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Richard Eckart de Castilho
@reckart
that is something we need to check
If you download the software from our website and try it out on your local machine, chances are good that the PDF will load
we'll try fixing it for the upcoming bugfix release - thanks !
lisa563
@lisa563
OK, I willl try it , thanks.
Three other questions, the first one, When I upload an html document, I select HTML as Format, Import is unsuccessful, and there is no response or prompt after loading 2s.
The second one, If run inception platform on my local machine, what kind of computer configuration is recommended?
Third, can’t I upload word format files?
I'd appreciate your help y'all thank you.
Richard Eckart de Castilho
@reckart
HTML upload is broken in the current release - fix is already in place and will be included in 0.18.2
There is no specific machine recommendation - it really depends on how you use it, data size, if you use recommenders, KBs, etc etc.
But it is strongly recommended that you use a proper MariaDB/MySQL database if you are not just testing around
also, make sure you make regular backups / project exports
Word files are not supported atm
Adding some basic support for word files shouldn't be too difficult though. If you would like to see that, best open a feature request: https://github.com/inception-project/inception/issues/new/choose
lisa563
@lisa563
Thank you very much for your answer.
Richard Eckart de Castilho
@reckart
star/watch the github project, then you should get a mail when 0.18.2 is released
lisa563
@lisa563
OK, thank you.
lisa563
@lisa563
I'm very sorry, I still have some questions.
When annotation, the content of a label marked spans 2 lines, what should I do to mark it all at once?
Annotate approximately how many files, the forecast is more accurate?
lisa563
@lisa563
After I annotated 150 files, there are still many prediction results with a confidence of 0.1 or 0.2. How can I reduce these low-confidence prediction results? Is the annotation file not enough? Or what configuration needs to be done?
Jan-Christoph Klie
@jcklie
@lisa563 Regarding the recommender, that totally depends on your task and the recommender you choose. What exactly are you annotating and using? You can remove recommender with low confidence automatically by setting a threshold in the recommender settings, then the suggestions of that recommender are not shown
Richard Eckart de Castilho
@reckart
@lisa563 for curiosity: did you annotate those 150 files on our demo server?
lisa563
@lisa563
The recommender I used set the tool to Multi-Token Sequence Classifier (OpenNLP NER) and set the Score threshold to 0.7, but there are still many prediction results with a confidence lower than 0.7.
Yes, I annotate those 150 files on your demo server.
Richard Eckart de Castilho
@reckart
@lisa563 please be aware there is no security on the demo server as everybody uses the shared demo account and that we even regularly wipe it. We can only warn about not doing any serious work on the demo server.
Richard Eckart de Castilho
@reckart
@lisa563 regarding the confidence threshold: I have opened an issue for it: inception-project/inception#2028
lisa563
@lisa563
Thanks
lisa563
@lisa563
When I made annotations in the Named Entity Layer, I set Recmmender to use the Multi-Token Sequence Classifier (OpenNLP NER) tool, so I couldn't select the content of multiple lines at once. When the Allow crossing sentence boundaries is checked in the Layer configuration, the OpenNLP NER Tool can no longer be used. What should I do?
Richard Eckart de Castilho
@reckart
sequence labelling across sentence boundaries is not supported . If you want to annotate entire sentences, set the annotation granularity from "tokens" to "sentences" and then you can choose another type of opennlp recommender more suitable. Mind you might have to create a custom span layer for that - I do not know if the builtin NE layer accepts being configured to entire sentences.
lisa563
@lisa563
After setting the annotation granularity from "tokens" to "sentences", what type of opennlp recommender is suitable?
lisa563
@lisa563
I found that you use the "." Symbol to divide the sentence boundaries, but many people's names contain the "." Symbol, which will be divided into multiple sentences and cannot be fully labeled, such as: Dr. Anthony Arnett, Yolonda Y. Green, can this problem be solved?
Richard Eckart de Castilho
@reckart
if you import as "plain text" or any other format which does not define sentence boundaries, then a default splitter is applied
currently, we have no way of selecting a different splitter
but you can import using "plain text (one sentence per line)"
or even "plain text (sentence per line, space-separated tokens) to have control over token / sentence splitting
won't work with PDF's I'm afraid
at some point, we plan to make tokens/sentence boundaries editable, but not quite there yet
for layers with sentence-level granularity, the OpenNLP doccat recommender should be offered
lisa563
@lisa563
Thank you very much.
lisa563
@lisa563
Can the relation between entities be predicted? How should I configure it?
Richard Eckart de Castilho
@reckart
Relation prediction is being worked on on a branch
Richard Eckart de Castilho
@reckart
@lisa563 btw, a proper confidence threshold setting on the recommender sidebar is also pretty much done now and will be in the 0.19.0 release when it comes out
lisa563
@lisa563
Thank you.
Ivan Habernal
@habernal
hey all, I'm trying to find out whether there is a quick fix for wrapping longer annotation spans. Looking into the HTML code, it seems it's a SVG text tag, which doesn't look like supporting text wrapping - so there is perhaps no quick fix, correct?
Richard Eckart de Castilho
@reckart
At the Moment you only have the option of prewrapping the text before Import and then using the “ brat line oriented” display mode
Ivan Habernal
@habernal
Alright, I see, that's what I thought
I tested also the HTML mode which is in fact not bad, the only thing is that you cannot select and remove existing annotation
Richard Eckart de Castilho
@reckart
It is a known issue even in brat
You can
When you move the mouse over an annotation a popup appears
Click on that to select the annotation
Ivan Habernal
@habernal
yeah... well hidden :)