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Utilizing deep learning for data mining in biomedicine often falls short in leveraging prior knowledge and adapting to the complexities of biomedical literature mining. Entity recognition, a fundamental task in information extraction, also provides data support for Natural Language Processing (NLP) downstream tasks. Bovine Viral Diarrhea Virus (BVDV) results in considerable economic losses in the cattle industry due to calf diarrhea, bovine respiratory syndrome, and cow abortion. This study aims to extract information on BVDV from relevant literature and build a knowledge base. It enhances feature extraction in the BioBERT pre-trained model using the Machine Reading Comprehension (MRC) framework for information fusion and bi-directionally extracts corpus information through the Bi-LSTM network, followed by a CRF layer for decoding and prediction. The results show the construction of a BVDV Corpus with 22 biomedical entities and introduce the BioBERT-Bi-LSTM-CRF Integrated with MRC (BBCM) model for Named Entity Recognition (NER), combining prior knowledge and the reading comprehension framework (MRC). The BBCM model achieves F 1 -scores of 78.79% and 76.3% on the public datasets JNLPBA and GENIA, respectively, and 67.52% on the BVDV Corpus, outperforming other models. This research presents a targeted NER method for BVDV, effectively identifying related entities and exploring their relationships, thus providing valuable data support for NLP's downstream tasks. [ABSTRACT FROM AUTHOR]

Artificial Intelligence (AI) has had a significant impact on language education, arousing the related studies from different fields and perspectives. However, the extant review articles only focused on a certain aspect in the field, thus neglecting the multi-disciplinary and multi-perspective features of AI-enhanced language education. The aim of the current study is to bridge this gap by reviewing the 812 articles selected from Web of Science via CiteSpace. The study found that the existing studies were mainly conducted from the perspectives of computer science, linguistics and psychology. Besides, the individualized development of language education supported by technology was not obvious, and the degree of human-computer interaction was weak. Moreover, previous studies focused more on the advantages of AI-enhanced language education than the disadvantages, and the empirical research to analyze specific problems was insufficient. In the future, the research tends to strengthen the cooperation among related disciplines and focus on the individualized development of language education. In addition, empirical research should be strengthened to explore the efficiency of AI-enhanced language education in order to promote the reform of language education.

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]

To promote the transformation of education and teaching concepts, guide the reform of teaching content and teaching methods, and promote the joint construction and sharing of high-quality curriculum teaching resources in colleges and universities through modern information technology, improve the quality of talent training and serve the construction of a learning society. Using video analysis and website research methods, the author takes the cross-cultural communication courses offered by 22 universities in the national excellent resource sharing courses published by the multimodal online corpus of “Love Course Network” as the research sample, taking the playability of teaching videos, noise level, picture definition, multimedia courseware playing, utilization of curriculum resources and network interaction as research contents, the construction and application of teaching videos are analyzed in depth. The results show that, in the construction of intercultural communication sharing courses, there are still problems such as poor playability of teaching videos, noise and poor picture clarity.

Drought disasters have caused serious impacts on the social economy and ecological environment, which are continuously and increasingly exacerbated by climate warming and other factors. Drought disaster management usually involves processing a mass of isolated data from many fields expressed in different terminologies and formats. These heterogeneous data or so-called data silos have greatly hindered drought disaster management in an information-rich manner. Establishing a drought disaster knowledge graph can facilitate the reuse of these heterogeneous data and provide references for drought disaster management, and ontology design and named entity recognition are the two major challenges. Therefore, in this study, we first designed a drought disaster ontology by recognizing the major concepts in the drought disaster field and their relationships, which was implemented with an ontology modeling language. We next constructed a drought disaster corpus and an integrated entity recognition model that was built by integrating multiple deep learning methods. Finally, we applied the integrated entity recognition model to extract information from the CNKI literature database. The integrated model shows satisfactory results in drought disaster named entity recognition. We thus conclude that combining ontology and deep learning technology toward establishing a knowledge graph for drought disasters is promising. HIGHLIGHTS Ontology was used to construct the schema for drought disaster knowledge graphs.; A corpus of drought disasters was constructed with unstructured documents.; Automatic drought disaster named entity recognition was achieved by the deep learning method.;

