Прогнозирование состояния фондового рынка на основе финансовых новостей

In this paper are discussed existing methods of Semantic Web technologies application for financial news processing

Визуализация оценки мониторинга состояния биобъектов с помощью метода "Лица Чернова"

The article is about the usage of pictogtaphics of Chernoff faces. The idea behind using faces is that humans easily recognize faces and notice small changes without difficulty. Chernoff faces themselves can be plotted on a standard X-Y graph. The main aim of the article is to find the right way how to treat the person with different diseases. It can be useful for all medical workforce who somehow connected with such a problem. Also the written article can help young programmers and students of medical universities with their scientific papers

Семантический анализ и поиск текстов на естественном языке для Интернет-портала

The article is devoted to solving the set of problems related to natural language texts semantic analysis. The following problems are addressed: automation of generating metadata files describing the semantic representation of a web page; semantic network construction for a given set of texts; semantic search execution for a given set of texts using metadata files; and semantic network export to RDF format. The algorithms for knowledge extraction from text, semantic network construction and query execution on a given semantic network are described. The lexico-syntactic patterns method was used as a basis to approach these problems. A specification for describing lexico-syntactic patterns has been developed and a pattern interpreter based on the morphological dictionary of the Ukrainian language has been created as a part of the software implementation of the method. Experimental studies have been carried out for the «classification of living organisms» subject environment set of patterns. Modified Boyer–Moore–Horspool algorithm was used to address the problem of interpreting.Стаття присвячена розв’язанню комплексу задач з семантичного аналізу текстів природною мовою. Розглянуті такі задачі: автоматизація процесу генерації файлів метаданих, що описують семантичне представлення веб-сторінки; побудова семантичної мережі по заданій множині текстів; виконання семантичного пошуку по заданій множині текстів з використанням файлів метаданих; експорт семантичної мережі в формат RDF. Для розв’язання поставлених задач описані алгоритми відокремлення знань із текстів, представлення їх у вигляді семантичної мережі і виконанні запитів до побудованої мережі. Основним підходом до розв’язання цих задач слугував метод лексико-синтаксичних шаблонів.Для програмної реалізації методу розроблено специфікацію опису лексико-синтаксичних шаблонів, створено інтерпретатор шаблонів на основі морфологічного словнику української мови. Експериментальні дослідження проведені для набор шаблонів предметного середовища «класифікація живих організмів». Для розв’язання задачі інтерпретації лексико-синтаксичних шаблонів використовувався модифікований алгоритм Бойера–Мура–Хорпускула.Статья посвящена решению комплекса задач семантического анализа текстов на естественном языке. Рассмотрены следующие задачи: автоматизация процесса генерации файлов метаданных, описывающих семантическое представление веб-страницы; построение семантической сети по заданному множеству текстов; выполнения семантического поиска по заданному множеству текстов с использованием файлов метаданных; экспорт семантической сети в формат RDF. Для решения поставленных задач описаны алгоритмы выделения знаний из текстов, представление их в виде семантической сети и выполнении запросов к построенной сети. Основным подходом к решению этих задач служил метод лексико-синтаксических шаблонов. Для программной реализации метода разработаны спецификации описания лексико-синтаксических шаблонов, создан интерпретатор шаблонов на основе морфологического словаре украинского языка. Экспериментальные исследования проведены для набор шаблонов предметной среды «классификация живых организмов». Для решения задачи интерпретации лексико-синтаксических шаблонов использовался модифицированный алгоритм Бойера-Мура-Хорпускул

