt a common metadata scheme on which all the structure of the database is built. The standard adopted by TLA used to be IMDI. 2   1 Homepage: https://tla. mpi. nl/ Corpora: https://corpus1. mpi. nl/ds/asv/?1 2 TLA has recently made available to the users also the new, CLARIN supported CMDI metadata format. See below The projects included in the BBC were obviously already supplied with rich information which had to fit into the metadata structure available.
3% in negation recognition and 94. 9% in speculation recognition. In the scope detection task, a token was correctly classified if it had been properly identified as being inside or outside the scope of all the negation signals present in the sentence. They achieved an F1 score of 93. 2% in negation and 80. 9% in speculation scope detection. Sohn et al. (2012)  developed hand crafted rules representing subtrees of dependency parsers of negated sentences and showed that they were effective on a dataset from their institution. Zou et al. (2015)  developed a system for detecting negation in clinical narratives, based on dependency parse trees. The process involves a first step of negative cue identification that exploits a binary classifier. The second step instead analyses the parse tree of each sentence and tries to identify possible candidates for a negative scope extracted with a heuristics: starting from a cue, all ancestors of the cue are considered, from which both the full subtree rooted in the ancestor and the list of its children are considered as candidates.
Precision/Recall are more tolerant measures since they count each correct token individually. The results achieved on our test set from the BioScope corpus are reported in Table 2. Precision Recall F1 PCS 78. 57% 79. 16% 78. 87% 54. 23% Table  2. Negation/Speculation Scope detection results We evaluated the performance of our algorithm also on the dataset from the CoNLL 2010 task 2 and we report the results in Table  3 , compared with the best results achieved at the challenge  (Morante et al. 2010). Precision Recall F1  Morante et al. 59. 62%  55. 18% 57. 32% Our system 61. 35% 63. 68% 62. 49% Table  3. Speculation scope detection We can note a significant improvement in Recall, that leads also to an relevant improvement in F1.
Corpus extension The ISDT corpus released for Evalita 2014 consists of 97,500 tokens derived from the TUT and 81,000 tokens derived from the ISST-TANL. Moreover a gold test dataset of 9,442 tokens was produced for the shared task. UD-it is a larger resource including the previous texts (with converted annotations), a new corpus of questions, and data obtained from ParTUT 2 (the Multilingual Turin University Treebank) for a total of 324,406 tokens (13,079 sentences). For release 1. 1, UD-it was randomly split into train, development and test data sets. Both development and test include 500 sentences each (~13,300 tokens).
Evalita dataset in UD 1. 1 Our first experiment is performed on the same dataset from Evalita 2014, present also in the official UD-it 1. 1 resource. We report in Table  5  the performance of the same parsers. Using the resource converted in UD, the LAS of all the three parsers improved, as shown in the Diff column. This was somehow not expected since the tree structure is characterized by longer distance dependencies.
Entity Linking for Italian Tweets Pierpaolo Basile, Annalina Caputo, Giovanni Semeraro Dept. of Computer Science -University of Bari Aldo Moro Via, E. Orabona, 4 -70125 Bari (Italy) {firstname. lastname}@uniba. it Abstract English. Linking entity mentions in Italian tweets to concepts in a knowledge base is a challenging task, due to the short and noisy nature of these short messages and the lack of specific resources for Italian. This paper proposes an adaptation of a general purpose Named Entity Linking algorithm, which exploits the similarity measure computed over a Distributional Semantic Model, in the context of Italian tweets. In order to evaluate the proposed algorithm, we introduce a new dataset of tweets for entity linking that we developed specifically for the Italian language. Italiano.
La creazione di collegamenti tra le menzioni di un'entità in tweet in italiano ed il corrispettivo concetto in una base di conoscenza è un compito problematico a causa del testo nei tweet, generalmente corto e rumoroso, e della mancanza di risorse specifiche per l'italiano. In questo studio proponiamo l'adattamento di un algoritmo generico di Named Entity Linking, che sfrutta la misura di similarità semantica calcolata su uno spazio distribuzionale, nel contesto dei tweet in Italiano. Al fine di valutare l'algoritmo proposto, inoltre, introduciamo un nuovo dataset di tweet per il task di entity linking specifico per la lingua italiana.
These difficulties notwithstanding, with an average of 500 billion posts being generated every day 1  , tweets represent a rich source of information. Twitterbased tasks like user interest discovery, tweet recommendation, social/economical analysis, and so forth, could benefit from such a kind of semantic features represented by named entities linked to a knowledge base. Such tasks become even more problematic when the tweet analysis involves languages different from English. Specifically, in the context of Italian language, the lack of languagespecific resources and annotated tweet datasets complicates the assessment of NEL algorithms for tweets. Our main contributions to this problem are: • An adaptation of a Twitter-based NEL algorithm based on a Distributional Semantic Model (DSM-TEL), which needs no specific Italian resources since it is completely unsupervised (Section 3).
