Size Of Corpus Research Articles

A Hybrid Intention Recognition Framework with Semantic Inference for Financial Customer Service

Automatic intention recognition in financial service scenarios faces challenges such as limited corpus size, high colloquialism, and ambiguous intentions. This paper proposes a hybrid intention recognition framework for financial customer service, which involves semi-supervised learning data augmentation, label semantic inference, and text classification. A semi-supervised learning method is designed to augment the limited corpus data obtained from the Chinese financial service scenario, which combines back-translation with BERT models. Then, a K-means-based semantic inference method is introduced to extract label semantic information from categorized corpus data, serving as constraints for subsequent text classification. Finally, a BERT-based text classification network is designed to recognize the intentions in financial customer service, involving a multi-level feature fusion for corpus information and label semantic information. During the multi-level feature fusion, a shallow-to-deep (StD) mechanism is designed to alleviate feature collapse. To validate our hybrid framework, 2977 corpus texts about loan service are provided by a financial company in China. Experimental results demonstrate that our hybrid framework outperforms existing deep learning methods in financial customer service intention recognition, achieving an accuracy of 89.06%, precision of 90.27%, recall of 90.40%, and an F1 score of 90.07%. This study demonstrates the potential of the hybrid framework to automatic intention recognition in financial customer service, which is beneficial for the improvement of the financial service quality.

Advancing Arabic Word Embeddings: A Multi-Corpora Approach with Optimized Hyperparameters and Custom Evaluation

The expanding Arabic user base presents a unique opportunity for researchers to tap into vast online Arabic resources. However, the lack of reliable Arabic word embedding models and the limited availability of Arabic corpora poses significant challenges. This paper addresses these gaps by developing and evaluating Arabic word embedding models trained on diverse Arabic corpora, investigating how varying hyperparameter values impact model performance across different NLP tasks. To train our models, we collected data from three distinct sources: Wikipedia, newspapers, and 32 Arabic books, each selected to capture specific linguistic and contextual features of Arabic. By using advanced techniques such as Word2Vec and FastText, we experimented with different hyperparameter configurations, such as vector size, window size, and training algorithms (CBOW and skip-gram), to analyze their impact on model quality. Our models were evaluated using a range of NLP tasks, including sentiment analysis, similarity tests, and an adapted analogy test designed specifically for Arabic. The findings revealed that both the corpus size and hyperparameter settings had notable effects on performance. For instance, in the analogy test, a larger vocabulary size significantly improved outcomes, with the FastText skip-gram models excelling in accurately solving analogy questions. For sentiment analysis, vocabulary size was critical, while in similarity scoring, the FastText models achieved the highest scores, particularly with smaller window and vector sizes. Overall, our models demonstrated strong performance, achieving 99% and 90% accuracies in sentiment analysis and the analogy test, respectively, along with a similarity score of 8 out of 10. These results underscore the value of our models as a robust tool for Arabic NLP research, addressing a pressing need for high-quality Arabic word embeddings.

From Evictions to Shame: Exploring Hysterectomy Through Metaphor

The present study analyzes metaphorical expressions related to hysterectomy in a diverse corpus comprising scientific texts, Reddit forums, and patient-authored articles and blogs. The research aims to understand how hysterectomy is discursively constructed and experienced by women. The corpus reveals a range of metaphors from clinical descriptions to personal and creative expressions. The analysis shows that metaphors not only reflect but also influence women's perceptions of their bodies and the procedure. The study identifies metaphors including the uterus as a 'tenant' or 'kidnapper,' and the procedure as an 'eviction' or a 'robbery', highlighting the emotional and psychological aspects of hysterectomy. Limitations include the corpus size, lack of metadata to establish more correlations, and its Western-centric perspective, suggesting the need for more inclusive research. The study contributes to the discourse on women's health, emphasizing the role of language in shaping medical experiences. It underscores the need for understanding metaphorical language to improve communication and support for women undergoing hysterectomy.

Cross-lingual Natural Language Processing on Limited Annotated Case/Radiology Reports in English and Japanese: Insights from the Real-MedNLP Workshop.

