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Automatic Correction of Real-Word Errors in Spanish Clinical Texts
sensors Article Automatic Correction of Real-Word Errors in Spanish Clinical Texts Daniel Bravo-Candel 1,Jésica López-Hernández 1, José Antonio García-Díaz 1 , Fernando Molina-Molina 2 and Francisco García-Sánchez 1,* 1 Department of Informatics and Systems, Faculty of Computer Science, Campus de Espinardo, University of Murcia, 30100 Murcia, Spain; [email protected] (D.B.-C.); [email protected] (J.L.-H.); [email protected] (J.A.G.-D.) 2 VÓCALI Sistemas Inteligentes S.L., 30100 Murcia, Spain; [email protected] * Correspondence: [email protected]; Tel.: +34-86888-8107 Abstract: Real-word errors are characterized by being actual terms in the dictionary. By providing context, real-word errors are detected. Traditional methods to detect and correct such errors are mostly based on counting the frequency of short word sequences in a corpus. Then, the probability of a word being a real-word error is computed. On the other hand, state-of-the-art approaches make use of deep learning models to learn context by extracting semantic features from text. In this work, a deep learning model were implemented for correcting real-word errors in clinical text. Specifically, a Seq2seq Neural Machine Translation Model mapped erroneous sentences to correct them. For that, different types of error were generated in correct sentences by using rules. Different Seq2seq models were trained and evaluated on two corpora: the Wikicorpus and a collection of three clinical datasets. The medicine corpus was much smaller than the Wikicorpus due to privacy issues when dealing Citation: Bravo-Candel, D.; López-Hernández, J.; García-Díaz, with patient information. -
D1.3 Deliverable Title: Action Ontology and Object Categories Type
Deliverable number: D1.3 Deliverable Title: Action ontology and object categories Type (Internal, Restricted, Public): PU Authors: Daiva Vitkute-Adzgauskiene, Jurgita Kapociute, Irena Markievicz, Tomas Krilavicius, Minija Tamosiunaite, Florentin Wörgötter Contributing Partners: UGOE, VMU Project acronym: ACAT Project Type: STREP Project Title: Learning and Execution of Action Categories Contract Number: 600578 Starting Date: 01-03-2013 Ending Date: 30-04-2016 Contractual Date of Delivery to the EC: 31-08-2015 Actual Date of Delivery to the EC: 04-09-2015 Page 1 of 16 Content 1. EXECUTIVE SUMMARY .................................................................................................................... 2 2. INTRODUCTION .............................................................................................................................. 3 3. OVERVIEW OF THE ACAT ONTOLOGY STRUCTURE ............................................................................ 3 4. OBJECT CATEGORIZATION BY THEIR SEMANTIC ROLES IN THE INSTRUCTION SENTENCE .................... 9 5. HIERARCHICAL OBJECT STRUCTURES ............................................................................................. 13 6. MULTI-WORD OBJECT NAME RESOLUTION .................................................................................... 15 7. CONCLUSIONS AND FUTURE WORK ............................................................................................... 16 8. REFERENCES ................................................................................................................................ -
Welsh Language Technology Action Plan Progress Report 2020 Welsh Language Technology Action Plan: Progress Report 2020
Welsh language technology action plan Progress report 2020 Welsh language technology action plan: Progress report 2020 Audience All those interested in ensuring that the Welsh language thrives digitally. Overview This report reviews progress with work packages of the Welsh Government’s Welsh language technology action plan between its October 2018 publication and the end of 2020. The Welsh language technology action plan derives from the Welsh Government’s strategy Cymraeg 2050: A million Welsh speakers (2017). Its aim is to plan technological developments to ensure that the Welsh language can be used in a wide variety of contexts, be that by using voice, keyboard or other means of human-computer interaction. Action required For information. Further information Enquiries about this document should be directed to: Welsh Language Division Welsh Government Cathays Park Cardiff CF10 3NQ e-mail: [email protected] @cymraeg Facebook/Cymraeg Additional copies This document can be accessed from gov.wales Related documents Prosperity for All: the national strategy (2017); Education in Wales: Our national mission, Action plan 2017–21 (2017); Cymraeg 2050: A million Welsh speakers (2017); Cymraeg 2050: A million Welsh speakers, Work programme 2017–21 (2017); Welsh language technology action plan (2018); Welsh-language Technology and Digital Media Action Plan (2013); Technology, Websites and Software: Welsh Language Considerations (Welsh Language Commissioner, 2016) Mae’r ddogfen yma hefyd ar gael yn Gymraeg. This document is also available in Welsh. -
