Coptic SCRIPTORIUM Wiki

Transcribe your text in GitDox. Alternatively, transcribe your text into a text file. Be sure the transcription divides the text into bound groups

At this point, you will want to use the Natural Language Processing (NLP) Service online. Veteran users will remember processing texts with individual NLP tools on a local machine and then editing the annotations in an Excel spreadsheet. The local process is outlined at the end of this page, but it is no longer recommended.

Run the NLP Service on your transcribed text in GitDox.

• If your text is in a text file, copy and paste it into the GitDox text editor. (See the GitDox page for more information on using the GitDox text editor.)
• If your text is already transcribed into the GitDox text editor and validated (see GitDox), you're ready for the NLP tools.

You will see an NLP button below the text window. Click it.

Note for veteran GitDox users: you do not need to proofread tokenization as part of the NLP Service process. The NLP service works better, now, without tokenizing first.

Clicking this button will annotate your data and change the format from running text to a spreadsheet, in which each item of textual data (known as a “token”) is a row in the spreadsheet, with each annotation as a column.

You can then edit the annotations manually. For example:

• You can add “Greek” to the “lang” (=language of origin) column if the tools failed to recognize and annotate a Greek loan word.
• You can correct the lemma, part-of-speech tag or normalization
• You can add an editorial note in a note_note column

Tips:

• You may need to add or subtract rows if the tokenization needs correcting. When doing so be careful of all the annotation layers, especially the spans for pages, columns, and lines. Ensure the spans all begin and end where they are supposed to.
• At the top of the spreadsheet in the upper right, you can click on a little bar in the upper right above the scrolling column and pull it down one row to freeze the column of labels in place as you scroll.
• You do not need to manually edit the columns for parsing. You do not need to worry about preserving the spans in these columns. Parsing will be done at a later step (whether manually or automatically).
• Use the online dictionary to look up word forms and their lemmas.
• Use ANNIS to search for word forms and how we usually tag/lemmatize them
• Prefacing a layer label (column label) with “ignore:” will ensure that that column is not imported into our public published data (ANNIS, GitHub).

Add verse and chapter layers following our guidelines on chapter divisions and versification.

Add a translation layer (even if you are not providing a translation at this time). Translation layers are usually the same length as the verse layer.

• To add a translation layer, position your cursor in the first row in the column to the right of the verse layer. Click on the button above the spreadsheet to add a column (it should show a green column). The spreadsheet will then add a column with spans matching the column on the left.
• If you are not providing an English translation right now, fill all translation spans with “…”. (Three periods, not the ellipsis character.)

Check all the column names to be sure they conform to our guidelines for annotation layers.

• In most cases, verse and translation spans should be identical in length, because typically verses are a sentence long. See our guidelines on versification for more information.
• You can delete unnecessary layers. If you are unsure about whether a layer/column should be deleted, relabel it with an “ignore:” prefix and ask a senior editor

Click the “validation” button to validate the data (= ensure it has valid structure to be published). If you are having trouble fixing validation errors, please contact a senior editor.

While data is saved automatically and frequently in the spreadsheet mode, we strongly recommend annotators commit their changes often using the commit log under the spreadsheet. Please use a commit message you will understand when you return to your work days or weeks later. (For example, “checked rows 101-200” is more detailed and more understandable than “continued checking annotations”.)

You will need to download the tools from our GitHub site. We no longer recommend using this process, as the most up to date tools are on our NLP online tool suite, available in three ways:

1. on our website (cut and paste or type in Coptic text)
2. an API (see https://corpling.uis.georgetown.edu/coptic-nlp/ for contact information)
3. using our annotation environment GitDox (see above)

If you are using our standalone tools, here are the steps:

Import the SGML into a spreadsheet.

Rename the existing layers according to the Annotation layer names guidelines. (Not all layers in the guidelines will exist in your file at this point.)

Remove any redundant columns. These may be hi (keep hi@rend); supplied (keep supplied@reason etc.); gap (keep gap@reason etc.).

Add missing information to existing layers. For instance, replace lb and cb placeholders in lb@n and cb@n columns with line and column numbers from original manuscript.

Note: the following steps are a guide to the kinds of work you will be doing. It is organized around the principle of editing one annotation layer at a time. When using the NLP pipeline to annotate everything at once, some editors, however, prefer to correct all annotation layers for one row at the same time and to go through the file row by row.

Create an original text ("orig") layer (You may do this last; you may even find it easier to do this last.)

Create a new or clean up an existing layer for original text in bound groups ("orig_group") (You may do this last; you may even find it easier to do this last.)

Proofread the normalized (norm) layer.

• You may wish to use Google Refine.
• You do not need to simultaneously proofread the norm_group layer; we can reconstruct norm_group using the data in norm.)

Reconstruct the norm_group layer.

Proofread the part of speech (pos), lemma (lemma), and morpheme (morph) layers. Part of speech and lemma are annotated on the norm level.

Proofread the language of origin (lang) layer.

• You may wish to use Google Refine.
• Coptic SCRIPTORIUM annotates for language of origin on the morph level not the word (norm) level. Be sure the language of origin tags align with the content and span in the morph layer rather than the norm layer.

Add translation, paragraph, and other layers as necessary following the annotation layer names guidelines.

Add Metadata.

Validate the file using the validation Excel Add-in.

Tokenizer

Import into spreadsheet

You now have an Excel file with tokenized morphemes aligned with bound groups, normalized morphemes. (If you are working with a Sahidica document, you may have translations and verses as well; with a diplomatic transcription line breaks and column breaks and other manuscript annotations are aligned.)

Proofread the tokenization of the bound groups. Add or delete rows if necessary. You may wish to use Google Refine.

Normalization

Create a normalized bound group layer

Create a morph layer

Ensuring orig and norm layers are the same span

Part of speech tagging and lemmatization. (Read the tagger instructions for parameters to lemmatize.)

Language of origin tagger

Metadata

Validate the file using the validation Excel Add-in.