Textual datasets (corpora) are crucial for the application of natural language processing (NLP) models. However, corpus creation in the medical field is challenging, primarily because of privacy issues with raw clinical data such as health records. Thus, the existing clinical corpora are generally small and scarce. Medical NLP (MedNLP) methodologies perform well with limited data availability. We present the outcomes of the Real-MedNLP workshop, which was conducted using limited and parallel medical corpora. Real-MedNLP exhibits three distinct characteristics: (1) limited annotated documents: the training data comprise only a small set (∼100) of case reports (CRs) and radiology reports (RRs) that have been annotated. (2) Bilingually parallel: the constructed corpora are parallel in Japanese and English. (3) Practical tasks: the workshop addresses fundamental tasks, such as named entity recognition (NER) and applied practical tasks. We propose three tasks: NER of ∼100 available documents (Task 1), NER based only on annotation guidelines for humans (Task 2), and clinical applications (Task 3) consisting of adverse drug effect (ADE) detection for CRs and identical case identification (CI) for RRs. Nine teams participated in this study. The best systems achieved 0.65 and 0.89 F1-scores for CRs and RRs in Task 1, whereas the top scores in Task 2 decreased by 50 to 70%. In Task 3, ADE reports were detected by up to 0.64 F1-score, and CI scored up to 0.96 binary accuracy. Most systems adopt medical-domain-specific pretrained language models using data augmentation methods. Despite the challenge of limited corpus size in Tasks 1 and 2, recent approaches are promising because the partial match scores reached ∼0.8-0.9 F1-scores. Task 3 applications revealed that the different availabilities of external language resources affected the performance per language.

The Semantic Function of Modern Chinese “Negation + X” Modal Words Based on Communication Technology and Big Data Corpus

The semantic function of modern Chinese “negation + X” modal words based on communication technology and big data corpus has gained wide attention. As the basis of SOA architecture, Web services provide the key resources for worldwide information transfer and information sharing with their characteristics of loose coupling, platform independence, and data exchange without additional support from third-party hardware and software. However, along with the popularization and improvement of Web service technology, the number and types of Web services in the Internet are also increasing massively, and there are a large number of Web services with various functions, quality and granularity. Therefore, how to quickly and accurately discover Web services that satisfy users’ query requests from a large and complex set of services has become a critical problem to be solved in the current Web service discovery research. Based on the real corpus, this paper analyzes the similarities and differences in the semantic functions of modern Chinese “negation + X” modal words by combining lexicalization and grammatization, cognitive linguistics, systemic functional grammar and other related theories. The experimental results demonstrate that the model is designed for automatic annotation of semantic word classes, and the annotation algorithm based on the hidden horse model, combined with the Viterbi algorithm based on dynamic programming, achieves a correct rate of 94.3% in the closed test and 89.1% in the open test despite the small size of the training corpus and severe data sparsity, and the model fitting effect meets the dynamic expectations.

Masketeer: An Ensemble-Based Pseudonymization Tool with Entity Recognition for German Unstructured Medical Free Text

Background: The recent rise of large language models has triggered renewed interest in medical free text data, which holds critical information about patients and diseases. However, medical free text is also highly sensitive. Therefore, de-identification is typically required but is complicated since medical free text is mostly unstructured. With the Masketeer algorithm, we present an effective tool to de-identify German medical text. Methods: We used an ensemble of different masking classes to remove references to identifiable data from over 35,000 clinical notes in accordance with the HIPAA Safe Harbor Guidelines. To retain additional context for readers, we implemented an entity recognition scheme and corpus-wide pseudonymization. Results: The algorithm performed with a sensitivity of 0.943 and specificity of 0.933. Further performance analyses showed linear runtime complexity (O(n)) with both increasing text length and corpus size. Conclusions: In the future, large language models will likely be able to de-identify medical free text more effectively and thoroughly than handcrafted rules. However, such gold-standard de-identification tools based on large language models are yet to emerge. In the current absence of such, we hope to provide best practices for a robust rule-based algorithm designed with expert domain knowledge.

Facilitating topic modeling in tourism research:Comprehensive comparison of new AI technologies

Facilitating topic modeling in tourism research:Comprehensive comparison of new AI technologies

COLOUR TERMS IN ASTROPHYSICAL TEXTS: A CORPUS STUDY

The aim of this paper is to present colour terms used by scientists in the field of astrophysics. We take into consideration both basic and non-basic colour terms – as understood by Berlin and Kay (1969). The research material, compiled in a corpus with AntConc, consists of abstracts published in selected astrophysical journals. The corpus size is 2,319,787 words. The most frequent basic colour term found in the abstracts is black (612 occurrences), followed by white (167), red (160), blue (111) and green (98). It appears that the frequent word combinations in which these and other colour words are used happen to be specialized terms whose formation involved metaphorical or metonymic processes, e.g. red giant, white dwarf, red planet, red noise and blue straggler.

