Category Archives: Talking tech

Talking tech: Scrivener

In this Talking tech column, Andy Coulson takes a closer look at the writing software Scrivener, and investigates how it might also be useful for development editors, especially in fiction.

Header image of pen and notepaperWith any writing, if you ask most people what tools they use, Microsoft Word is likely to be the first thing that comes to mind. However, Scrivener is a tool for writers that its developers describe as a tool that ‘combines all the tools you need to create a first draft’. I’m going to take a look at Scrivener and see what it does and whether it might be a useful tool for us as editors and proofreaders to consider.

Scrivener has been around since 2006 and the current version (3.0) is available for Windows, Mac and iOS. It combines a word processor, outliner and tools to manage research. While this might sound like your familiar Word environment it offers a different, more flexible and freeform way to organise your work. The ‘Getting Started’ document in Scrivener describes a number of scenarios, but one in particular that felt familiar to me was where you might produce some parts of a written piece quickly and easily, but struggle with others. The process of producing a draft then involves linking those bits that are clear and filling in the gaps and the more unclear bits. Scrivener allows you to develop a process that lets you capture and reorganise those bits in a way that would be far more difficult in Word.

Features

Screenshot from Scrivener showing the sidebar on the left and document in the middle

Scrivener projects are not just a text file like Word (although Word files are a bit more complex than that): they are a collection of files that Scrivener refers to as a project. You can decide on your own organising principle, but for this article I will use the model the ‘Getting Started’ document in Scrivener uses. The key element is the binder, the sidebar in the interface that has a stacked list of all the elements of the project, which you can see on the left of the screenshot above. The content you are writing all sits within a ‘Draft’ folder. Under this there are top-level folders for chapters, second-level folders for parts and then documents, some of which have subdocuments containing the text.

Breaking up a large writing task in this way also helps to support the writing process (or any long project) by giving you a smaller task to aim at. Scrivener includes further tools that build on this, like writing targets. These allow you to set a target word count for the whole draft and for each writing session, which can help with motivation. The model of breaking, say, a chapter into smaller files can also help as it allows you to see your progress more clearly. There is also a ‘Composition Mode’ that is very clean and sparse if you find the distraction-free approach helps with writing.

One of the big differences from Word is that you can associate synopses and notes with each document. The synopsis is always linked to the document, and this can be viewed in the Outline and Corkboard tools to allow you to get different overviews of your whole project. Notes can then be used to keep any ideas that don’t need to be in the text, such as problems you can’t fix or ideas you can’t immediately work on, or what one review described as ‘a random epiphany’.

Another feature is that you can add labels and statuses to documents. You can assign labels for a whole project and give them different colours. You can use virtually anything as a label, but ‘Getting Started’ gives the example of using this to record the character whose point of view a document is written from, to help with reviewing the structure of a story. A status is a simple text label, intended to keep track of the state of the text – ‘done’, ‘in progress’, ‘first draft’, etc.

Another potentially useful feature is Snapshots. This allows you to take a snapshot of a document at a particular moment in time. You can then compare the text (but not format) changes in this to the current version and see the differences. In Scrivener you will tend to work on smaller chunks of text than in Word, as you split the project into multiple documents, which means the compare function is much easier to use than the one in Word.

Person researching their writing project

Scrivener also allows you to keep all of your research material within the project for easy reference. This can be material created in Scrivener (the ‘Getting Started’ document has examples of character and location sheets as the references for those things in a story), Word documents, PDFs, images, and video or audio files. These are all organised within a research folder in the binder. There is also a handy scratchpad feature for making quick notes about, say, a website that you can then save within your project.

You may remember that at the start of this section I said all of your writing is stored in a ‘Draft’ folder. This is so that you can export (or ‘compile’, in Scrivener terminology) the finished draft into another format. Scrivener supports a range of output formats, including docx and pdf. You can mark different levels of file or folder as different section types, so clearly identify where chapters and parts break. When you compile you can add further options, like using a specific font, or sectional numbering, giving you a lot of control over the finished output. The Word docx output looks very accurate and retains styles accurately, both by name and in the style features.

