#LancBox is a new-generation corpus analysis tool. Version 3 has been designed primarily for 64-bit operating systems (Windows 64-bit, Mac and Linux) that allow the tool’s best performance. #LancsBox also operates on older 32-bit systems, but its performance is somewhat limited. Downloading and running it is very easy. It is done in three simple steps: 1) download, 2) extract and 3) run.
Win 10: Windows 10 users need to
allow #LancsBox to run ■ Click on 'More info' ■ Then click on 'Run anyway'.
for the first time after download. Mac: Mac users can
run #LancsBox ■ Download #LancsBox for mac. ■ Double-click on the downloaded file to extract the LancsBox app. ■ Open in Finder and copy the 'LancsBox' app to the Applications folder – N.B. it is really important to run LancsBox from Applications!
■ Double-click on ‘LancsBox’ to run it. ■ If your computer complains about the source of the downloaded package, go to >System Preferences>Security and Privacy and allow the app to run.
Data can be loaded and imported into #LancsBox on the ‘Corpora’ tab. This tab opens automatically when you run #LancsBox. #LancsBox works with corpora in different formats (.txt, .xml, .doc, .docx, .pdf, .odt, .xls, .xlsx and many others) and with wordlists (.cvs). There are two options for loading corpora and wordlists: i) load data and ii) download corpora and wordlists that are distributed with #LancsBox.
The Ngrams tool allows in-depth analysis of frequencies of ngram types, lemmas and POS categories as well as comparison of corpora using the key ngram technique. It can be used, for example, to:
Compute frequency and dispersion measures for ngram types, lemmas and POS tags.
Visualize frequency and dispersion in corpora.
Compare corpora using the key ngram technique.
Visualize key ngrams.
If you have a question about #LancsBox functionalities, please read the manual or watch video tutorials to see if you can find the answers there. If you are experiencing problems with #LancsBox, try to find the answer in the troubleshooting chart.
(c) Vaclav Brezina, Lancaster University, 2018. The development of this website was supported by ESRC grants reference ES/K002155/1 and EP/P001559/1.