Constructing Cooking Ontology for Live Streams

We build a cooking domain knowledge by using an ontology schema that reflects natural language processing and enhances ontology instances with semantic query. Our research helps audiences to better understand live streaming, especially when they just switch to a show. The practical contribution of our research is to use cooking ontology, so we may map clips of cooking live stream video and instructions of recipes. The architecture of our study presents three sections: ontology construction, ontology enhancement, and mapping cooking video to cooking ontology. Also, our preliminary evaluations consist of three hierarchies—nodes, ordered-pairs, and 3-tuples—that we use to referee (1) ontology enhancement performance for our first experiment evaluation and (2) the accuracy ratio of mapping between video clips and cooking ontology for our second experiment evaluation. Our results indicate that ontology enhancement is effective and heightens accuracy ratios on matching pairs with cooking ontology and video clips

Pattern Learning for Detecting Defect Reports and Improvement Requests in App Reviews

Online reviews are an important source of feedback for understanding customers. In this study, we follow novel approaches that target this absence of actionable insights by classifying reviews as defect reports and requests for improvement. Unlike traditional classification methods based on expert rules, we reduce the manual labour by employing a supervised system that is capable of learning lexico-semantic patterns through genetic programming. Additionally, we experiment with a distantly-supervised SVM that makes use of noisy labels generated by patterns. Using a real-world dataset of app reviews, we show that the automatically learned patterns outperform the manually created ones, to be generated. Also the distantly-supervised SVM models are not far behind the pattern-based solutions, showing the usefulness of this approach when the amount of annotated data is limited.Comment: Accepted for publication in the 25th International Conference on Natural Language & Information Systems (NLDB 2020), DFKI Saarbr\"ucken Germany, June 24-26 202

Continuous Health Interface Event Retrieval

Knowing the state of our health at every moment in time is critical for advances in health science. Using data obtained outside an episodic clinical setting is the first step towards building a continuous health estimation system. In this paper, we explore a system that allows users to combine events and data streams from different sources to retrieve complex biological events, such as cardiovascular volume overload. These complex events, which have been explored in biomedical literature and which we call interface events, have a direct causal impact on relevant biological systems. They are the interface through which the lifestyle events influence our health. We retrieve the interface events from existing events and data streams by encoding domain knowledge using an event operator language.Comment: ACM International Conference on Multimedia Retrieval 2020 (ICMR 2020), held in Dublin, Ireland from June 8-11, 202

Development of an intelligent information resource model based on modern natural language processing methods

Currently, there is an avalanche-like increase in the need for automatic text processing, respectively, new effective methods and tools for processing texts in natural language are emerging. Although these methods, tools and resources are mostly presented on the internet, many of them remain inaccessible to developers, since they are not systematized, distributed in various directories or on separate sites of both humanitarian and technical orientation. All this greatly complicates their search and practical use in conducting research in computational linguistics and developing applied systems for natural text processing. This paper is aimed at solving the need described above. The paper goal is to develop model of an intelligent information resource based on modern methods of natural language processing (IIR NLP). The main goal of IIR NLP is to render convenient valuable access for specialists in the field of computational linguistics. The originality of our proposed approach is that the developed ontology of the subject area “NLP” will be used to systematize all the above knowledge, data, information resources and organize meaningful access to them, and semantic web standards and technology tools will be used as a software basis

Una revisión de la literatura sobre población de ontologías

The main goal of ontologies in computing is related to the definition of a common vocabulary for describing basic concepts and relationships on a specific domain. Main components of ontologies are classes—concepts—, instances, properties, relations, and axioms, among others elements. The ontology population process is intended to receive an ontology as input in order to extract and relate the instances of each ontology class from heterogenous information sources. In this paper we perform a systematic state-of-the-art review about ontology population. We select papers from specialized databases and we create a research question for driving paper search. The results of our review points out ontology population as an interesting topic for researchers. Even though we have several techniques for driving the process, fully automated tools are still missing and we also miss high levels of precision and recall.El principal objetivo de las ontologías en computación es la definición de un vocabulario común para describir conceptos básicos y sus relaciones en un dominio específico. Los principales componentes de las ontologías son clases (conceptos), instancias, propiedades, relaciones y axiomas, entre otros elementos. El proceso de población de ontologías se refiere a la recepción de una ontología como entrada, para luego extraer y relacionar las instancias a cada clase de la ontología desde fuentes de información heterogéneas. En este artículo se realiza una revisión sistemática de literatura sobre la población de ontologías. Se seleccionan artículos de bases de datos especializadas y se crea una pregunta de investigación que permita dirigir la búsqueda de los artículos. Los resultados de la revisión apuntan a que la población de ontologías es un tema de interés para los investigadores. A pesar de que existen muchas técnicas para realizar el proceso, hace falta crear herramientas automáticas y con altos niveles de precision y recall