Cardiovascular disease has a significant impact on both society and patients, making it necessary to conduct knowledge-based research such as research that utilizes knowledge graphs and automated question answering. However, the existing research on corpus construction for cardiovascular disease is relatively limited, which has hindered further knowledge-based research on this disease. Electronic medical records contain patient data that span the entire diagnosis and treatment process and include a large amount of reliable medical information. Therefore, we collected electronic medical record data related to cardiovascular disease, combined the data with relevant work experience and developed a standard for labeling cardiovascular electronic medical record entities and entity relations. By building a sentence-level labeling result dictionary through the use of a rule-based semi-automatic method, a cardiovascular electronic medical record entity and entity relationship labeling corpus (CVDEMRC) was constructed. The CVDEMRC contains 7691 entities and 11,185 entity relation triples, and the results of consistency examination were 93.51% and 84.02% for entities and entity-relationship annotations, respectively, demonstrating good consistency results. The CVDEMRC constructed in this study is expected to provide a database for information extraction research related to cardiovascular diseases.

Given the specialized nature of English text translation in academic settings and the frequent absence of reliable reference materials, translation processes often lack verifiable evidence, impacting both efficiency and quality. This paper addresses these challenges by first developing a basic syntactic error correction model that leverages the structural features of recurrent neural networks (RNNs) and gated recurrent unit (GRU) networks to establish a Seq2Seq syntactic error correction framework. To enhance this model, we incorporate an Attention mechanism into the Seq2Seq-based English grammar error correction model. This innovation enables the model to swiftly focus on segments most pertinent to the current context, thereby boosting operational efficiency. Subsequently, we create a college English text translation data corpus using Numerical Intelligence techniques to maintain grammatical accuracy within the corpus. Comparative analysis of the model training reveals that the Seq2Seq model with the Attention mechanism achieves an accuracy rate of 41.7%, which represents a 9.19% improvement over the basic model, underscoring its significant advantage. Furthermore, the average accuracy rate for grammatical error correction stands at 72.87%. A practical application analysis shows a minimal difference of only 0.05 points between the model’s grammar correction scores and those of human teachers. The corpus developed using this enhanced grammar error correction model scored 86 overall, outperforming other corpora. Therefore, the augmented Seq2Seq model with the Attention mechanism proves highly effective for developing English text translation corpora in collegiate environments.

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]

Most corpora tacitly subscribe to a speech-only view filtering out anything that is not a ‘word’ and transcribing the spoken language merely orthographically despite the fact that the “speech-only view on language is fundamentally incomplete” (Kok 2017, 2) due to the deep intertwining of the verbal, vocal, and kinesic modalities (Levinson and Holler 2014). This article introduces the Freiburg Multimodal Interaction Corpus (FreMIC), a multimodal and interactional corpus of unscripted conversation in English currently under construction. At the time of writing, FreMIC comprises (i) c. 29 h of video-recordings transcribed and annotated in detail and (ii) automatically (and manually) generated multimodal data. All conversations are transcribed in ELAN both orthographically and using Jeffersonian conventions to render verbal content and interactionally relevant details of sequencing (e.g. overlap, latching), temporal aspects (pauses, acceleration/deceleration), phonological aspects (e.g. intensity, pitch, stretching, truncation, voice quality), and laughter. Moreover, the orthographic transcriptions are exhaustively PoS-tagged using the CLAWS web tagger (Garside and Smith 1997). ELAN-based transcriptions also provide exhaustive annotations of re-enactments (also referred to as (free) direct speech, constructed dialogue, etc.) as well as silent gestures (meaningful gestures that occur without accompanying speech). The multimodal data are derived from psychophysiological measurements and eye tracking. The psychophysiological measurements include, inter alia, electrodermal activity or GSR, which is indicative of emotional arousal (e.g. Peräkylä et al. 2015). Eye tracking produces data of two kinds: gaze direction and pupil size. In FreMIC, gazes are automatically recorded using the area-of-interest technology. Gaze direction is interactionally key, for example, in turn-taking (e.g. Auer 2021) and re-enactments (e.g. Pfeiffer and Weiss 2022), while changes in pupil size provide a window onto cognitive intensity (e.g. Barthel and Sauppe 2019). To demonstrate what opportunities FreMIC’s (combination of) transcriptions, annotations, and multimodal data open up for research in Interactional (Corpus) Linguistics, this article reports on interim results derived from work-in-progress.