Librarians As Wikimedia Movement Organizers in Spain: an Interpretive Inquiry Exploring Activities and Motivations
Librarians as Wikimedia Movement Organizers in Spain: An interpretive inquiry exploring activities and motivations by Laurie Bridges and Clara Llebot Laurie Bridges Oregon State University https://orcid.org/0000-0002-2765-5440 Clara Llebot Oregon State University https://orcid.org/0000-0003-3211-7396 Citation: Bridges, L., & Llebot, C. (2021). Librarians as Wikimedia Movement Organizers in Spain: An interpretive inQuiry exploring activities and motivations. First Monday, 26(6/7). https://doi.org/10.5210/fm.v26i3.11482 Abstract How do librarians in Spain engage with Wikipedia (and Wikidata, Wikisource, and other Wikipedia sister projects) as Wikimedia Movement Organizers? And, what motivates them to do so? This article reports on findings from 14 interviews with 18 librarians. The librarians interviewed were multilingual and contributed to Wikimedia projects in Castilian (commonly referred to as Spanish), Catalan, BasQue, English, and other European languages. They reported planning and running Wikipedia events, developing partnerships with local Wikimedia chapters, motivating citizens to upload photos to Wikimedia Commons, identifying gaps in Wikipedia content and filling those gaps, transcribing historic documents and adding them to Wikisource, and contributing data to Wikidata. Most were motivated by their desire to preserve and promote regional languages and culture, and a commitment to open access and open education. Introduction This research started with an informal conversation in 2018 about the popularity of Catalan Wikipedia, Viquipèdia, between two library coworkers in the United States, the authors of this article. Our conversation began with a sense of wonder about Catalan Wikipedia, which is ranked twentieth by number of articles, out of 300 different language Wikipedias (Meta contributors, 2020). -
Learning to Read by Spelling Towards Unsupervised Text Recognition
Learning to Read by Spelling Towards Unsupervised Text Recognition Ankush Gupta Andrea Vedaldi Andrew Zisserman Visual Geometry Group Visual Geometry Group Visual Geometry Group University of Oxford University of Oxford University of Oxford [email protected] [email protected] [email protected] tttttttttttttttttttttttttttttttttttttttttttrssssss ttttttt ny nytt nr nrttttttt ny ntt nrttttttt zzzz iterations iterations bcorote to whol th ticunthss tidio tiostolonzzzz trougfht to ferr oy disectins it has dicomered Training brought to view by dissection it was discovered Figure 1: Text recognition from unaligned data. We present a method for recognising text in images without using any labelled data. This is achieved by learning to align the statistics of the predicted text strings, against the statistics of valid text strings sampled from a corpus. The figure above visualises the transcriptions as various characters are learnt through the training iterations. The model firstlearns the concept of {space}, and hence, learns to segment the string into words; followed by common words like {to, it}, and only later learns to correctly map the less frequent characters like {v, w}. The last transcription also corresponds to the ground-truth (punctuations are not modelled). The colour bar on the right indicates the accuracy (darker means higher accuracy). ABSTRACT 1 INTRODUCTION This work presents a method for visual text recognition without read (ri:d) verb • Look at and comprehend the meaning of using any paired supervisory data. We formulate the text recogni- (written or printed matter) by interpreting the characters or tion task as one of aligning the conditional distribution of strings symbols of which it is composed. -
