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francaisCet article cherche a stimuler une reflexion plus approfondie dans la traductologie de corpus concernant les outils numeriques au moyen desquels la recherche est menee dans cette discipline. L’article explique diverses possibilites et contraintes de l’analyse des textes traduits, assistee par un concordancier « KeyWord In Context » (KWIC) et par d’autres outils de visualisation des donnees. Une attention particuliere sera portee a la maniere dont ces affordances technologiques ont faconne des hypotheses theoriques centrales ainsi que les grands principes de la construction de corpus, dans l’approche traductologique basee sur le corpus et dans d’autres domaines proches. Cette discussion est illustree par une etude de cas appliquant la suite logicielle developpee pour l’analyse de corpus dans le cadre du projet Genealogies of Knowledge, a deux traductions en anglais du Manifeste communiste. EnglishThis paper seeks to promote deeper reflection within the field of corpus-based translation studies (CTS) regarding the digital tools by means of which research in this discipline proceeds. It explicates a range of possibilities and constraints brought to the analysis of translated texts by the keyword in context (KWIC) concordancer and other data visualisation applications, paying particular attention to the ways in which these technological affordances have actively shaped central theoretical hypotheses within CTS and related fields, as well as the general principles of corpus construction. This discussion is illustrated through a small case study which applies the suite of corpus analysis tools developed as part of the Genealogies of Knowledge project to the investigation of two English translations of the Communist Manifesto.

This paper presents the construction of a corpus of writings by Slovene learners of Japanese as a foreign language at the beginner and intermediate levels and an analysis of the grammar errors contained within it, with the purpose of providing a simple and effective means of acquiring data on errors made by students of Japanese as a second language. Additionally, an error analysis of the grammar errors in the corpus and a comparison of the most common errors found on both levels, reveals the types of errors that carry over from the beginner to the intermediate level, negatively affecting the learning process. By compiling and analyzing a collection of 182 written texts written by Japanese learners, 492 cases of grammar misuse were observed on the beginner and 564 on the intermediate level. A comparative analysis of the most common types of grammar misuse on each level highlights the types of errors that seem to carry over from the beginner to the intermediate level. The findings can be useful to Japanese language learners as well as teachers. Furthermore, the learner’s corpus created in the process marks the first step towards the creation of a larger, annotated and publicly accessible learner corpus of writings by Slovenian learners of Japanese to be used for further research in the field of second language acquisition.

The field of writing analytics is currently in a state of growth, redefinition, and refinement. In this essay, I review the trajectory of a related subfield, text-as-data in political science, as a lens through which to understand the present and future of writing analytics. I first describe how text-as-data has evolved over several eras, before transitioning to a review of some of the most exciting contemporary areas of political text-as-data. I then draw parallels between these developments and the work being done by the community of writing analytics scholars across the globe. I conclude by proposing several considerations for current practitioners seeking to emulate the “success story” of political text-as-data, including investment in the collection of new, high-quality corpora, development of shareable and open-source methodological tools for text analysis, and the strengthening of a community of scholarship.

Currently, there are only a limited number of Japanese-Chinese bilingual corpora of a sufficient amount that can be used as training data for neural machine translation (NMT). In particular, there are few corpora that include spoken language such as daily conversation. In this research, we attempt to construct a Japanese-Chinese bilingual corpus of a certain scale by crawling the subtitle data of movies and TV series from the websites. We calculated the BLEU scores of the constructed WCC-JC (Web Crawled Corpus—Japanese and Chinese) and the other compared corpora. We also manually evaluated the translation results using the translation model trained on the WCC-JC to confirm the quality and effectiveness.

This article details the data management principles and practices developed by the University of Ottawa Sociolinguistics Lab (http://www.sociolinguistics.uottawa.ca/thelab.html), home to 19 major corpora representing hundreds of hours and millions of words of recorded everyday speech. Couched within the variationist framework for linguistic analysis, it provides a practical overview of tried-and-true methods for corpus construction, including data collection, transcription, annotation, and citation, as well as data retrieval, coding, and analysis. It also features observations on data preservation and data lifecycle, and discusses ethical considerations involved in collecting and analyzing vernacular speech. It concludes with a summary of the wide variety of linguistic applications to which properly managed spontaneous speech data can be put. Cet article détaille les principes et pratiques de gestion de données en vigueur au Laboratoire de sociolinguistique de l’Université d’Ottawa (le Labo ; http://www.sociolinguistique.uottawa.ca/laboratoire.html), centre qui abrite 19 importants corpus correspondant à des centaines d’heures d’enregistrement de parler spontané. Notre propos s’inscrit dans le cadre de la sociolinguistique variationniste et fournit un aperçu des méthodes éprouvées en matière de constitution de corpus, qui comprend notamment la collecte, la transcription, l’annotation, le repérage, le codage, et l’analyse des données. Nous abordons également la préservation et le cycle de vie des données, et jetons un coup d’œil aux considérations déontologiques qui caractérisent la collecte et l’analyse du vernaculaire. Nous concluons par un survol des nombreuses applications linguistiques possibles des données de parler spontané bien gérées.