With the accelerating development of science and technology, the academic papers being published in various fields are increasing rapidly. Academic papers specially in science and technology fields are a crucial media for researchers who develop new technologies by identifying knowledge regarding the latest technological trends and conduct derivative studies in science and technology. Therefore, the continual collection of extensive academic papers, structuring of metadata, and construction of databases are significant tasks. However, research on automatic metadata extraction from Korean papers is not being actively conducted currently owing to insufficient Korean training data. We automatically constructed the largest labeled corpus in South Korea to date from 315,320 PDF papers belonging to 503 Korean academic journals and this labeled corpus can be used for training the models of automatic extraction for 12 metadata types from PDF papers. This labeled corpus is available at https://doi.org/10.23057/48. Moreover, we developed inspection process and guidelines for the automatically constructed data and performed a full inspection of the validation and testing data. The reliability of the inspected data was verified through the inter-annotator agreement measurement. Using our corpus, we trained and evaluated the BERT based transfer learning model to verify its reliability. Furthermore, we proposed new training methods that can improve the metadata extraction performance of Korean papers, and through these methods, we developed KorSciBERT-ME-J and KorSciBERT-ME-J+C models. The KorSciBERT-ME-J showed the highest performance with an F1 score of 99.36%, as well as robust performance in automatic metadata extraction from Korean academic papers in various formats.

Abstract The relationship between biomedical entities is complex, and many of them have not yet been identified. For many biomedical research areas including drug discovery, it is of paramount importance to identify the relationships that have already been established through a comprehensive literature survey. However, manually searching through literature is difficult as the amount of biomedical publications continues to increase. Therefore, the relation classification task, which automatically mines meaningful relations from the literature, is spotlighted in the field of biomedical text mining. By applying relation classification techniques to the accumulated biomedical literature, existing semantic relations between biomedical entities that can help to infer previously unknown relationships are efficiently grasped. To develop semantic relation classification models, which is a type of supervised machine learning, it is essential to construct a training dataset that is manually annotated by biomedical experts with semantic relations among biomedical entities. Any advanced model must be trained on a dataset with reliable quality and meaningful scale to be deployed in the real world and can assist biologists in their research. In addition, as the number of such public datasets increases, the performance of machine learning algorithms can be accurately revealed and compared by using those datasets as a benchmark for model development and improvement. In this paper, we aim to build such a dataset. Along with that, to validate the usability of the dataset as training data for relation classification models and to improve the performance of the relation extraction task, we built a relation classification model based on Bidirectional Encoder Representations from Transformers (BERT) trained on our dataset, applying our newly proposed fine-tuning methodology. In experiments comparing performance among several models based on different deep learning algorithms, our model with the proposed fine-tuning methodology showed the best performance. The experimental results show that the constructed training dataset is an important information resource for the development and evaluation of semantic relation extraction models. Furthermore, relation extraction performance can be improved by integrating our proposed fine-tuning methodology. Therefore, this can lead to the promotion of future text mining research in the biomedical field.