Dataset One of the main limitations to the development of specific algorithms for tweet-based entity linking in Italian lies on the dearth of datasets for training and assessing the quality of such techniques. Hence, we built a new dataset by following the guidelines proposed in the #Micro-posts2015 Named Entity Linking (NEEL) challenge 2 (Rizzo et al. , 2015). We randomly selected 1,000 tweets from the TWITA dataset  (Basile and Nissim, 2013) , the first corpus of Italian tweets. For each tweet, we first select the named entities (NE). A NE is a string in the tweet representing a proper noun, excluding the preceding article (like "il", "lo", "la", etc. ) and any other prefix (e. g. "Dott. ", "Prof. ") or post-posed modifier. More specifically, an entity is any proper noun that: 1) belongs to one of the categories specified in a taxonomy and/or 2) can be linked to a DBpedia concept.
This means that some concepts have a NIL DBpedia reference; these concepts belong to one of the categories but they have no corresponding concept in DBpedia. The taxonomy is defined by the following categories: Thing 3  , Event, Character, Location, Organization, Person and Product. We annotated concepts by using the canonicalized dataset of Italian DBpedia 2014  4. For specific Italian concepts that are not linked to an English article, we adopt the localized version of DBpedia. Finally, some concepts have an Italian Wikipedia article but they are not in DBpedia; in that case we linked the entity by using the Wikipedia URL. Entities represented neither in DBpedia nor Wikipedia are linked to NIL. The annotation process poses some challenges specific to the Twitter context. For example, entities can be part of a user mention or tag; all these strings are valid entities: #[Alemanno], and @[CarlottaFerlito].
The '#' and '@' characters are not considered as part of the annotation. This first version of the dataset was annotated by only one annotator, and comprises 756 entity mentions, with a mean of about 0. 75 entities for each tweet. The distribution of entities in categories is as follows: 301  Persons, 197 Organizations, 124 Locations, 96 Products, 18 Things, 11  Events and 9 Characters. 63% of tweets links to a DBpedia concept, about 30% of them is annotated as NIL, 6% links to an URL of a Wikipedia page, while only one entity links to an Italian DBpedia concept. The dataset 5  is composed of two files: the data and the annotation file. The data file contains pairs of tweet id and text, each listed on a different line. The annotation file consists of a line for each tweet id, which is followed by the start and the end offset 6  of the annotation, the linked concept and the category.
Evaluation The evaluation aims to compare several entity linking tools for Italian language exploiting the proposed dataset. We include in the evaluation our method that is an adaptation of the system that participated in the NEEL challenge for English tweets  (Basile et al. , 2015b). We select three tools able to perform entity linking for Italian: TAGME, Babelfy, and TextRazor. TAGME (Ferragina and Scaiella, 2010) has a particular option that enables a special parser for Twitter messages. This parser has been designed to better handle entities in tweets like URL, user mentions and hash-tag. However, some other tools are not developed specifically for Twitter. For example, Babelfy  (Moro et al. , 2014)  is an algorithm for entity linking and disambiguation developed for generic texts that uses BabelNet  (Navigli and Ponzetto, 2012)  as knowledge source.
The third system is TextRazor 12 , a commercial system able to recognize, disambiguate and link entities in ten languages, including Italian. Systems are compared using the typical metrics of precision, recall and F-measure. We compute the metrics in two settings: the exact match set requires that both start and end offsets match the gold standard annotation, while in non exact match the offsets provided by the systems can differ of two positions with respect to the gold standard. Each algorithm provides a different output that needs some post-processing operations in order to make it comparable with our annotation standard. Most of the annotations are made with respect to the canonicalized version of DBpedia, while only 6% of the dataset is annotated using Wikipedia page URLs or the localized version (just one).
310. 410 DSMTEL. 470. 505. 487. 495. 532. 513  Table  1  reports the results about the entity recognition task with respect to exact and non exact match respectively. DSM-TEL provides the best results followed by TextRazor (exact match) and TAGME (non exact match), while the low performance of Babelfy proves that it is not able to tackle the irregular language used in tweets. In all the cases TextRazor achieves the best precision. Entity linking performance are reported in Tables 2. It is important to underline that a correct linking requires the proper recognition of the entity involved. TextRazor achieves the best performance in the entity linking task with an F-measure in both exact and non exact match of 0. 280. Moreover, in order to understand if the recog-nition and linking tasks pose more challenges for Italian language, we evaluated all the systems on an English dataset.