Analysis and Design of Computational News Angles

A key skill for a journalist is the ability to assess the newsworthiness of an event or situation. To this purpose journalists often rely on news angles, conceptual criteria that are used both i) to assess whether something is newsworthy and also ii) to shape the structure of the resulting news item. As journalism becomes increasingly computer-supported, and more and more sources of potentially newsworthy data become available in real time, it makes sense to try and equip journalistic software tools with operational versions of news angles, so that, when searching this vast data space, these tools can both identify effectively the events most relevant to the target audience, and also link them to appropriate news angles. In this paper we analyse the notion of news angle and, in particular, we i) introduce a formal framework and data schema for representing news angles and related concepts and ii) carry out a preliminary analysis and characterization of a number of commonly used news angles, both in terms of our formal model and also in terms of the computational reasoning capabilities that are needed to apply them effectively to real-world scenarios. This study provides a stepping stone towards our ultimate goal of realizing a solution capable of exploiting a library of news angles to identify potentially newsworthy events in a large journalistic data space

Hybrid fuzzy multi-objective particle swarm optimization for taxonomy extraction

Ontology learning refers to an automatic extraction of ontology to produce the ontology learning layer cake which consists of five kinds of output: terms, concepts, taxonomy relations, non-taxonomy relations and axioms. Term extraction is a prerequisite for all aspects of ontology learning. It is the automatic mining of complete terms from the input document. Another important part of ontology is taxonomy, or the hierarchy of concepts. It presents a tree view of the ontology and shows the inheritance between subconcepts and superconcepts. In this research, two methods were proposed for improving the performance of the extraction result. The first method uses particle swarm optimization in order to optimize the weights of features. The advantage of particle swarm optimization is that it can calculate and adjust the weight of each feature according to the appropriate value, and here it is used to improve the performance of term and taxonomy extraction. The second method uses a hybrid technique that uses multi-objective particle swarm optimization and fuzzy systems that ensures that the membership functions and fuzzy system rule sets are optimized. The advantage of using a fuzzy system is that the imprecise and uncertain values of feature weights can be tolerated during the extraction process. This method is used to improve the performance of taxonomy extraction. In the term extraction experiment, five extracted features were used for each term from the document. These features were represented by feature vectors consisting of domain relevance, domain consensus, term cohesion, first occurrence and length of noun phrase. For taxonomy extraction, matching Hearst lexico-syntactic patterns in documents and the web, and hypernym information form WordNet were used as the features that represent each pair of terms from the texts. These two proposed methods are evaluated using a dataset that contains documents about tourism. For term extraction, the proposed method is compared with benchmark algorithms such as Term Frequency Inverse Document Frequency, Weirdness, Glossary Extraction and Term Extractor, using the precision performance evaluation measurement. For taxonomy extraction, the proposed methods are compared with benchmark methods of Feature-based and weighting by Support Vector Machine using the f-measure, precision and recall performance evaluation measurements. For the first method, the experiment results concluded that implementing particle swarm optimization in order to optimize the feature weights in terms and taxonomy extraction leads to improved accuracy of extraction result compared to the benchmark algorithms. For the second method, the results concluded that the hybrid technique that uses multi-objective particle swarm optimization and fuzzy systems leads to improved performance of taxonomy extraction results when compared to the benchmark methods, while adjusting the fuzzy membership function and keeping the number of fuzzy rules to a minimum number with a high degree of accuracy