Discourse structure is generally represented as hierarchical structure, the two most well known representations are rhetorical structure theory (RST) and Penn discourse treebank (PDTB). The main problem of the hierarchical structure is that it can not describe the direct semantic relevance between the elementary discourse units (EDU), especially the non-adjacent and cross-level EDUs. Discourse dependency structure (DDS) has been put forward in recent years to describe the head-dependent relation between the EDUs. However, the judgment process of the head can not be answered theoretically. This problem is particularly serious in Chinese discourse analysis, because Chinese lacks the form differences between the main clauses and the subordinate clauses. In this paper, we propose clause relevance structure to represent the discourse structure. Compared with the hierarchical discourse structure and DDS, the clause relevance structure can effectively describe the direct semantic association between discontinuous and cross-level clauses in a text, and the construction of structure is not presupposed by the head recognition. We propose the judgment criteria and formal constraints of the clause relevance structure, and built a human-annotated corpus on Chinese text. Based on the Chinese corpus, we explore the automatic recognition of clause relevance structure. The clause relevance recognition task is formalized as a classification problem and performed by the BERT-based model. A bidirectional LSTM layer is added on the top of the BERT to improve the performance, and the recognition accuracy (90.77%) is achieved by the BERT-LSTM model. Experimental results show that the long distance clause pairs are the main difficulties in the clause relevance recognition, and these difficulties mainly focus on the positive examples, while the clause pairs with short distance are especially difficult to be correctly recognized as negative relevance. [ABSTRACT FROM AUTHOR]

• Agricultural Entity Extraction from raw Textual documents. • A Hybrid Approach for Extraction without labeled Dataset. • Combines Dictionaries, Regular expression-based Rules and Deep learning techniques. • Fine-tuning of transformer-based model and obtain improved performance. Entity extraction is one prerequisite for various Natural Language Processing(NLP) applications like relation extraction, question answering and knowledge graph construction. This paper proposes a complete framework for extracting agricultural entities from unstructured textual documents by combining dictionary-based, rule-based and transformer-based deep learning techniques. Initially, an entity extraction method based on dictionaries is employed to obtain patterns for creating rules that could identify more similar entities. The dictionary has been updated with the newly obtained entities, and this combined dictionary-based and rule-based technique has been repeatedly applied in multiple steps to obtain enough sample data. The labeled dataset is constructed with these extracted entities to train the proposed BERT-based transformer model with Conditional Random Field(CRF) for agricultural entity extraction, and the performance is evaluated. The results show the enhanced performance of the proposed model compared to other state-of-the-art models with a Precision of 88.84 %, Recall of 88.9 % and F1-Score of 88.87 %. [ABSTRACT FROM AUTHOR]

Speech emotion recognition has been developed rapidly in recent decades because of the appearance of machine learning. Nevertheless, lack of corpus remains a significant issue. For actual speech emotion corpus construction, many professional actors are required to perform voices with various emotions in specific scenes. In the process of data labelling, since the number of samples of different emotion categories is extremely imbalanced, it is difficult to efficiently label the samples. Hence, we proposed an integrated active learning sampling strategy and designed an efficient framework for constructing speech emotion corpora in order to address the problems presented above. Comparing experiments with other active learning algorithms on 13 datasets, our method was shown to improve sampling efficiency. In addition, it is able to select small category samples to be labelled with preference in imbalanced datasets. During the actual corpus construction experiments, our method can prioritize selecting small class emotion samples. As even when the amount of labelled data is less than 50%, the accuracy rate still can reach 90%. This greatly enhances the efficiency of constructing the speech emotion corpus and fills in the gaps. [ABSTRACT FROM AUTHOR]

