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This paper proposes incorporating linguistic semantic information into discourse relation recognition and constructing a Semantic Augmented Chinese Discourse Corpus (SACA) comprising 9546 adversative complex sentences. In adversative complex sentences, we suggest a quadruple (P, Q, R, Q β ) representing internal semantic elements, where the semantic opposition between Q and Q β forms the basis of the adversative relationship. P denotes the premise, and R represents the adversative reason. The overall annotation approach of this corpus follows the Penn Discourse Treebank (PDTB), except for the classification of senses. We combined insights from the Chinese Discourse Treebank (CDTB) and obtained eight sense categories for Chinese adversative complex sentences. Based on this corpus, we explore the relationship between sense classification and internal semantic elements within our newly proposed Chinese Adversative Discourse Relation Recognition (CADRR) task. Leveraging deep learning techniques, we constructed various classification models and the model that utilizes internal semantic element features, demonstrating their effectiveness and the applicability of our SACA corpus. Compared with pre-trained models, our model incorporates internal semantic element information to achieve state-of-the-art performance. [ABSTRACT FROM AUTHOR]

This article introduces and explores the 3.5-million-word Victorian Anti-Vaccination Discourse Corpus (VicVaDis). The corpus is intended to provide a (freely accessible) historical resource for the investigation of the earliest public concerns and arguments against vaccination in England, which revolved around compulsory vaccination against smallpox in the second half of the 19th century. It consists of 133 anti-vaccination pamphlets and publications gathered from 1854 to 1906, a span of 53 years that loosely coincides with the Victorian era (1837–1901). This timeframe was chosen to capture the period between the 1853 Vaccination Act, which made smallpox vaccination for babies compulsory, and the 1907 Act that effectively ended the mandatory nature of vaccination. After an overview of the historical background, this article describes the rationale, design and construction of the corpus, and then demonstrates how it can be exploited to investigate the main arguments against compulsory vaccination by means of widely accessible corpus linguistic tools. Where appropriate, parallels are drawn between Victorian and 21st-century vaccine-hesitant attitudes and arguments. Overall, this article demonstrates the potential of corpus analysis to add to our understanding of historical concerns about vaccination. [ABSTRACT FROM AUTHOR]

This research introduces WCC-EC 2.0 (Web-Crawled Corpus—English and Chinese), a comprehensive parallel corpus designed for enhancing Neural Machine Translation (NMT), featuring over 1.6 million English-Chinese sentence pairs meticulously gathered via web crawling. This corpus, extracted through an advanced web crawler, showcases the vast linguistic diversity and richness of English and Chinese, uniquely spanning the rarely covered news and music domains. Our methodical approach in web crawling and corpus assembly, coupled with rigorous experiments and manual evaluations, demonstrated its superiority by achieving high BLEU scores, marking significant strides in translation accuracy and model resilience. Its inclusion of these specific areas adds significant value, providing a unique dataset that enriches the scope for NMT research and development. With the rise of NMT technology, WCC-EC 2.0 emerges not only as an invaluable resource for researchers and developers, but also as a pivotal tool for improving translation accuracy, training more resilient models, and promoting interlingual communication. [ABSTRACT FROM AUTHOR]

This paper describes the rationale for and design of a new multimodal corpus of L2 academic English from a Sino-British university in China: the Corpus of Chinese Academic Written and Spoken English (cawse). The unique context for this corpus provides language samples from Chinese students who use English as a second language (L2) in a preliminary-year programme, which prepares students for academic studies at university level, at a campus where English is used as the Medium of Instruction (emi). Data were collected from a variety of settings, including written (i.e., exam scripts and essays) and spoken assessments (i.e., interviews and presentations), covering the full range of grades awarded to those language samples, as well as from student group interactions during teaching and learning activities. The multimodal nature of the corpus is realised through the availability of selected audio/video recordings accompanied by the orthographically transcribed text. This open-access corpus is designed to help shed light on Chinese students' academic L2 English language use in a variety of written, spoken and multimodal discourses. [ABSTRACT FROM AUTHOR]

The study of dialectal microvariation in spoken Spanish faces challenges due to the absence of an adequate morpho-syntactically annotated and parsed corpus. Therefore, this article introduces a novel technique, a game-based approach, for creating resources for non-standard Spanish language varieties. The article provides an overview of the progress in designing three Games With A Purpose (GWAPs) prototypes, to wit, Agentes, Tesoros, and Anotatlón. These games aim to facilitate the confirmation and correction of the morpho-syntactic tagging task of the COSER-AP (Corpus Oral y Sonoro del Español Rural-Anotado y Parseado, 'Annotated and Parsed Audible Corpus of Spoken Rural Spanish'). First, the article presents the methodology used to build the games. Second, it offers a detailed description of the implemented Game Design Elements (GDEs). Finally, the article discusses the results of a pilot evaluation that assesses player enjoyment and the linguistic accuracy. Findings are promising, with Tesoros and Anotatlón demonstrating high levels of enjoyment. Additionally, Agentes proves to be effective in collecting a large number of annotations. The linguistic accuracy also shows potential benefits of gamified approaches in linguistic annotation tasks. However, it also emphasizes the importance of considering regional in player assessment and training them in multidialectal contexts. [ABSTRACT FROM AUTHOR]

