The Linguistic Annotation Framework: a standard for annotation interchange and merging

Ide, Nancy; Suderman, Keith

doi:10.1007/s10579-014-9268-1

Cited by 23 publications

(10 citation statements)

References 8 publications

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“…Chapter 18 describes the SHEBANQ application, which enables search in the WIVU Hebrew Bible Text Database. It illustrates a typical project in which certain data, originally stored in an idiosyncratic format, have been curated and converted to a CLARIN-supported format based on the Linguistic Annotation Framework (LAF; Ide and Suderman, 2014), and a web application has been built for searching in these data, either by creating queries oneself, or by reusing queries created by others and stored here.…”

Section: Contents Of Part Iii: Infrastructure For Syntaxmentioning

confidence: 99%

Infrastructure for Syntax: Introduction

Odijk¹

2017

CLARIN in the Low Countries

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Section: Contents Of Part Iii: Infrastructure For Syntaxmentioning

confidence: 99%

Infrastructure for Syntax: Introduction

Odijk¹

2017

CLARIN in the Low Countries

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“…Lap eXchange Format (LXF) Closely following the ISO LAF guidelines (Ide and Suderman, 2014), LXF represents annotations as a directed graph that references pieces of text; elements comprising the annotation graph and the base segmentation of the text are explicitly represented in LXF with a set of node, edge, and region elements. Regions describe text segmentation in terms of character offsets, while nodes contain (sets of) annotations (and optionally direct links to regions).…”

Section: A Simple Examplementioning

confidence: 99%

Representation and Interchange of Linguistic Annotation. An In-Depth, Side-by-Side Comparison of Three Designs

Castilho

Ide

Lapponi

et al. 2017

Proceedings of the 11th Linguistic Annotation Workshop

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For decades, most self-respecting linguistic engineering initiatives have designed and implemented custom representations for various layers of, for example, morphological, syntactic, and semantic analysis. Despite occasional efforts at harmonization or even standardization, our field today is blessed with a multitude of ways of encoding and exchanging linguistic annotations of these types, both at the levels of 'abstract syntax', naming choices, and of course file formats. To a large degree, it is possible to work within and across design plurality by conversion, and often there may be good reasons for divergent design reflecting differences in use. However, it is likely that some abstract commonalities across choices of representation are obscured by more superficial differences, and conversely there is no obvious procedure to tease apart what actually constitute contentful vs. mere technical divergences. In this study, we seek to conceptually align three representations for common types of morpho-syntactic analysis, pinpoint what in our view constitute contentful differences, and reflect on the underlying principles and specific requirements that led to individual choices. We expect that a more in-depth understanding of these choices across designs may lead to increased harmonization, or at least to more informed design of future representations.

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“…Some of these models were designed by a committee of language annotation professionals, who attempts to cover the widest range of annotation situations in order to build a complete and expressive format. LAF (36), XMI 15 , and Open Annotation (37) are examples of models designed with this goal in mind. These formats are suited to expose and share annotations, especially if these annotations are complex.…”

Section: Knowledge Encodingmentioning

confidence: 99%

Text mining resources for the life sciences

Przybyła¹,

Shardlow²,

Aubin³

et al. 2016

Database

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Text mining is a powerful technology for quickly distilling key information from vast quantities of biomedical literature. However, to harness this power the researcher must be well versed in the availability, suitability, adaptability, interoperability and comparative accuracy of current text mining resources. In this survey, we give an overview of the text mining resources that exist in the life sciences to help researchers, especially those employed in biocuration, to engage with text mining in their own work. We categorize the various resources under three sections: Content Discovery looks at where and how to find biomedical publications for text mining; Knowledge Encoding describes the formats used to represent the different levels of information associated with content that enable text mining, including those formats used to carry such information between processes; Tools and Services gives an overview of workflow management systems that can be used to rapidly configure and compare domain- and task-specific processes, via access to a wide range of pre-built tools. We also provide links to relevant repositories in each section to enable the reader to find resources relevant to their own area of interest. Throughout this work we give a special focus to resources that are interoperable—those that have the crucial ability to share information, enabling smooth integration and reusability.

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The Linguistic Annotation Framework: a standard for annotation interchange and merging

Cited by 23 publications

References 8 publications

Infrastructure for Syntax: Introduction

Infrastructure for Syntax: Introduction

Representation and Interchange of Linguistic Annotation. An In-Depth, Side-by-Side Comparison of Three Designs

Text mining resources for the life sciences

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