The relationship between biomedical entities is complex, and many of them have not yet been identified. For many biomedical research areas including drug discovery, it is of paramount importance to identify the relationships that have already been established through a comprehensive literature survey. However, manually searching through literature is difficult as the amount of biomedical publications continues to increase. Therefore, the relation classification task, which automatically mines meaningful relations from the literature, is spotlighted in the field of biomedical text mining. By applying relation classification techniques to the accumulated biomedical literature, existing semantic relations between biomedical entities that can help to infer previously unknown relationships are efficiently grasped. To develop semantic relation classification models, which is a type of supervised machine learning, it is essential to construct a training dataset that is manually annotated by biomedical experts with semantic relations among biomedical entities. Any advanced model must be trained on a dataset with reliable quality and meaningful scale to be deployed in the real world and can assist biologists in their research. In addition, as the number of such public datasets increases, the performance of machine learning algorithms can be accurately revealed and compared by using those datasets as a benchmark for model development and improvement. In this paper, we aim to build such a dataset. Along with that, to validate the usability of the dataset as training data for relation classification models and to improve the performance of the relation extraction task, we built a relation classification model based on Bidirectional Encoder Representations from Transformers (BERT) trained on our dataset, applying our newly proposed fine-tuning methodology. In experiments comparing performance among several models based on different deep learning algorithms, our model with the proposed fine-tuning methodology showed the best performance. The experimental results show that the constructed training dataset is an important information resource for the development and evaluation of semantic relation extraction models. Furthermore, relation extraction performance can be improved by integrating our proposed fine-tuning methodology. Therefore, this can lead to the promotion of future text mining research in the biomedical field. [ABSTRACT FROM AUTHOR]

Objective: Faced with the challenges of differential diagnosis caused by the complex clinical manifestations and high pathological heterogeneity of pituitary adenomas, this study aims to construct a high-quality annotated corpus to characterize pituitary adenomas in clinical notes containing rich diagnosis and treatment information. Methods : A dataset from a pituitary adenomas neurosurgery treatment center of a tertiary first-class hospital in China was retrospectively collected. A semi-automatic corpus construction framework was designed. A total of 2000 documents containing 9430 sentences and 524,232 words were annotated, and the text corpus of pituitary adenomas (TCPA) was constructed and analyzed. Its potential application in large language models (LLMs) was explored through fine-tuning and prompting experiments. Results: TCPA had 4782 medical entities and 28,998 tokens, achieving good quality with the inter-annotator agreement value of 0.862-0.986. The LLMs experiments showed that TCPA can be used to automatically identify clinical information from free texts, and introducing instances with clinical characteristics can effectively reduce the need for training data, thereby reducing labor costs. Conclusion: This study characterized pituitary adenomas in clinical notes, and the proposed method were able to serve as references for relevant research in medical natural language scenarios with highly specialized language structure and terminology., Competing Interests: Declaration of conflicting interestsThe author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Artificial Intelligence (AI) has had a significant impact on language education, arousing the related studies from different fields and perspectives. However, the extant review articles only focused on a certain aspect in the field, thus neglecting the multi-disciplinary and multi-perspective features of AI-enhanced language education. The aim of the current study is to bridge this gap by reviewing the 812 articles selected from Web of Science via CiteSpace. The study found that the existing studies were mainly conducted from the perspectives of computer science, linguistics and psychology. Besides, the individualized development of language education supported by technology was not obvious, and the degree of human-computer interaction was weak. Moreover, previous studies focused more on the advantages of AI-enhanced language education than the disadvantages, and the empirical research to analyze specific problems was insufficient. In the future, the research tends to strengthen the cooperation among related disciplines and focus on the individualized development of language education. In addition, empirical research should be strengthened to explore the efficiency of AI-enhanced language education in order to promote the reform of language education., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2024 The Authors.)