In this paper, we present an overview of existing parallel corpora for Automatic Text Simplification (ATS) in different languages focusing on the approach adopted for their construction. We make the main distinction between manual and (semi)–automatic approaches in order to investigate in which respect complex and simple texts vary and whether and how the observed modifications may depend on the underlying approach. To this end, we perform a two-level comparison on Italian corpora, since this is the only language, with the exception of English, for which there are large parallel resources derived through the two approaches considered. The first level of comparison accounts for the main types of sentence transformations occurring in the simplification process, the second one examines the results of a linguistic profiling analysis based on Natural Language Processing techniques and carried out on the original and the simple version of the same texts. For both levels of analysis, we chose to focus our discussion mostly on sentence transformations and linguistic characteristics that pertain to the morpho-syntactic and syntactic structure of the sentence. [ABSTRACT FROM AUTHOR]

The paper describes the process of building the electronic corpus of 17th- and 18th-century Polish texts, a relatively large, balanced, structurally and morphologically annotated resource of the Middle Polish language, available for searching at https://www.korba.edu.pl. The corpus consists of samples extracted from over seven hundred texts written and published between 1601 and 1772, summing up to a total size of 13.5 million tokens which makes it one of the largest historical corpora for a Slavic language. [ABSTRACT FROM AUTHOR]

Pretrained models with high performance in various tasks within natural language processing have the advantage of learning the linguistic patterns of sentences using large corpus during the training, allowing each token in the input sentence to be represented with appropriate feature vectors. One of the methods of constructing a corpus required for a pre-trained model training is a collection method using web crawler. However, sentences that exist on web may contain unnecessary words in some or all of the sentences because they have various patterns. In this paper, we propose a dataset construction method for filtering sentences containing unnecessary words using neural network models for corpus collected from the web. As a result, we construct a dataset containing a total of 2,330 sentences. We also evaluated the performance of neural network models on the constructed dataset, and the BERT model showed the highest performance with an accuracy of 93.75%. [ABSTRACT FROM AUTHOR]

Live blogs are an increasingly popular news format to cover breaking news and live events in online journalism. Online news websites around the world are using this medium to give their readers a minute by minute update on an event. Good summaries enhance the value of the live blogs for a reader, but are often not available. In this article, (a) we first define the task of summarizing a live blog, (b) study ways of automatically collecting corpora for live blog summarization, and (c) understand the complexity of the task by empirically evaluating well-known state-of-the-art unsupervised and supervised summarization systems on our new corpus. We show that live blog summarization poses new challenges in the field of news summarization, since frequency and positional signals cannot be used. We make our tools publicly available to reconstruct the corpus and to conduct our empirical experiments. This encourages the research community to build upon and replicate our results. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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The main purposes of this article are to provide an overview of a research project on a longitudinal learner spoken corpus and to share procedures related to the transcription of learners' utterances from audio files using automated speech recognition (ASR) technology (IBM Watson Speech-to-text). The data of the corpus were collected twice or thrice a year for three consecutive years from 2016, creating eight data collection points altogether. They were gathered from 120 secondary school students who had been learning English in an English as a Foreign Language context for three years. The students were asked to take a monologue speaking test, the Telephone Standard Speaking Test, consisting of various tasks. The overall discussion of the article focuses on the details of this project and highlights how a methodological approach of combining electronic learner language data and ASR technology is useful in constructing learner spoken corpora. [ABSTRACT FROM AUTHOR]

This paper describes the construction process involved in creating a robust local learner corpus of texts produced by international students in a first-year writing course at a large public, mid-western university in the U.S. We show how involving faculty members and graduate students of our local writing program in the process of learner corpus analysis provides them with opportunities to develop their skills and knowledge as writing instructors, course designers, and, ultimately, knowledge producers. An additional benefit of such an undertaking is that the corpus can become part of the infrastructure of a research community that allows continued contributions by others individually and collaboratively. We also illustrate the usefulness of our local learner corpus for research, teaching, mentoring, and collaboration within our writing program with examples of the research projects and teaching interventions we have developed. [ABSTRACT FROM AUTHOR]

Removal of boilerplate is one of the essential tasks in web corpus construction and web indexing. Boilerplate (redundant and automatically inserted material like menus, copyright notices, navigational elements, etc.) is usually considered to be linguistically unattractive for inclusion in a web corpus. Also, search engines should not index such material because it can lead to spurious results for search terms if these terms appear in boilerplate regions of the web page. In this paper, I present and evaluate a supervised machine-learning approach to general-purpose boilerplate detection for languages based on Latin alphabets using Multi-Layer Perceptrons (MLPs). It is both very efficient and very accurate (between 95 % and $$99\,\%$$ correct classifications, depending on the input language). I show that language-specific classifiers greatly improve the accuracy of boilerplate detectors. The single features used for the classification are evaluated with regard to the merit they contribute to the classification. Furthermore, I show that the accuracy of the MLP is on a par with that of a wide range of other classifiers. My approach has been implemented in the open-source texrex web page cleaning software, and large corpora constructed using it are available from the COW initiative, including the CommonCOW corpora created from CommonCrawl datasets. [ABSTRACT FROM AUTHOR]