Quantifying Nature in Tourism Discourse: A Corpus-ecolinguistic Perspective

Nature or the physical environment is strategically presented as a compelling attraction to persuade tourists to visit a country, including Indonesia. While previous studies investigated tourism promotion from persuasive devices like metaphors and adjectives, this study takes a different trajectory by investigating the language of attitudes, identified explicitly from the Graduation, in tourism promotion discourse. This study built a specific corpus compiled from articles promoting nature on the official website of Indonesian tourism (OWIT). Using Sketch Engine and Wmatrix5, this study aims to identify 1) the types of nature offered on the Indonesian tourism promotion website, 2) quantification and its grammatical realization used in the promotion, and 3) attitudes amplified from the graduation strategy. Findings reveal that Indonesian tourism is promoted mainly by its topography, flora and fauna, nature-based activity, and weather and climate. Graduation is realized through diverse grammatical realizations, such as the specific and non-specific numerals and infused amounts, in order to quantify nature’s values/ numbers, extent, and mass/ presence. The concordance study indicates that upscaling quantification emphasizes Indonesia’s natural beauty and richness, while downscaling quantification promotes the endangered species in Indonesia’s national parks. From an ecological lens, the Graduation is employed to amplify readers’ Appreciation of the country’s valuation and composition. Quantification has also been proven to be a productive persuasive device in tourism promotion that functions to materialize and commodify nature. Notwithstanding the corpus size, this study contributes to tourism promotion discourse and ecolinguistics studies.

AraFast: Developing and Evaluating a Comprehensive Modern Standard Arabic Corpus for Enhanced Natural Language Processing

The research presented in the following paper focuses on the effectiveness of a modern standard Arabic corpus, AraFast, in training transformer models for natural language processing tasks, particularly in Arabic. In the study described herein, four experiments were conducted to evaluate the use of AraFast across different configurations: segmented, unsegmented, and mini versions. The main outcomes of the present study are as follows: Transformer models trained with larger and cleaner versions of AraFast, especially in question-answering, indicate the impact of corpus quality and size on model efficacy. Secondly, a dramatic reduction in training loss was observed with the mini version of AraFast, underscoring the importance of optimizing corpus size for effective training. Moreover, the segmented text format led to a decrease in training loss, highlighting segmentation as a beneficial strategy in Arabic NLP. In addition, using the study findings, challenges in managing noisy data derived from web sources are identified, which were found to significantly hinder model performance. These findings collectively demonstrate the critical role of well-prepared, segmented, and clean corpora in advancing Arabic NLP capabilities. The insights from AraFast’s application can guide the development of more efficient NLP models and suggest directions for future research in enhancing Arabic language processing tools.

Applying short text topic models to instant messaging communication of software developers

Applying short text topic models to instant messaging communication of software developers

Oversampling effect in pretraining for bidirectional encoder representations from transformers (BERT) to localize medical BERT and enhance biomedical BERT

Pretraining large-scale neural language models on raw texts has made a significant contribution to improving transfer learning in natural language processing. With the introduction of transformer-based language models, such as bidirectional encoder representations from transformers (BERT), the performance of information extraction from free text has improved significantly in both the general and medical domains. However, it is difficult to train specific BERT models to perform well in domains for which few databases of a high quality and large size are publicly available. We hypothesized that this problem could be addressed by oversampling a domain-specific corpus and using it for pretraining with a larger corpus in a balanced manner. In the present study, we verified our hypothesis by developing pretraining models using our method and evaluating their performance. Our proposed method was based on the simultaneous pretraining of models with knowledge from distinct domains after oversampling. We conducted three experiments in which we generated (1) English biomedical BERT from a small biomedical corpus, (2) Japanese medical BERT from a small medical corpus, and (3) enhanced biomedical BERT pretrained with complete PubMed abstracts in a balanced manner. We then compared their performance with those of conventional models. Our English BERT pretrained using both general and small medical domain corpora performed sufficiently well for practical use on the biomedical language understanding evaluation (BLUE) benchmark. Moreover, our proposed method was more effective than the conventional methods for each biomedical corpus of the same corpus size in the general domain. Our Japanese medical BERT outperformed the other BERT models built using a conventional method for almost all the medical tasks. The model demonstrated the same trend as that of the first experiment in English. Further, our enhanced biomedical BERT model, which was not pretrained on clinical notes, achieved superior clinical and biomedical scores on the BLUE benchmark with an increase of 0.3 points in the clinical score and 0.5 points in the biomedical score. These scores were above those of the models trained without our proposed method. Well-balanced pretraining using oversampling instances derived from a corpus appropriate for the target task allowed us to construct a high-performance BERT model.