With the rapidly increasing use of social media platforms, much of our lives is spent online. Despite the great advantages of using social media, unfortunately, the spread of hate, cyberbullying, harassment, and trolling can be very common online. Many extremists use social media platforms to communicate their messages of hatred and spread violence, which may result in serious psychological consequences and even contribute to real-world violence. Thus, the aim of this research was to build the HateMotiv corpus, a freely available dataset that is annotated for types of hate crimes and the motivation behind committing them. The dataset was developed using Twitter as an example of social media platforms and could provide the research community with a very unique, novel, and reliable dataset. The dataset is unique as a consequence of its topic-specific nature and its detailed annotation. The corpus was annotated by two annotators who are experts in annotation based on unified guidelines, so they were able to produce an annotation of a high standard with F-scores for the agreement rate as high as 0.66 and 0.71 for type and motivation labels of hate crimes, respectively.

Background In this study, we focus on building a fine-grained entity annotation corpus with the corresponding annotation guideline of traditional Chinese medicine (TCM) clinical records. Our aim is to provide a basis for the fine-grained corpus construction of TCM clinical records in future. Methods We developed a four-step approach that is suitable for the construction of TCM medical records in our corpus. First, we determined the entity types included in this study through sample annotation. Then, we drafted a fine-grained annotation guideline by summarizing the characteristics of the dataset and referring to some existing guidelines. We iteratively updated the guidelines until the inter-annotator agreement (IAA) exceeded a Cohen’s kappa value of 0.9. Comprehensive annotations were performed while keeping the IAA value above 0.9. Results We annotated the 10,197 clinical records in five rounds. Four entity categories involving 13 entity types were employed. The final fine-grained annotated entity corpus consists of 1104 entities and 67,799 tokens. The final IAAs are 0.936 on average (for three annotators), indicating that the fine-grained entity recognition corpus is of high quality. Conclusions These results will provide a foundation for future research on corpus construction and named entity recognition tasks in the TCM clinical domain.

MotivationAnnotation tools are applied to build training and test corpora, which are essential for the development and evaluation of new natural language processing algorithms. Further, annotation tools are also used to extract new information for a particular use case. However, owing to the high number of existing annotation tools, finding the one that best fits particular needs is a demanding task that requires searching the scientific literature followed by installing and trying various tools.MethodsWe searched for annotation tools and selected a subset of them according to five requirements with which they should comply, such as being Web-based or supporting the definition of a schema. We installed the selected tools (when necessary), carried out hands-on experiments and evaluated them using 26 criteria that covered functional and technical aspects. We defined each criterion on three levels of matches and a score for the final evaluation of the tools.ResultsWe evaluated 78 tools and selected the following 15 for a detailed evaluation: BioQRator, brat, Catma, Djangology, ezTag, FLAT, LightTag, MAT, MyMiner, PDFAnno, prodigy, tagtog, TextAE, WAT-SL and WebAnno. Full compliance with our 26 criteria ranged from only 9 up to 20 criteria, which demonstrated that some tools are comprehensive and mature enough to be used on most annotation projects. The highest score of 0.81 was obtained by WebAnno (of a maximum value of 1.0).

Cardiovascular disease (CVD) has become the leading cause of death in China, and most of the cases can be prevented by controlling risk factors. The goal of this study was to build a corpus of CVD risk factor annotations based on Chinese electronic medical records (CEMRs). This corpus is intended to be used to develop a risk factor information extraction system that, in turn, can be applied as a foundation for the further study of the progress of risk factors and CVD. We designed a light annotation task to capture CVD risk factors with indicators, temporal attributes and assertions that were explicitly or implicitly displayed in the records. The task included: 1) preparing data; 2) creating guidelines for capturing annotations (these were created with the help of clinicians); 3) proposing an annotation method including building the guidelines draft, training the annotators and updating the guidelines, and corpus construction. Then, a risk factor annotated corpus based on de-identified discharge summaries and progress notes from 600 patients was developed. Built with the help of clinicians, this corpus has an inter-annotator agreement (IAA) F1-measure of 0.968, indicating a high reliability. To the best of our knowledge, this is the first annotated corpus concerning CVD risk factors in CEMRs and the guidelines for capturing CVD risk factor annotations from CEMRs were proposed. The obtained document-level annotations can be applied in future studies to monitor risk factors and CVD over the long term., Comment: 32 pages, 3 figures, 3 tables

PAISA' is a Creative Commons licensed, large web corpus of contemporary Italian. We describe the design, harvesting, and processing steps involved in its creation.

In this paper, we report our tree based statistical translation study from English to Turkish. We describe our data generation process and report the initial results of tree-based translation under a simple model. For corpus construction, we used the Penn Treebank in the English side. We manually translated about 5K trees from English to Turkish under grammar constraints with adaptations to accommodate the agglutinative nature of Turkish morphology. We used a permutation model for subtrees together with a word to word mapping. We report BLEU scores under simple choices of inference algorithms. Publisher's Version