Related papers: Standards for Language Resources
In spoken Task-Oriented Dialogue (TOD) systems, the choice of the semantic representation describing the users' requests is key to a smooth interaction. Indeed, the system uses this representation to reason over a database and its domain…
The quality of the dataset is crucial for ensuring optimal performance and reliability of downstream task models. However, datasets often contain noisy data inadvertently included during the construction process. Numerous attempts have been…
Developing document understanding models at enterprise scale requires large, diverse, and well-annotated datasets spanning a wide range of document types. However, collecting such data is prohibitively expensive due to privacy constraints,…
The Open Annotation Core Data Model specifies an interoperable framework for creating associations between related resources, called annotations, using a methodology that conforms to the Architecture of the World Wide Web. Open Annotations…
Our paper addresses the problem of annotation projection for semantic role labeling for resource-poor languages using supervised annotations from a resource-rich language through parallel data. We propose a transfer method that employs…
This document is part of the deliverables created by the RightsStatements.org consortium. It provides the technical requirements for implementation of the Standardized International Rights Statements. These requirements are based on the…
Large language models (LLMs) are widely used to generate structured outputs such as JSON, SQL, and code, yet public resources remain limited for evaluating generation that must simultaneously satisfy industry-standard XML and domain…
The Metadata Object Description Schema (MODS) was developed to describe bibliographic concepts and metadata and is maintained by the Library of Congress. Its authoritative version is given as an XML schema based on an XML mindset which…
We introduce a framework for lightweight dependency syntax annotation. Our formalism builds upon the typical representation for unlabeled dependencies, permitting a simple notation and annotation workflow. Moreover, the formalism encourages…
Special technologies need to be used to take advantage of, and overcome, the challenges associated with acquiring, transforming, storing, processing, and distributing spoken language resources in organisations. This paper introduces an…
Recent advancement in large language models (LLMs) has offered a strong potential for natural language systems to process informal language. A representative form of informal language is slang, used commonly in daily conversations and…
We describe a resource-based method of morphological annotation of written Korean text. Korean is an agglutinative language. The output of our system is a graph of morphemes annotated with accurate linguistic information. The language…
Data warehousing and OLAP applications must nowadays handle complex data that are not only numerical or symbolic. The XML language is well-suited to logically and physically represent complex data. However, its usage induces new theoretical…
In this paper we describe an approach for the analysis of documents in German and English with a shared pool of resources. For the analysis of German documents we use a document suite, which supports the user in tasks like information…
The process of documenting and describing the world's languages is undergoing radical transformation with the rapid uptake of new digital technologies for capture, storage, annotation and dissemination. However, uncritical adoption of new…
Generative large language models (LLMs) can be a powerful tool for augmenting text annotation procedures, but their performance varies across annotation tasks due to prompt quality, text data idiosyncrasies, and conceptual difficulty.…
XML is becoming the most relevant new standard for data representation and exchange on the WWW. Novel languages for extracting and restructuring the XML content have been proposed, some in the tradition of database query languages (i.e.…
The growing demand for AI training data has transformed data annotation into a global industry, but traditional approaches relying on human annotators are often time-consuming, labor-intensive, and prone to inconsistent quality. We propose…
Researchers have traditionally recruited native speakers to provide annotations for widely used benchmark datasets. However, there are languages for which recruiting native speakers can be difficult, and it would help to find learners of…
Large language models (LLMs) are powerful tools capable of handling diverse tasks. Comparing and selecting appropriate LLMs for specific tasks requires systematic evaluation methods, as models exhibit varying capabilities across different…