Оценка потребности региона в человеческих ресурсах на основе анализа статистических данных и патентных ландшафтов

Переход России на новую технологическую платформу актуализирует проблему кадрового обеспечения по перспективным направлениям квалификации. Структурная трансформация экономики после пандемии ускорила изменения на рынке труда, выявив необходимость разработки новых методов прогнозирования с учетом приоритетов регионального технологического развития. Целью данного исследования является разработка новых подходов, основывающихся на структурированных и неструктурированных базах данных, для определения системы факторов формирования потребности в кадровом обеспечении. Потребности региона в человеческих ресурсах были спрогнозированы с использованием методик интеллектуального анализа данных и патентных ландшафтов. Такое прогнозирование учитывает экономическую направленность региона, а также его географическое положение, программу развития инвестиций и НИОКР, специфику рынка труда. Преимуществом предлагаемой методики является получение обоснованных оценок потребности региона в человеческих ресурсах методами интеллектуального анализа данных и патентных ландшафтов в условиях недостатка официальных статистических данных. База исследования включает более 25 миллионов записей: полнотекстовые коллекции российских и зарубежных патентов, научные работы, статистические показатели и т. д. Анализ ситуации в Вологодской области выявил перспективные направления подготовки, привлекательные для квалифицированных кадров и соответствующие приоритетам регионального технологического развития. В дальнейшем планируется совершенствование методики количественной оценки региональной потребности в специалистах отдельных отраслей. Полученные результаты могут быть использованы государственными органами и исследовательскими центрами при разработке региональных стратегий

Оценка потребности региона в человеческих ресурсах на основе анализа статистических данных и патентных ландшафтов

Переход России на новую технологическую платформу актуализирует проблему кадрового обеспечения по перспективным направлениям квалификации. Структурная трансформация экономики после пандемии ускорила изменения на рынке труда, выявив необходимость разработки новых методов прогнозирования с учетом приоритетов регионального технологического развития. Целью данного исследования является разработка новых подходов, основывающихся на структурированных и неструктурированных базах данных, для определения системы факторов формирования потребности в кадровом обеспечении. Потребности региона в человеческих ресурсах были спрогнозированы с использованием методик интеллектуального анализа данных и патентных ландшафтов. Такое прогнозирование учитывает экономическую направленность региона, а также его географическое положение, программу развития инвестиций и НИОКР, специфику рынка труда. Преимуществом предлагаемой методики является получение обоснованных оценок потребности региона в человеческих ресурсах методами интеллектуального анализа данных и патентных ландшафтов в условиях недостатка официальных статистических данных. База исследования включает более 25 миллионов записей: полнотекстовые коллекции российских и зарубежных патентов, научные работы, статистические показатели и т. д. Анализ ситуации в Вологодской области выявил перспективные направления подготовки, привлекательные для квалифицированных кадров и соответствующие приоритетам регионального технологического развития. В дальнейшем планируется совершенствование методики количественной оценки региональной потребности в специалистах отдельных отраслей. Полученные результаты могут быть использованы государственными органами и исследовательскими центрами при разработке региональных стратегий

LED down the rabbit hole: exploring the potential of global attention for biomedical multi-document summarisation

In this paper we report on our submission to the Multidocument Summarisation for Literature Review (MSLR) shared task. Specifically, we adapt PRIMERA (Xiao et al., 2022) to the biomedical domain by placing global attention on important biomedical entities in several ways. We analyse the outputs of the 23 resulting models, and report patterns in the results related to the presence of additional global attention, number of training steps, and the input configuration.Comment: SDP Workshop at COLING 202