Featured Application: This research crawled a bilingual Japanese-Chinese corpus of a certain size through websites. As a necessary resource for Japanese-Chinese neural machine translation (NMT), it is beneficial for researchers to promote the progress of Japanese-Chinese language-related natural language processing research. Specifically, topics include comparative analysis of grammar, comparative studies of Chinese and Japanese languages, compilation of dictionaries, etc. This will have great significance and contribution to the cultural exchange and industrial cooperation between China and Japan. It also has important theoretical significance and application value to the industrialization of Japanese-Chinese machine translation. In addition, the application of this research will be of great significance in strengthening civil communication and enhancing mutual understanding between China and Japan, as the current Chinese and Japanese relations are not well perceived by the citizens of both countries. We hope that the construction and pathways of the Japanese-Chinese bilingual corpus in this research will help to solve the problem of language barriers in Japanese-Chinese people-to-people communication and mutual understanding. We offer the WCC-JC as a free download under the premise that it is intended for research purposes only. 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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Furthermore, it is only by extending the results from the 2PL completion task, which shows parallel use of CA-morpheme and full pronouns, that we could assume that the I -s i -morphemes in the translation task are also CA-morphemes and not clitic pronouns. While we generally follow this interpretation as a CA morpheme, in our subsequent analysis, we will distinguish instances where only a I -st i -morpheme appears from those where both CA morpheme and clitic pronoun (i.e., I -stu i ) occur. With few exceptions, the presence of both I -s i -morpheme and full pronoun allows an interpretation that the I -s i -morpheme is not a mere clitic pronoun but indeed an indication of CA. Instead, the majority of respondents ( I ob i 9; 60%, I wann i 6; 67%) shows CA and a full pronoun in the completion task, but only a CA-morpheme/clitic in the translation task. Keywords: syntax; language production experiments; complementizer agreement; corpus construction EN syntax language production experiments complementizer agreement corpus construction 237 262 26 05/12/22 20220501 NES 220501 1 Introduction Investigation of specific phenomena in corpora of spontaneous speech often meets hurdles, as they may be limited to specific interactive or linguistic contexts or of a generally low frequency (Lenz 2008: 163; [28]: 513). [Extracted from the article]

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]

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.

This paper aims to present a methodological proposal for the analysis of Twitter data from a mixed approach. Specifically, the procedure for collecting and processing information is characterized by the use of qualitative and quantitative resources, as well as the construction of a manageable corpus for subsequent qualitative analysis. The procedure to approach Twitter digital discourses consists of the following route: 1) virtual ethnography registration; 2) data collection through the Twitter API using Python; 3) visualisation and filtering of the data with Open Refine; 4) corpus construction; 5) categorisation and labelling of the iconic verb utterances with Atlas. ti. The paper reconstructs the methodological path carried out in ongoing doctoral research with a qualitative approach, from which the examples are extracted, to offer an accessible route that can be replicated in research with this type of data. [ABSTRACT FROM AUTHOR]

Abstract 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.

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]

In this paper, we propose an Ontology-Style Relation (OSR) annotation approach. In conventional Relation Extraction (RE) datasets, relations are annotated as a link between two entity mentions. In contrast, in our OSR annotation, a relation is annotated as a relation mention (i.e. , not a link but a node) and rdfs:domain and rdfs:range links are annotated from the relation mention to its argument entity mentions. This approach has the following benefits: (1) the relation annotations can be easily converted to Resource Description Framework (RDF) triples to populate an Ontology, (2) some part of conventional RE tasks can be tackled as Named Entity Recognition (NER) tasks, and the relation classes are limited to several RDF properties, and (3) OSR annotations can be used for clear documentations of Ontology contents. We conducted two kinds of evaluation to investigate effects of OSR annotation. We converted (1) an in-house corpus of Japanese Rules of the Road (RoR) in conventional annotations into the OSR annotations and built a novel OSR-RoR corpus and (2) SemEval-2010 Task 8 dataset into the OSR annotations (called OSR-SemEval corpus). We compared the NER and RE performance using neural NER/RE tool DyGIE++ on the conventional and OSR annotations. The experimental results show that the OSR annotations make the RE task easier while introducing slight complexity into the NER task. • The relation annotations can be easily converted to Resource Description Framework (RDF) triples to populate an Ontology. • Some part of conventional RE tasks can be tackled as Named Entity Recognition (NER) tasks. The relation classes are limited to several RDF properties. • OSR annotations can be used for clear documentations of Ontology contents. [ABSTRACT FROM AUTHOR]