Linguistic Metadata Research Articles

Modelling Chinese contemporary calligraphy: the WRITE data model

Abstract This article presents the WRITE data model and dataset, a comprehensive collection of Chinese contemporary calligraphic data, utilizing Linked Open Data (LOD) principles. Calligraphy plays a pivotal role in Chinese culture, reflecting national identity and cultural transformations. The objective of this study is to enhance understanding and provide new tools for exploring Chinese contemporary calligraphy through LOD. The WRITE data model comprises artistic, linguistic, and socio-political-economic aspects. The WRITE data model, developed collaboratively with domain specialists, represents four collections: Contemporary Visual Art, Performance, Graffiti, and Decorative and Applied Arts. Metadata describing the artworks is structured by reusing and extending the Wikidata model. Complex relations are established between artworks and contextual elements, (e.g. people, exhibition history, organizations, and literary works). The artistic and linguistic metadata recorded over the ‘calli-writing units’ provide insights into shared and diverging characteristics with traditional calligraphy. Traditional and contemporary calligraphy practices are compared, highlighting how contemporary calligraphy challenges traditional rules. Two case studies demonstrate the formalization of specific items in the WRITE collection, showcasing the study of graffiti art’s socio-political meaning in China and the multidimensional nature of musicalligraphy performance. The WRITE dataset and data model contribute to advancing knowledge and understanding of Chinese contemporary calligraphy, offering valuable resources for artistic analysis and interdisciplinary research.

ADPBC: Arabic Dependency Parsing Based Corpora for Information Extraction

There is a massive amount of different information and data in the World Wide Web, and the number of Arabic users and contents is widely increasing. Information extraction is an essential issue to access and sort the data on the web. In this regard, information extraction becomes a challenge, especially for languages, which have a complex morphology like Arabic. Consequently, the trend today is to build a new corpus that makes the information extraction easier and more precise. This paper presents Arabic linguistically analyzed corpus, including dependency relation. The collected data includes five fields; they are a sport, religious, weather, news and biomedical. The output is CoNLL universal lattice file format (CoNLL-UL). The corpus contains an index for the sentences and their linguistic meta-data to enable quick mining and search across the corpus. This corpus has seventeenth morphological annotations and eight features based on the identification of the textual structures help to recognize and understand the grammatical characteristics of the text and perform the dependency relation. The parsing and dependency process conducted by the universal dependency model and corrected manually. The results illustrated the enhancement in the dependency relation corpus. The designed Arabic corpus helps to quickly get linguistic annotations for a text and make the information Extraction techniques easy and clear to learn. The gotten results illustrated the average enhancement in the dependency relation corpus.

Utilizing Neural Networks and Linguistic Metadata for Early Detection of Depression Indications in Text Sequences

Depression is ranked as the largest contributor to global disability and is also a major reason for suicide. Still, many individuals suffering from forms of depression are not treated for various reasons. Previous studies have shown that depression also has an effect on language usage and that many depressed individuals use social media platforms or the internet in general to get information or discuss their problems. This paper addresses the early detection of depression using machine learning models based on messages on a social platform. In particular, a convolutional neural network based on different word embeddings is evaluated and compared to a classification based on user-level linguistic metadata. An ensemble of both approaches is shown to achieve state-of-the-art results in a current early detection task. Furthermore, the currently popular ERDE score as metric for early detection systems is examined in detail and its drawbacks in the context of shared tasks are illustrated. A slightly modified metric is proposed and compared to the original score. Finally, a new word embedding was trained on a large corpus of the same domain as the described task and is evaluated as well.

Diachronic Atlas of Comparative Linguistics (DiACL)-A database for ancient language typology.

Feature stability, time and tempo of change, and the role of genealogy versus areality in creating linguistic diversity are important issues in current computational research on linguistic typology. This paper presents a database initiative, DiACL Typology, which aims to provide a resource for addressing these questions with specific of the extended Indo-European language area of Eurasia, the region with the best documented linguistic history. The database is pre-prepared for statistical and phylogenetic analyses and contains both linguistic typological data from languages spanning over four millennia, and linguistic metadata concerning geographic location, time period, and reliability of sources. The typological data has been organized according to a hierarchical model of increasing granularity in order to create datasets that are complete and representative.

Where in the World Are You? Geolocation and Language Identification in Twitter

The movements of ideas and content between locations and languages are unquestionably crucial concerns to researchers of the information age, and Twitter has emerged as a central, global platform on which hundreds of millions of people share knowledge and information. A variety of research has attempted to harvest locational and linguistic metadata from tweets to understand important questions related to the 300 million tweets that flow through the platform each day. Much of this work is carried out with only limited understandings of how best to work with the spatial and linguistic contexts in which the information was produced, however. Furthermore, standard, well-accepted practices have yet to emerge. As such, this article studies the reliability of key methods used to determine language and location of content in Twitter. It compares three automated language identification packages to Twitter's user interface language setting and to a human coding of languages to identify common sources of disagreement. The article also demonstrates that in many cases user-entered profile locations differ from the physical locations from which users are actually tweeting. As such, these open-ended, user-generated profile locations cannot be used as useful proxies for the physical locations from which information is published to Twitter.

A novel 3-tier XML schematic approach for web page translation

The proposed 3-Tier XML schematic approach is to demonstrate how to embed syntactic, semantic and computational linguistic metadata information in the structure of an XML document and how the various markups help in Internationalization and Localization processes toward faster and more meaningful machine translation of Web contents from one human language to another human language. In this approach, an XML content author needs to embed a source human language specific metadata information in an XML document. Various markups used in this novel 3-Tier XML Schematic approach are very useful for the machine translation of a web content. This is a significant step forward toward better internationalization and localization of web pages as well.