All in all, this is a well-written program with a clean and reasonably easy-to-use interface, given the number of features it has. Looking at it with my writing hat on I can really see the advantages of it. In many ways it is a much nicer writing environment than the standard Word interface, offering a less cluttered feel. With a 30 days actual usage trial (in other words you can use it in full for 30 days, even if it takes six months to do that) and for $45 this offers writers a really good alternative tool.

Scrivener for editors?

From an editing point of view, Scrivener lacks a lot of Word’s tools. There is no Track Changes function (although Snapshots could be used in an ad-hoc way), no support for macros and no support for PerfectIt. There is a comments function that is similar to Word’s Modern Comments feature but is slightly quicker to use. Comments are tagged with the name of their author along with the time and date. However, you can’t reply to comments like you can in Word.

For most copyeditors and proofreaders, Scrivener probably isn’t going to be much help. However, you can at least be certain that if your author uses Scrivener, the Word file you get will be an accurate representation of what they have. It may also mean that the author has notes and research to hand that will make dealing with queries simpler.

For development editors Scrivener could be a different proposition. It could certainly cut down on some of the back and forth of clarifying issues within a manuscript if the research, notes and comments are all available to the development editor. I think you could also manage quite a bit of the communication about the manuscript within the Scrivener file, again helping you to organise and follow how it develops.

About Andy Coulson

Andy CoulsonAndy Coulson is a reformed engineer and primary teacher, and a Professional Member of CIEP. He is a copyeditor and proofreader specialising In STEM subjects and odd formats like LaTeX.

 

 

About the CIEP

The Chartered Institute of Editing and Proofreading (CIEP) is a non-profit body promoting excellence in English language editing. We set and demonstrate editorial standards, and we are a community, training hub and support network for editorial professionals – the people who work to make text accurate, clear and fit for purpose.

Find out more about:

 

Photo credits: desktop by Tobias Herrmann on Pixabay; researcher by StockSnap on Pixabay.

Posted by Harriet Power, CIEP information commissioning editor.

The views expressed here do not necessarily reflect those of the CIEP.

Talking tech: Can a machine use conscious language?

In this Talking tech column, Andy Coulson delves into the world of artificial intelligence to find out how it might be able to consider the use of conscious language or edit text in the future.

For this issue of The Edit my column is going to be a little different from normal. Usually, I try to highlight how technology can help you with the theme of the issue. This issue’s theme, conscious language, proves to be a bit of a challenge on that front. What I am going to do instead is to get the crystal ball out and do a bit of speculating about how technology might develop to help ensure more conscious language use.

Natural language processing

Natural language processing (NLP) is the term used to describe a field of computer science that covers developing computer systems to understand text and speech in a comparable way to a human. This is a branch of artificial intelligence (AI), and I will get into some more detail about that later. This enables tools like Google Translate or the digital assistants Siri or Alexa to work. This is the field from which any tools (or indeed our competitors!) will come that will be able to improve how conscious the language in a text is.

Just to simplify things (slightly) I am going to ignore speech and all the computational issues that speech recognition brings. Let us concentrate on text and look at how machines are taught to understand that and make decisions about how to respond to it. To date, a lot of the NLP development has focused more on teaching a machine to respond to some text, whereas what we are trying to think about is how a machine would understand and amend a text. Microsoft and Grammarly both use AI to help improve their editing tools, so you can be sure there are other tech companies experimenting with this.