The Wikipedia Diversity Observatory a Project to Identify and Bridge Content Gaps in Wikipedia
The Wikipedia Diversity Observatory A Project to Identify and Bridge Content Gaps in Wikipedia Marc Miquel-Ribé David Laniado Universitat Pompeu Fabra, Barcelona, Catalonia Eurecat, Centre Tecnològic de Catalunya [email protected] [email protected] ABSTRACT 1 Introduction In this paper we present the Wikipedia Diversity Observatory, Wikipedia is among the largest information repositories on the a project aimed to increase diversity within Wikipedia Internet that are both multilingual and created through language editions. The project includes dashboards with collaborative effort. Its prime objective1 is to "give free access visualizations and tools which show the gaps in terms of to the sum of all human knowledge" and, consequently, it exists concepts not represented or not shared across languages. The in as many as 309 languages. Even though the language dashboards are built on datasets generated for each of the more communities make the projects grow on a constant basis, the than 300 language editions, with features that label each article content does not represent the existing diversity in peoples, according to different categories relevant to overall content places, and cultures of the world; furthermore, there is a gap diversity. Through various examples, we show how the tools between language editions and articles often are not shared, or encourage and help editors to bridge the gaps in Wikipedia remain even unique to one language [1]. The creation of articles content. Finally, we discuss the project's impact on the in Wikipedia language editions is spontaneous and non- communities and implications for the Wikimedia movement, in directed. Several studies showed that cultural and geographical a moment in which covering diversity is considered strategic. -
Collocation Classification with Unsupervised Relation Vectors
Collocation Classification with Unsupervised Relation Vectors Luis Espinosa-Anke1, Leo Wanner2,3, and Steven Schockaert1 1School of Computer Science, Cardiff University, United Kingdom 2ICREA and 3NLP Group, Universitat Pompeu Fabra, Barcelona, Spain fespinosa-ankel,schockaerts1g@cardiff.ac.uk [email protected] Abstract Morphosyntactic relations have been the focus of work on unsupervised relational similarity, as Lexical relation classification is the task of it has been shown that verb conjugation or nomi- predicting whether a certain relation holds be- nalization patterns are relatively well preserved in tween a given pair of words. In this pa- vector spaces (Mikolov et al., 2013; Pennington per, we explore to which extent the current et al., 2014a). Semantic relations pose a greater distributional landscape based on word em- beddings provides a suitable basis for classi- challenge (Vylomova et al., 2016), however. In fication of collocations, i.e., pairs of words fact, as of today, it is unclear which operation per- between which idiosyncratic lexical relations forms best (and why) for the recognition of indi- hold. First, we introduce a novel dataset with vidual lexico-semantic relations (e.g., hyperonymy collocations categorized according to lexical or meronymy, as opposed to cause, location or ac- functions. Second, we conduct experiments tion). Still, a number of works address this chal- on a subset of this benchmark, comparing it in lenge. For instance, hypernymy has been modeled particular to the well known DiffVec dataset. In these experiments, in addition to simple using vector concatenation (Baroni et al., 2012), word vector arithmetic operations, we also in- vector difference and component-wise squared vestigate the role of unsupervised relation vec- difference (Roller et al., 2014) as input to linear tors as a complementary input. -
Comparative Evaluation of Collocation Extraction Metrics