ПОКРАЩЕННЯ ДЕТЕРМІНОВАНОГО МЕТОДУ ГЕНЕРУВАННЯ КОРПУСІВ ТЕКСТОВИХ ДАНИХ

This paper is devoted to the issue of generating corpora of text data for their use in solving software engineering problems in the context of developing information systems for natural language processing (NLP). One of the methods intended for this is the basic CorDeGen method, however, the analysis revealed certain disadvantages. Such a disadvantage is that NLP methods at the pre-processing stage can remove part of the terms generated by this method from the texts, treating them as stop words of a certain language. Removing part of the terms leads to the fact that the distribution of terms between the texts predicted by the CorDeGen method is distorted and the obtained result of processing the corpus with a certain NLP method is significantly different from the expected one. To solve this disadvantage, a new modified CorDeGen+ method is proposed in the paper, which introduces an additional, language-dependent stage of checking each generated term for admissibility, and if necessary, replacing it with another one. At the same time, all the advantages of the basic CorDeGen method are preserved by the proposed method, as well as other possible disadvantages, except for the corrected one. The paper examines language variations of the proposed method for the four most common European languages and a variation for languages that use non-Latin letters. The conducted experimental test showed the effectiveness of the CorDeGen+ method in terms of correcting the described disadvantage of the basic CorDeGen method. Also, this test showed that the degree of slowdown of the corpora generation process due to the introduction of an additional stage depends on the corpus size. In the case of micro-corpora (100 unique terms), the degree of slowdown reaches 39%, but as the size of the corpus increases, the degree drops sharply, and for super-large corpora (312500 unique terms) it reaches a maximum of 6.8%.

Mediatization of the Ecological Security in the English Media: Corpus-Assisted Approach

Introduction. The relevance of the work is due to the interest of linguistics in the issues of discursive mediatization of socially significant phenomena in the English-language media, in particular environmental safety as the most important value of modern society. The scientific novelty of the study lies in establishing the content of the value “environmental safety” on the basis of systematization and quantification of semantic fields that are correlated with the main topics that determine the interpretation of this socially significant phenomenon in the British media and form a discourse-based thesaurus of its representation. Methodology and sources. The paper used corpus linguistics methods and the interpretative method of discourse analysis. The empirical basis of the study was a corpus of media texts of high-quality and popular media devoted to environmental safety issues, the corpus size was more than 200 thousand words. Results and discussion. Based on corpus analysis, semantic fields were identified that represent the concept under study in the high-quality and popular British press. It is shown that in the media narratives of the British media, ES is closely associated with climate change and global warming. It was revealed that the dominant topics in narratives about environmental security in both types of media are: 1) the environment; 2) science; 3) politics. A comparative analysis of the semantic fields showed the predominance of the semantic field “science” in high-quality media, which indicates rational and logical strategies for influencing a wide readership. In the texts of popular media, the semantic field “politics” dominates, which indicates the politicization of the discourse of environmental safety and indicates a strategy of emotional impact on the addressee. Both corpora emphasize the issues of climate change, related to unfavourable weather conditions and extremely high temperatures, however, high-quality press texts mention temperature anomalies much more often, which indicates an attempt to arouse public concern about the existing threat and encourage the adoption of necessary measures. Conclusion. The proposed methodology can be used in analyzing the specifics of other media discursive practices in terms of their discursive-textual organization, in studying the rhetorical, pragmatic and linguocultural organization of environmental safety discourse in sociocultural and ideological perspectives.

