THE DETECTION OF FRAUDULENT FINANCIAL STATEMENTS: AN INTEGRATED LANGUAGE MODEL

Among the growing number of Chinese companies that went public overseas, many have been detected and alleged as conducting financial fraud by market research firms or U.S. Securities and Exchange Commission (SEC). Then investors lost money and even confidence to all overseas-listed Chinese companies. Likewise, these companies suffered serious stock sank or were even delisted from the stock exchange. Conventional auditing practices failed in these cases when misleading financial reports presented. This is partly because existing auditing practices and academic researches primarily focus on statistical analysis of structured financial ratios and market activity data in auditing process, while ignoring large amount of textual information about those companies in financial statements. In this paper, we build integrated language model, which combines statistical language model (SLM) and latent semantic analysis (LSA), to detect the strategic use of deceptive language in financial statements. By integrating SLM with LSA framework, the integrated model not only overcomes SLM’s inability to capture long-span information, but also extracts the semantic patterns which distinguish fraudulent financial statements from non-fraudulent ones. Four different modes of the integrated model are also studied and compared. With application to assess fraud risk in overseas-listed Chinese companies, the integrated model shows high accuracy to flag fraudulent financial statements

SSentiaA: A Self-Supervised Sentiment Analyzer for Classification From Unlabeled Data

In recent years, supervised machine learning (ML) methods have realized remarkable performance gains for sentiment classification utilizing labeled data. However, labeled data are usually expensive to obtain, thus, not always achievable. When annotated data are unavailable, the unsupervised tools are exercised, which still lag behind the performance of supervised ML methods by a large margin. Therefore, in this work, we focus on improving the performance of sentiment classification from unlabeled data. We present a self-supervised hybrid methodology SSentiA (Self-supervised Sentiment Analyzer) that couples an ML classifier with a lexicon-based method for sentiment classification from unlabeled data. We first introduce LRSentiA (Lexical Rule-based Sentiment Analyzer), a lexicon-based method to predict the semantic orientation of a review along with the confidence score of prediction. Utilizing the confidence scores of LRSentiA, we generate highly accurate pseudo-labels for SSentiA that incorporates a supervised ML algorithm to improve the performance of sentiment classification for less polarized and complex reviews. We compare the performances of LRSentiA and SSSentA with the existing unsupervised, lexicon-based and self-supervised methods in multiple datasets. The LRSentiA performs similarly to the existing lexicon-based methods in both binary and 3-class sentiment analysis. By combining LRSentiA with an ML classifier, the hybrid approach SSentiA attains 10%–30% improvements in macro F1 score for both binary and 3-class sentiment analysis. The results suggest that in domains where annotated data are unavailable, SSentiA can significantly improve the performance of sentiment classification. Moreover, we demonstrate that using 30%–60% annotated training data, SSentiA delivers similar performances of the fully labeled training dataset

Predicting dental implant failures by integrating multiple classifiers

El campo de la ciencia de datos ha tenido muchos avances respecto a la aplicación y desarrollo de técnicas en el sector de la salud. Estos avances se ven reflejados en la predicción de enfermedades, clasificación de imágenes, identificación y reducción de riesgos, así como muchos otros. Este trabajo tiene por objetivo investigar el beneficio de la utilización de múltiples algoritmos de clasificación, para la predicción de fracasos en implantes dentales de la provincia de Misiones, Argentina y proponer un procedimiento validado por expertos humanos. El modelo abarca la combinación de los clasificadores: Random Forest, C-Support Vector, K-Nearest Neighbors, Multinomial Naive Bayes y Multi-layer Perceptron. La integración de los modelos se realiza con el weighted soft voting method. La experimentación es realizada con cuatro conjuntos de datos, un conjunto de implantes dentales confeccionado para el estudio de caso, un conjunto generado artificialmente y otros dos conjuntos obtenidos de distintos repositorios de datos. Los resultados arrojados del enfoque propuesto sobre el conjunto de datos de implantes dentales, es validado con el desempeño en la clasificación por expertos humanos. Nuestro enfoque logra un porcentaje de acierto del 93% de casos correctamente identificados, mientras que los expertos humanos consiguen un 87% de precisión.The field of data science has made many advances in the application and development of techniques in several aspects of the health sector, such as in disease prediction, image classification, risk identification and risk reduction. Based on this, the objectives of this work were to investigate the benefit of using multiple classification algorithms to predict dental implant failures in patients from Misiones province, Argentina, and to propose a procedure validated by human experts. The model used the integration of several types of classifiers.The experimentation was performed with four data sets: a data set of dental implants made for the case study, an artificially generated data set, and two other data sets obtained from different data repositories. The results of the approach proposed were validated by the performance in classification made by human experts. Our approach achieved a success rate of 93% of correctly identified cases, whereas human experts achieved 87% accuracy. Based on this, we can argue that multi-classifier systems are a good approach to predict dental implant failures.Fil: Ganz, Nancy Beatriz. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Nordeste. Instituto de Materiales de Misiones. Universidad Nacional de Misiones. Facultad de Ciencias Exactas Químicas y Naturales. Instituto de Materiales de Misiones; ArgentinaFil: Ares, Alicia Esther. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Nordeste. Instituto de Materiales de Misiones. Universidad Nacional de Misiones. Facultad de Ciencias Exactas Químicas y Naturales. Instituto de Materiales de Misiones; ArgentinaFil: Kuna, Horacio Daniel. Universidad Nacional de Misiones. Facultad de Cs.exactas Quimicas y Naturales. Instituto de Investigacion Desarrollo E Innovacion En Informatica.; Argentin

On nonprimary selectional restrictions

This paper argues for non-primary c- and s-selectional restrictions of verbs in computing nonprimary predicatives such as resultatives, depictives, and manners. Our discussion is based both on the selection violations in the presence of nonprimary predicates and on the cross-linguistic and language-internal variations of categorial and semantic constraints on nonprimary predicates. We claim that all types of thematic predication are represented by an extended projection, and that the merger of lexical heads with another element, regardless of the type of the element, consistently has c- and s-selectional restrictions

Manipulating texture and cohesion in academic writing: A keystroke logging study

Research has repeatedly shown that problems arise when students are asked to link information co-textually and contextually across larger phases of discourse. Within Systemic Functional Linguistics (SFL), a text-oriented theory of language, co-textual and contextual links are analyzed and operationalized in terms of textual and logical metafunctions, both of which work together to connect and enable experiential and interpersonal metafunctions. While most writing studies to date have investigated text as product (synoptic approach), there has been increasing interest in studying text as an evolving process (dynamic approach). The current study contributes to this emerging research by examining the real-time choices made by six student writers. Drawing on keystroke logging software (Inputlog), it explores writers’ revision choices within the systems of theme, information, and identification, in conjunction with the logical metafunction. Results indicate that complex choices contribute to unfolding cohesiveness and information flow, where choices in specificity and congruency are key contributors to managing texture while also manipulating complexity and context-dependency. Overall findings suggest that students may benefit from an explicit focus on the nominal group as a means to create and maintain texture and cohesion through over-specification, classification (pre-modifiers), and qualification (post-modifiers)