This paper introduces the Spoken British National Corpus 2014, an 11.5-million-word corpus of orthographically transcribed conversations among L1 speakers of British English from across the UK, recorded in the years 2012-2016. After showing that a survey of the recent history of corpora of spoken British English justifies the compilation of this new corpus, we describe the main stages of the Spoken BNC2014's creation: design, data and metadata collection, transcription, XML encoding, and annotation. In doing so we aim to (i) encourage users of the corpus to approach the data with sensitivity to the many methodological issues we identified and attempted to overcome while compiling the Spoken BNC2014, and (ii) inform (future) compilers of spoken corpora of the innovations we implemented to attempt to make the construction of corpora representing spontaneous speech in informal contexts more tractable, both logistically and practically, than in the past. [ABSTRACT FROM AUTHOR]

Abstract: This paper reports on the feasibility and value of an approach to teaching EAP writing in which students construct and examine their own individual, discipline-specific corpora. The approach was trialed in multidisciplinary classes of advanced-level students (mostly graduates). The course consisted of six weekly 2-h sessions. Data were collected from initial and final questionnaires, which provided background information and asked students to evaluate the corpus work. Data from 50 participants are presented and show generally positive results. Over 90% of students found it easy to build their own corpora and most succeeded in constructing a corpus of 10–15 research articles. Most students were enthusiastic about working with their own corpora: about 90% agreed that their corpus helped them improve their writing and intended to use it in the future. This suggests that even corpora of this size and type can provide a useful resource for writing discipline-specific texts. The paper discusses the data on participants’ attitudes and experiences and considers the issues and problems that arise in connection with do-it-yourself corpus-building. It argues that this approach need not be restricted to small groups of well-resourced students, but can be implemented in mainstream EAP classes. [Copyright &y& Elsevier]

This exploratory study describes a framework for data-driven learning (DDL), inGeneral (non-major) English university classes, in which learners construct linguistic corpora instead of merely consulting them. Prior related work has addressed the needs of language specialists, in particular trainee translators who are learning how to compile glossaries, rather than non-major students of English. It is argued in this article that the process of creating a corpus inculcates a sense of ownership in the learner and therefore has a motivational impetus. This is especially true, it is claimed here, when the topic of the corpus is of personal interest to the learner, or coincides with their major field of study. Learners may pursue language study for only a short period of their university career, but once the corpus is constructed, some students may be sufficiently motivated to consult it and add to it when needed. Moreover, the process of compiling the corpus may lead to the acquisition of not only language but also useful transferable skills, including information technology and problem-solving competencies. This study presents some of the motivational issues surrounding DDL in Asia and suggests corpus construction as a solution. Previous research on corpus construction by learners is reviewed. Inthe experiment which forms the core of this study, 90 freshmen compiled andanalyzed corpora as part of a General English course in Taiwan. Of these, 19students completed final projects based on corpora they had compiled. Their findings – and reactions to the use of corpora compilation as a language learning tool – are reported in a qualitative data analysis. [ABSTRACT FROM AUTHOR]

The role of the Web for text corpus construction is becoming increasingly significant. However, the contribution of the Web is largely confined to building a general virtual corpus or low quality specialised corpora. In this paper, we introduce a new technique called SPARTAN for constructing specialised corpora from the Web by systematically analysing website contents. Our evaluations show that the corpora constructed using our technique are independent of the search engines employed. In particular, SPARTAN-derived corpora outperform all corpora based on existing techniques for the task of term recognition. [ABSTRACT FROM AUTHOR]

The focus of this paper is two-fold. First, similarities generated from six semantic models were compared to human ratings of paragraph similarity on two datasets-23 World Entertainment News Network paragraphs and 50 ABC newswire paragraphs. Contrary to findings on smaller textual units such as word associations (), our results suggest that when single paragraphs are compared, simple nonreductive models (word overlap and vector space) can provide better similarity estimates than more complex models (LSA, Topic Model, SpNMF, and CSM). Second, various methods of corpus creation were explored to facilitate the semantic models' similarity estimates. Removing numeric and single characters, and also truncating document length improved performance. Automated construction of smaller Wikipedia-based corpora proved to be very effective, even improving upon the performance of corpora that had been chosen for the domain. Model performance was further improved by augmenting corpora with dataset paragraphs. [ABSTRACT FROM AUTHOR]

This article introduces ukWaC, deWaC and itWaC, three very large corpora of English, German, and Italian built by web crawling, and describes the methodology and tools used in their construction. The corpora contain more than a billion words each, and are thus among the largest resources for the respective languages. The paper also provides an evaluation of their suitability for linguistic research, focusing on ukWaC and itWaC. A comparison in terms of lexical coverage with existing resources for the languages of interest produces encouraging results. Qualitative evaluation of ukWaC versus the British National Corpus was also conducted, so as to highlight differences in corpus composition (text types and subject matters). The article concludes with practical information about format and availability of corpora and tools. [ABSTRACT FROM AUTHOR]