Although the two datasets are not directly comparable (due to the different sizes and the number of entities involved per tweet), we run an experiment over the Making Sense of Microposts (#Microposts2015) Named Entity rEcognition and Linking (NEEL) Challenge dataset  (Rizzo et al. , 2015)  (Table  2 ). The evaluation results show a different behaviour of the systems for the English language. F-measure values are slightly lower than for Italian and TextRazor almost always outperforms other systems, with the only exception of TAGME for the linking with non exact match.
Conclusion We tackled the problem of entity linking for Italian tweets. Our contribution is threefold: 1) we build a first Italian tweet dataset for entity linking, 2) we adapted a distributional-based NEL algorithm to the Italian language, and 3) we compared stateof-the-art systems on the built dataset. As for English, the entity linking task for Italian tweets turn out to be quite difficult, as pointed out by the very low performance of all systems employed. As future work we plan to extend the dataset in order to provide more examples for training and testing data.
Training data are generally used in the first stage, usually approached by supervised systems, while the linking stage is generally performed using unsupervised or knowledge based systems. As knowledge base, the Italian version of DBpedia could be adopted, while the entity taxonomy could consist of the following classes: Thing, Event, Character, Location, Organization, Person and Product. As for Task 2, it is basically conceived as a follow-up of the SENTIPOLC task. In order to ensure continuity, it makes sense to carry out the annotation using a format compatibile with the existing dataset. The SENTIPOLC annotation scheme consists in four binary fields, indicating the presence of subjectivity, positive polarity, negative polarity, and irony. The fields are not mutually ex-clusive, for instance both positive and negative polarity can be present, resulting in a mixed polarity message.
However, not all possible combinations are allowed. Table  1  shows some examples of annotations from the SENTIPOLC dataset. With respect to the annotation adopted in SEN-TIPOLC, two additional fields are reported to reflect the task organization scheme we propose in this paper, including the sentiment analysis of tweet containing figurative language. These fields, highlighted in bold face, encode respectively the presence of positive and negative polarity considering the eventual polarity inversion due to the use of figurative language, thus the existing pos and neg fields refer to literal polarity of the tweet. For the annotation of the gold standard dataset for SENTIPOLC, the polarity of ironic messages has been annotated according to the intended meaning of the tweets, so for the new task the literal polarity will have to be manually annotated in order to complete the gold standard.
Annotation of items in the figurative language dataset could be the same as in the message-level polarity detection task of Evalita, but tweets would be opportunistically selected only if they contain figurative language, so as to reflect the goal of the task. For the entity-based sentiment analysis subtask, the boundaries for the marked instance will be also provided by indicating the offsets of the entity for which the polarity is annotated, as it was done for SemEval  (Pontiki et al. , 2014; Pontiki et al. , 2015). Participants who want to attempt entity-based sentiment analysis only can use the data that contains the gold output of Task 1, while those who want to perform entity detection and linking only, without doing sentiment analysis, are free to stop there. Participants who want to attempt both tasks can treat them in sequence, and evaluation can be performed for the whole system as well as for each of the two tasks (for the second one over gold input), as it will be done for teams that participate in one task only.
To promote a fair evaluation and comparison of supervised and unsupervised systems, corpora from different domains could be provided as train and test sets. To this aim, it could be possible to exploit the topic field in the annotation of tweets used in the SENTIPOLC dataset, where a flag indicates whether the tweets refer to the political domain or not. Hence, the train set could be built by merging political tweets from both the train and the test set used in SENTIPOLC. A new test set would be created by annotating tweets in one or more different domains. To conclude, we presented the entity linking and sentiment analysis tasks as related to one another, as shown in the pipeline in Figure  1 , specifying that participants will be able to choose which portions of the tasks they want to concentrate on.
In particular, our model is inspired by the successful neural network architecture presented by  Collobert et al. (2011)  to which we propose several innovative and valuable enhancements: (i) a simple recurrent feedback mechanism to model the dependencies between the output tags and (ii) a pre-training process based on two-steps: (a) training the network on a weekly labeled dataset and then (b) refining the weights on the supervised training set. Our final model obtains 82. 81 in F1 on the Evalita 2009 Italian dataset  (Speranza, 2009) , which is an improvement of +0. 81 over the  Zanoli and Pianta (2009)  system that won the competition. Our model only uses the words in the sentence, four morphological features and a gazetteer. Interestingly, if the gazetteer is removed from our network, it achieves an F1 of 81.