Comparing semantic representation methods for keyword analysis in bibliometric research

Comparing semantic representation methods for keyword analysis in bibliometric research

Automatic categorization of self-acknowledged limitations in randomized controlled trial publications

Acknowledging study limitations in a scientific publication is a crucial element in scientific transparency and progress. However, limitation reporting is often inadequate. Natural language processing (NLP) methods could support automated reporting checks, improving research transparency. In this study, our objective was to develop a dataset and NLP methods to detect and categorize self-acknowledged limitations (e.g., sample size, blinding) reported in randomized controlled trial (RCT) publications. We created a data model of limitation types in RCT studies and annotated a corpus of 200 full-text RCT publications using this data model. We fine-tuned BERT-based sentence classification models to recognize the limitation sentences and their types. To address the small size of the annotated corpus, we experimented with data augmentation approaches, including Easy Data Augmentation (EDA) and Prompt-Based Data Augmentation (PromDA). We applied the best-performing model to a set of about 12K RCT publications to characterize self-acknowledged limitations at larger scale. Our data model consists of 15 categories and 24 sub-categories (e.g., Population and its sub-category DiagnosticCriteria). We annotated 1090 instances of limitation types in 952 sentences (4.8 limitation sentences and 5.5 limitation types per article). A fine-tuned PubMedBERT model for limitation sentence classification improved upon our earlier model by about 1.5 absolute percentage points in F1 score (0.821 vs. 0.8) with statistical significance (p<.001). Our best-performing limitation type classification model, PubMedBERT fine-tuning with PromDA (Output View), achieved an F1 score of 0.7, improving upon the vanilla PubMedBERT model by 2.7 percentage points, with statistical significance (p<.001). The model could support automated screening tools which can be used by journals to draw the authors' attention to reporting issues. Automatic extraction of limitations from RCT publications could benefit peer review and evidence synthesis, and support advanced methods to search and aggregate the evidence from the clinical trial literature.

Extraction and attribution of public figures statements for journalism in Indonesia using deep learning

Extraction and attribution of public figures statements for journalism in Indonesia using deep learning

Non-Fluent Synthetic Target-Language Data Improve Neural Machine Translation.

When the amount of parallel sentences available to train a neural machine translation is scarce, a common practice is to generate new synthetic training samples from them. A number of approaches have been proposed to produce synthetic parallel sentences that are similar to those in the parallel data available. These approaches work under the assumption that non-fluent target-side synthetic training samples can be harmful and may deteriorate translation performance. Even so, in this paper we demonstrate that synthetic training samples with non-fluent target sentences can improve translation performance if they are used in a multilingual machine translation framework as if they were sentences in another language. We conducted experiments on ten low-resource and four high-resource translation tasks and found out that this simple approach consistently improves translation performance as compared to state-of-the-art methods for generating synthetic training samples similar to those found in corpora. Furthermore, this improvement is independent of the size of the original training corpus, the resulting systems are much more robust against domain shift and produce less hallucinations.

A new word embedding model integrated with medical knowledge for deep learning-based sentiment classification

The development of intelligent systems that use social media data for decision-making processes in numerous domains such as politics, business, marketing, and finance, has been made possible by the popularity of social media platforms. However, the utilization of textual data from social media in the healthcare management industry is still somewhat limited when it is compared to other industries. Investigating how current machine learning and natural language processing technologies can be used in the healthcare industry to gauge public sentiment is an important study. Earlier works on healthcare sentiment analysis have utilized traditional word embedding models trained on the general and medical corpus. However, integration of medical knowledge to pre-trained word embedding models has not been considered yet. Word embedding models trained on the general corpus led to the problem of lacking medical knowledge and the models trained on the small size of the medical corpus have limitations in capturing semantic and syntactic properties. This research proposes a new word embedding model named Word Embedding Integrated with Medical Knowledge Vector (WE-iMKVec). The proposed model integrates sentiment lexicons and medical knowledgebases into the pre-trained word embedding to enrich the properties of word embedding. A new medical-aware sentiment polarity score is proposed for the utilization in learning neural-network sentiment and these vectors incorporate with the original pre-trained word vectors. The resulting vectors are enriched with lexicon vectors and the medical knowledge vectors: Adverse Drug Reaction (ADR) vector and Unified Medical Language System (UMLS) vector are used to build the proposed WE-iMKVec model. WE-iMKVec is validated on the five different social media healthcare review datasets and the empirical results showed its superiority over traditional word embedding models in medical sentiment analysis. The highest improvement can be found in the patients.info medical condition dataset where the proposed model outperforms three conventional word2vec models (Google-News, PubMed-PMC, and Drug Reviews) by 12.7%, 31.4%, and 25.4% respectively in terms of F1 score.

Corpus-linguistic approaches to lexical statutory meaning: Extensionalist vs. intensionalist approaches

Corpus-linguistic approaches to lexical statutory meaning: Extensionalist vs. intensionalist approaches