장르 특정적 담화 유형 기반의 온라인 리뷰의 감정분석

학위논문 (석사)-- 서울대학교 대학원 : 언어학과 언어학전공, 2015. 8. 신효필.Though in recent years sentiment analysis has evolved from simple lexicon-based and statistical models to methods involving discourse information, the major problem with the current approaches is that they use the same set of features for sentiment classification of texts of all genres and types (tweets, editorials, discussion board posts, online reviews etc.). Moreover, features that were used by previous researchers reflect only one aspect of discourse, namely, coherence, and they are limited to explicit ways of ensuring coherence, such as conjunctions. To be more specific, these are such features as implicit coherence, realized through adjacency of two sentences, continuity, which shows that two sentences have the same sentiment and is commonly reflected through the use of such conjunctions as and or moreover, and contrast, which is indicated by such conjunctions as but and shows the shift of the opinions polarity. In this study we propose a new set of features which reflects the specific traits of a particular genre ? online reviews: implicit contrast, realized through usage of such limiting expressions as the only drawbackbackground patterns, which are expressions that help to establish a review authors identityand involvement features, which are used to interact with the reader. To show the effectiveness of these features, we annotated a corpus of 120 product reviews and represented each review as a set of non-discourse, generic and genre-specific discourse features extracted from it (together with the target label from the annotation). Such feature sets were used in two series of experiments: fine-grained and coarse grained. At the sentence level we conducted the experiments with and without lexical features, while at the document level we performed 5-, 3- and 2-class classification. Our experiments showed that genre-specific features in general perform better than the generic ones, ensuring greater improvements in precision and recall. If generic features led to minor increases or even deteriorated the performance (as in case of implicit coherence), genre-specific features (especially background) were more stable and allowed us to achieve better recall and precision across all experiments. These tendencies were especially remarkable in the fine-grained classification with lexical features, where adding generic discourse features to the lexical ones deteriorated the results. Moreover, the performance of genre-specific features is not only statistically reliable but also reflects the theoretical properties of online reviews discourse outlined in our study.1. Introduction 1 1.1 Subject Matter 1 1.2 Purposes of the Study 3 1.3 Contributions of the Study 4 1.4 Structure of the Study 5 2. Previous Studies 7 2.1 Previous Studies on Sentiment Analysis of Online Reviews 7 2.2 Previous Studies on Discourse in Sentiment Analysis 9 3. Generic and Genre-specific Discourse Features for Sentiment Analysis 12 3.1 Theoretical Background 12 3.2 Discourse in Rhetorical Structure Theory 15 3.3 Discourse in Sociolinguistics 18 4. Data and Features 20 4.1 Data and Annotation 20 4.1.1 Corpus 20 4.1.2 Annotation Guidelines and Results 21 4.2 Features Used for Experiments 25 4.2.1 Non-discourse Features 25 4.2.1.1 Lexical Features 26 4.2.1.2 Global Polarity Features 27 4.2.2 Generic Discourse Features 28 4.2.2.1 Implicit Coherence 28 4.2.2.2 Continuity 29 4.2.2.3 Explicit Contrast 33 4.2.3 Discourse Features Specific to Online Reviews 36 4.2.3.1 Implicit Contrast 36 4.2.3.2 Background Features 39 4.2.3.3 Involvement Features 44 4.3 Feature Validation 45 5. Predicting Sentence Polarity Using Discourse Features 48 5.1 Experiment Setup 48 5.2 Evaluation of Experiments 50 5.2.1 Measures 50 5.2.2 Results 51 5.2.2.1 Preliminary Classification 51 5.2.2.2 Classification with Lexical Features 52 5.2.2.3 Classification without Lexical Features 56 5.3 Discussion 58 6. Predicting Review Ratings Using Discourse Features 61 6.1 Experiment Setup 61 6.2 Experiment Results 63 6.2.1 5-class Classification 63 6.2.2 Comparison of Results of 2, 3 and 5-class Classification 65 6.3 Discussion 66 7. Conclusion and Future Prospects 68 References 70Maste

Methods for Mid-Term Forecasting of Crop Export and Production

A vast number of studies are devoted to the short-term forecasting of agricultural production and market. However, those results are more helpful for market traders than producers and agricultural policy regulators because any structural change in that field requires a while to be implemented. The mid and long-term predictions (from one year and more) of production and market demand seem more helpful. However, this problem requires considering long-term dependencies between various features. The most natural way of analyzing all those features together is with deep neural networks. The paper presents neural network models for mid-term forecasting of crop production and export, which considers heterogeneous features such as trade flows, production levels, macroeconomic indicators, fuel pricing, and vegetation indexes. They also utilize text-mining to assess changes in the news flow related to the state agricultural policy, sanctions, and the context in the local and international food markets. We collected and combined data from various local and international providers such as UN FAOSTAT, UN Comtrade, social media, the International Monetary Fund for 15 of the world’s top wheat exporters. The experiments show that the proposed models with additive regularization can accurately predict grain export and production levels. We also confirmed that vegetation indexes and fuel prices are crucial for export prediction. Still, the fuel prices seem to be more important for predicting production than the NDVI indexes from past observations