Comparative Evaluation of Collocation Extraction Metrics Aristomenis Thanopoulos, Nikos Fakotakis, George Kokkinakis Wire Communications Laboratory Electrical & Computer Engineering Dept., University of Patras 265 00 Rion, Patras, Greece {aristom,fakotaki,gkokkin}@wcl.ee.upatras.gr Abstract Corpus-based automatic extraction of collocations is typically carried out employing some statistic indicating concurrency in order to identify words that co-occur more often than expected by chance. In this paper we are concerned with some typical measures such as the t-score, Pearson’s χ-square test, log-likelihood ratio, pointwise mutual information and a novel information theoretic measure, namely mutual dependency. Apart from some theoretical discussion about their correlation, we perform comparative evaluation experiments judging performance by their ability to identify lexically associated bigrams. We use two different gold standards: WordNet and lists of named-entities. Besides discovering that a frequency-biased version of mutual dependency performs the best, followed close by likelihood ratio, we point out some implications that usage of available electronic dictionaries such as the WordNet for evaluation of collocation extraction encompasses. dependence, several metrics have been adopted by the 1. Introduction corpus linguistics community. Typical statistics are Collocational information is important not only for the t-score (TSC), Pearson’s χ-square test (χ2), log- second language learning but also for many natural likelihood ratio (LLR) and pointwise mutual language processing tasks. Specifically, in natural information (PMI). However, usually no systematic language generation and machine translation it is comparative evaluation is accomplished. For example, necessary to ensure generation of lexically correct Kita et al. (1993) accomplish partial and intuitive expressions; for example, “strong”, unlike “powerful”, comparisons between metrics, while Smadja (1993) modifies “coffee” but not “computers”. -
Master Thesis
MASTER THESIS TITLE : Cultural configuration of Wikipedia: measuring Autoreferentiality in different languages MASTER DEGREE: Master of Science in Telecommunication Engineering & Management AUTHOR: Marc Miquel Ribe´ DIRECTOR: Horacio Rodr´ıguez Hontoria TUTOR: Sebastia` Sallent Ribes DATE: March 31, 2011 T´ıtol : Cultural configuration of Wikipedia: measuring Autoreferentiality in different languages Autor: Marc Miquel Ribe´ Director: Horacio Rodr´ıguez Hontoria Tutor: Sebastia` Sallent Ribes Data: 31 de marc¸de 2011 Resum ”Wikipedia es´ un projecte enciclopedic` multiling¨ue, col·laboratiu, basat en web i sense anim` de lucre impulsat per la Fundacio´ Wikimedia”, aix´ı es´ com s’autodescriu Wikipedia en la definicio´ de l’article que du el seu nom. Aixo` significa que l’enciclopedia` pot ser modificada en qualsevol moment, per qualsevol persona i des de qualsevol lloc. Aquestes premisses i la seva gran participacio´ fan que es tracti d’un excel·lent objecte social d’estudi, que a la vegada, per tractar-se d’un artefacte tecnologic,` permeti tambe´ l’´us de tecniques` de processament llenguatge natural, obtencio´ i mineria de dades. Tanmateix, en la recerca actual hi ha una clara mancanc¸a en software que pugui aproximar-s’hi d’una manera integral. Tenint en compte aquest buit realitzem una caracteritzacio´ de Wikipedia amb l’objectiu de coneixer` a fons quins son´ els elements i estructures d’informacio´ que conte´ i com despres´ poden obtenir-se mitjanc¸ant una eina anal´ıtica. Partim de l’API existent anomenada wikAPIdia, que desenvolupem fins a incloure-hi noves funcionalitats i posar-la apunt per a encarar m´ultiples escenaris i problematiques` de les ciencies` socials. -
Text Corpora and the Challenge of Newly Written Languages
Proceedings of the 1st Joint SLTU and CCURL Workshop (SLTU-CCURL 2020), pages 111–120 Language Resources and Evaluation Conference (LREC 2020), Marseille, 11–16 May 2020 c European Language Resources Association (ELRA), licensed under CC-BY-NC Text Corpora and the Challenge of Newly Written Languages Alice Millour∗, Karen¨ Fort∗y ∗Sorbonne Universite´ / STIH, yUniversite´ de Lorraine, CNRS, Inria, LORIA 28, rue Serpente 75006 Paris, France, 54000 Nancy, France [email protected], [email protected] Abstract Text corpora represent the foundation on which most natural language processing systems rely. However, for many languages, collecting or building a text corpus of a sufficient size still remains a complex issue, especially for corpora that are accessible and distributed under a clear license allowing modification (such as annotation) and further resharing. In this paper, we review the sources of text corpora usually called upon to fill the gap in low-resource contexts, and how crowdsourcing has been used to build linguistic resources. Then, we present our own experiments with crowdsourcing text corpora and an analysis of the obstacles we encountered. Although the results obtained in terms of participation are still unsatisfactory, we advocate that the effort towards a greater involvement of the speakers should be pursued, especially when the language of interest is newly written. Keywords: text corpora, dialectal variants, spelling, crowdsourcing 1. Introduction increasing number of speakers (see for instance, the stud- Speakers from various linguistic communities are increas- ies on specific languages carried by Rivron (2012) or Soria ingly writing their languages (Outinoff, 2012), and the In- et al. -
