RYCHLÝ, Pavel. Corpus Annotation Tool. 2017.
Other formats:   BibTeX LaTeX RIS
Basic information
Original name Corpus Annotation Tool
Authors RYCHLÝ, Pavel (203 Czech Republic, guarantor, belonging to the institution).
Edition 2017.
Other information
Original language English
Type of outcome Software
Field of Study 60200 6.2 Languages and Literature
Country of publisher Czech Republic
Confidentiality degree is not subject to a state or trade secret
WWW URL
RIV identification code RIV/00216224:14330/17:00096859
Organization unit Faculty of Informatics
Keywords in English text corpora; corpus annotation; part of speech tagging
Technical parameters The tool was used to annotate texts in 6 languages by 16 annotators in total. Czech and Norwegian corpora were annotated mainly for evaluation reasons, there are several PoS annotated (including UD tag set) corpora for both languages. Annotation of 4 Ethiopian languages (Amharic, Oromo, Somali, Tigrinya) was used to build respective PoS taggers which are part of the HaBiT system.
Changed by Changed by: doc. Mgr. Pavel Rychlý, Ph.D., učo 3692. Changed: 1/6/2017 13:53.
Abstract
The main goal of this work is an annotation tool for easy and fast production of small annotated corpora for languages without linguistics resources. The tool is optimised for the following priorities: Simple tool for instant usage: The client part of the tool is a web application which works in any browser, it requires small amount of all resources (memory, internet connection, screen size), it could be used even from mobile devices (tablets, phones). Using small standard tag set: If possible, we have used Universal Dependencies (UD) tag set (version 2). It is well documented and used for many different languages. The main description of the tags is directly included in the tool with links to the UD site. Fast navigation: Users can use mouse or keyboard for navigation, only one or two mouse clicks or key strokes are needed for annotation of one token. All sentences are pre-annotated via an builtin adaptive tagger and only incorrect annotation have to be corrected by users. Clean texts: Users can (and are instructed to) reject to annotate a sentence if the sentence is not clear.
Links
7F14047, research and development projectName: Harvesting big text data for under-resourced languages (Acronym: HaBiT)
Investor: Ministry of Education, Youth and Sports of the CR
PrintDisplayed: 23/7/2024 02:36