Semantic Classification of Adverbial Phraseological Units of the Russian Language

In the article, the author makes an attempt to semantic classification of adverbial phraseological units of the Russian language by applying the method of component analysis.

No Perfect Outdoors: Towards a Deep Profiling of GNSS-Based Location Contexts

While both outdoor and indoor localization methods are flourishing, how to properly marry them to offer pervasive localizability in urban areas remains open. Recently, proposals on indoor–outdoor detection have made the first step towards such an integration, yet complicated urban environments render such a binary classification incompetent. Fortunately, the latest developments in Android have granted us access to raw GNSS measurements, which contain far more information than commonly derived GPS location indicators. In this paper, we explore these newly available measurements in order to better characterize diversified urban environments. Essentially, we tackle the challenges introduced by the complex GNSS data and apply a deep learning model to identify representations for respective location contexts. We further develop two preliminary applications of our deep profiling: one, we offer a more fine-grained semantic classification than binary indoor–outdoor detection; and two, we derive a GPS error indicator that is more meaningful than that provided by Google Maps. These results are all corroborated by our extensive data collection and trace-driven evaluations.

Language-switch costs from comprehension to production might just be task-switch costs

Spanish–English bilinguals switched between naming pictures in one language and either reading-aloud or semantically classifying written words in both languages. When switching between reading-aloud and picture-naming, bilinguals exhibited no language switch costs in picture-naming even though they produced overt language switches in speech. However, when switching between semantic classification and picture-naming, bilinguals, especially unbalanced bilinguals, exhibited switch costs in the dominant language and switch facilitation in the nondominant language even though they never switched languages overtly. These results reveal language switching across comprehension and production can be cost-free when the intention remains the same. Assuming switch costs at least partially reflect inhibition of the nontarget language, this implies such language control mechanisms are recruited only under demanding task conditions, especially for unbalanced bilinguals. These results provide striking demonstration of adaptive control mechanisms and call into question previous claims that language switch costs necessarily transfer from comprehension to production.

ChemTables: a dataset for semantic classification on tables in chemical patents

Chemical patents are a commonly used channel for disclosing novel compounds and reactions, and hence represent important resources for chemical and pharmaceutical research. Key chemical data in patents is often presented in tables. Both the number and the size of tables can be very large in patent documents. In addition, various types of information can be presented in tables in patents, including spectroscopic and physical data, or pharmacological use and effects of chemicals. Since images of Markush structures and merged cells are commonly used in these tables, their structure also shows substantial variation. This heterogeneity in content and structure of tables in chemical patents makes relevant information difficult to find. We therefore propose a new text mining task of automatically categorising tables in chemical patents based on their contents. Categorisation of tables based on the nature of their content can help to identify tables containing key information, improving the accessibility of information in patents that is highly relevant for new inventions. For developing and evaluating methods for the table classification task, we developed a new dataset, called ChemTables, which consists of 788 chemical patent tables with labels of their content type. We introduce this data set in detail. We further establish strong baselines for the table classification task in chemical patents by applying state-of-the-art neural network models developed for natural language processing, including TabNet, ResNet and Table-BERT on ChemTables. The best performing model, Table-BERT, achieves a performance of 88.66 micro-averaged $$F_1$$ F 1 score on the table classification task. The ChemTables dataset is publicly available at https://doi.org/10.17632/g7tjh7tbrj.3, subject to the CC BY NC 3.0 license. Code/models evaluated in this work are in a Github repository https://github.com/zenanz/ChemTables.

Linking the Dictionary of Old Dutch to A Thesaurus of Old English

At the Instituut voor de Nederlandse Taal (Dutch Language Institute), DiaMaNT, a diachronic semantic computational lexicon of Dutch, is being developed, based on the scholarly historical dictionaries of Dutch. The main purpose of this lexicon is to enhance text accessibility and foster research in the development of concepts. This article explores the feasibility of enriching DiaMaNT with an existing semantic classification by linking a subset of the vocabulary of the Dictionary of Old Dutch to A Thesaurus of Old English.

A Survey of Semantic Construction and Application of Satellite Remote Sensing Images and Data

With the rapid development of satellite technology, remote sensing data has entered the era of big data, and the intelligent processing of remote sensing image has been paid more and more attention. Through the semantic research of remote sensing data, the processing ability of remote sensing data is greatly improved. This paper aims to introduce and analyze the research and application progress of remote sensing image satellite data processing from the perspective of semantic. Firstly, it introduces the characteristics and semantic knowledge of remote sensing big data; Secondly, the semantic concept, semantic construction and application fields are introduced in detail; then, for remote sensing big data, the technical progress in the study field of semantic construction is analyzed from four aspects: semantic description and understanding, semantic segmentation, semantic classification and semantic search, focusing on deep learning technology; Finally, the problems and challenges in the four aspects are discussed in detail, in order to find more directions to explore.

A Survey of Semantic Construction and Application of Satellite Remote Sensing Images and Data

With the rapid development of satellite technology, remote sensing data has entered the era of big data, and the intelligent processing of remote sensing image has been paid more and more attention. Through the semantic research of remote sensing data, the processing ability of remote sensing data is greatly improved. This paper aims to introduce and analyze the research and application progress of remote sensing image satellite data processing from the perspective of semantic. Firstly, it introduces the characteristics and semantic knowledge of remote sensing big data; Secondly, the semantic concept, semantic construction and application fields are introduced in detail; then, for remote sensing big data, the technical progress in the study field of semantic construction is analyzed from four aspects: semantic description and understanding, semantic segmentation, semantic classification and semantic search, focusing on deep learning technology; Finally, the problems and challenges in the four aspects are discussed in detail, in order to find more directions to explore.