Reciprocal Enrichment Between Basque Wikipedia and Machine Translation
Reciprocal Enrichment Between Basque Wikipedia and Machine Translation Inaki˜ Alegria, Unai Cabezon, Unai Fernandez de Betono,˜ Gorka Labaka, Aingeru Mayor, Kepa Sarasola and Arkaitz Zubiaga Abstract In this chapter, we define a collaboration framework that enables Wikipe- dia editors to generate new articles while they help development of Machine Trans- lation (MT) systems by providing post-edition logs. This collaboration framework was tested with editors of Basque Wikipedia. Their post-editing of Computer Sci- ence articles has been used to improve the output of a Spanish to Basque MT system called Matxin. For the collaboration between editors and researchers, we selected a set of 100 articles from the Spanish Wikipedia. These articles would then be used as the source texts to be translated into Basque using the MT engine. A group of volunteers from Basque Wikipedia reviewed and corrected the raw MT translations. This collaboration ultimately produced two main benefits: (i) the change logs that would potentially help improve the MT engine by using an automated statistical post-editing system , and (ii) the growth of Basque Wikipedia. The results show that this process can improve the accuracy of an Rule Based MT (RBMT) system in nearly 10% benefiting from the post-edition of 50,000 words in the Computer Inaki˜ Alegria Ixa Group, University of the Basque Country UPV/EHU, e-mail: [email protected] Unai Cabezon Ixa Group, University of the Basque Country, e-mail: [email protected] Unai Fernandez de Betono˜ Basque Wikipedia and University of the Basque Country, e-mail: [email protected] Gorka Labaka Ixa Group, University of the Basque Country, e-mail: [email protected] Aingeru Mayor Ixa Group, University of the Basque Country, e-mail: [email protected] Kepa Sarasola Ixa Group, University of the Basque CountryU, e-mail: [email protected] Arkaitz Zubiaga Basque Wikipedia and Queens College, CUNY, CS Department, Blender Lab, New York, e-mail: [email protected] 1 2 Alegria et al. -
Same Gap, Different Experiences
SAME GAP, DIFFERENT EXPERIENCES An Exploration of the Similarities and Differences Between the Gender Gap in the Indian Wikipedia Editor Community and the Gap in the General Editor Community By Eva Jadine Lannon 997233970 An Undergraduate Thesis Presented to Professor Leslie Chan, PhD. & Professor Kenneth MacDonald, PhD. in the Partial Fulfillment of the Requirements for the Degree of Honours Bachelor of Arts in International Development Studies (Co-op) University of Toronto at Scarborough Ontario, Canada June 2014 ACKNOWLEDGEMENTS I would like to express my deepest gratitude to all of those people who provided support and guidance at various stages of my undergraduate research, and particularly to those individuals who took the time to talk me down off the ledge when I was certain I was going to quit. To my friends, and especially Jennifer Naidoo, who listened to my grievances at all hours of the day and night, no matter how spurious. To my partner, Karim Zidan El-Sayed, who edited every word (multiple times) with spectacular patience. And finally, to my research supervisors: Prof. Leslie Chan, you opened all the doors; Prof. Kenneth MacDonald, you laid the foundations. Without the support, patience, and understanding that both of you provided, it would have never been completed. Thank you. i EXECUTIVE SUMMARY According to the second official Wikipedia Editor Survey conducted in December of 2011, female- identified editors comprise only 8.5% of contributors to Wikipedia's contributor population (Glott & Ghosh, 2010). This significant lack of women and women's voices in the Wikipedia community has led to systemic bias towards male histories and culturally “masculine” knowledge (Lam et al., 2011; Gardner, 2011; Reagle & Rhue, 2011; Wikimedia Meta-Wiki, 2013), and an editing environment that is often hostile and unwelcoming to women editors (Gardner, 2011; Lam et al., 2011; Wikimedia Meta- Wiki, 2013).