Approaches for forecasting of socioeconomic impacts to the spread of COVID-19 with territorial differences of Russian regions

The COVID-19 pandemic has brought severe demographical, socioeconomic, and territorial impacts. Those challenges require the world community to develop both response measures and anticipation of new threats. Therefore, creating the modern tools to forecast various indicators of the impact intensity pandemic becomes important and relevant for consideration and evaluation of interregional differences. This paper presents deep neural network models to predict a viral pandemic's effects in the regional cluster of Moscow and its neighbors. They are based on recurrent and Transformer-like architectures and utilize the attention mechanism to consider the features of the neighbor regions and dependencies between various indicators. These models are trained on heterogeneous data, including daily cases and deaths, the diseased age structure, transport, and hospital availability of the regions. The experimental evaluation shows that the demographic and healthcare features can significantly improve the accuracy of economic impact prediction. We also revealed that the neighboring regions' data helps predict the outburst's healthcare and economic impact. Namely, that data helps to improve accuracy for both the number of infected and the unemployment rate. The impact forecasting would help to develop strategies to reduce inter-territorial inequality due to the pandemic

The Impacts of Payment Policy on Performance of Human Resource Market System: Agent-Based Modeling and Simulation of Growth-Oriented Firms

The impact of human resource management (HRM) on corporate growth is a crucial research topic, especially for growth-oriented firms. This paper aims to study how different payment policies (such as recruitment and dismissal strategies and payment plans) affect the human resource market system. Based on the HRM characteristics of growth-oriented firms, we develop an agent-based model to simulate the decision-making and interaction behaviors of firms and workers. The system performance is measured by six indicators: the average profit, the profit Gini coefficient, the average output of firms, the average payment, the payment Gini coefficient, and the employment rate of workers. According to the simulation results and statistical analysis, the recruitment plan is the only key factor that significantly impacts all performance indicators other than the employment rate, and companies should pay extra attention to such plans. This study also finds that the changing worker’s payment gap is influenced by industry growth and their abilities, and that the payment cap policy has a positive impact on the development of growth-oriented firms in the startup stage

Multi-label classification for biomedical literature: an overview of the BioCreative VII LitCovid Track for COVID-19 literature topic annotations

International audienceAbstract The coronavirus disease 2019 (COVID-19) pandemic has been severely impacting global society since December 2019. The related findings such as vaccine and drug development have been reported in biomedical literature—at a rate of about 10 000 articles on COVID-19 per month. Such rapid growth significantly challenges manual curation and interpretation. For instance, LitCovid is a literature database of COVID-19-related articles in PubMed, which has accumulated more than 200 000 articles with millions of accesses each month by users worldwide. One primary curation task is to assign up to eight topics (e.g. Diagnosis and Treatment) to the articles in LitCovid. The annotated topics have been widely used for navigating the COVID literature, rapidly locating articles of interest and other downstream studies. However, annotating the topics has been the bottleneck of manual curation. Despite the continuing advances in biomedical text-mining methods, few have been dedicated to topic annotations in COVID-19 literature. To close the gap, we organized the BioCreative LitCovid track to call for a community effort to tackle automated topic annotation for COVID-19 literature. The BioCreative LitCovid dataset—consisting of over 30 000 articles with manually reviewed topics—was created for training and testing. It is one of the largest multi-label classification datasets in biomedical scientific literature. Nineteen teams worldwide participated and made 80 submissions in total. Most teams used hybrid systems based on transformers. The highest performing submissions achieved 0.8875, 0.9181 and 0.9394 for macro-F1-score, micro-F1-score and instance-based F1-score, respectively. Notably, these scores are substantially higher (e.g. 12%, higher for macro F1-score) than the corresponding scores of the state-of-art multi-label classification method. The level of participation and results demonstrate a successful track and help close the gap between dataset curation and method development. The dataset is publicly available via https://ftp.ncbi.nlm.nih.gov/pub/lu/LitCovid/biocreative/ for benchmarking and further development. Database URL https://ftp.ncbi.nlm.nih.gov/pub/lu/LitCovid/biocreative