While language is to a degree rule based, it is also full of subtleties and ambiguities. The rules allow tools like PerfectIt to work – we can describe and recognise patterns and so teach a machine to do this too. This only takes us so far, as NLP then needs to pick the text apart to find the meaning within it. It must undertake a range of tasks on the text to enable the computer to ‘understand’ it. These include:

  • Speech or grammatical tagging, where the computer figures out the role of each word. This would be where it would identify ‘make’ being used as a verb (make a jacket) rather than a noun (the make of jacket).
  • Recognising names, so it can identify a proper noun. It knows Lesley is likely to be someone’s name rather than a thing, so ‘picking Lesley up on the way’ can be interpreted in the right sense.
  • Resolving co-references, where it relates a pronoun to a previously named object, so it recognises that ‘she’ is ‘Kathy’ from a previous sentence. This task can also be involved with dealing with metaphors or idioms – recognising that someone who is cold may not want an extra jumper but might not be much fun to talk to.
  • Sentiment analysis, which is also known as opinion mining. Here the computer is attempting to recognise more hidden aspects of the text, such as whether the tone is positive or negative.

All of these, and other functions we would need in order to make judgements about how conscious the language used in a text is, do not lend themselves to rules. Rather, they rely on a knowledge of context and conventions. Acceptable language in a novel set in 1960s Alabama would be quite different from that used in a modern social sciences paper about the same city and its inhabitants, but understanding the context will frame and shape language choices.

How machines learn

So, we have realised we are not going to be able to fix this one with a clever macro. What sort of computation do we need? Step forward AI – a term that covers a number of fields that involve machines that mimic human intelligence. One of the main aspects of this that NLP uses is machine learning, a field of computing covering machines that learn a task or tasks through different approaches.

One of the best-known AI companies is Google’s DeepMind division. They have made a name for themselves by approaching AI from the perspective of learning to play games using machine learning. To understand how they have progressed in the field we need a bit of a history lesson.

In 1997 an IBM project called Deep Blue beat the then World Chess Champion, Garry Kasparov. What Deep Blue did was to search all possible moves in the game and then pick the best next move. What is different about DeepMind’s AlphaGo is that they had to follow a different approach, as the game of Go has so many more possible moves than chess. This version of AlphaGo used neural networks (a brain-like arrangement of computing elements with lots of connections between each element) to compare the best move from the current position and the likelihood of winning from that move, which gave a more efficient way of narrowing down the choice of moves. AlphaGo was trained by playing vast numbers of games of Go to improve its ability to select moves and predict its current chance of winning. Eventually, in 2016, it beat Lee Sedol, widely regarded as one of the best players of all time.

DeepMind have since developed AlphaGo further and, instead of playing against experienced players, it learns from scratch by playing against itself. It uses a technique called reinforcement learning, where the system tries to optimise a reward called a Q-value. It has been able to play and master various video games from scratch (the Atari benchmark). Here AlphaGo tries to gain positive awards (and avoid negative ones) by, for example, collecting a game’s currency or surviving for a certain amount of time. It can then use the information about what it did and what reward it received to alter its strategy and see if that improves the Q-value.

Why is this important? It shows a progression from a very controlled environment with a limited (although large) number of variables, to a more complex one (Go) and then to a more generalised one (more varied games). We are still not at the point where this could be applied to a problem (like our language one) with very few constraints, but this certainly shows a progression. The latest version, AlphaZero, has apparently taught itself chess from scratch to a world champion level in 24 hours.

This technique of using neural networks and reinforcement learning seems to me to offer the potential to create tools with a more subtle understanding of learning. One issue that can cause problems is that AI often uses huge datasets to train the systems, but using already acquired data can bring with it historical problems. Microsoft created an AI chatbot for Twitter called Tay, designed to mimic the speech patterns of a 19-year-old girl, which it did very well right up to the point it learned to be inflammatory and offensive and had to be shut down. Microsoft believe that the trolling the bot experienced taught it how to be offensive. Similarly, Amazon developed an AI system to shortlist job candidates, and this showed a distinct bias against women. Amazon tracked the problem down to an underlying bias in the training data.

Given the increasing pressure on social media companies to filter offensive content, platforms like YouTube and Facebook are undoubtedly trying to use AI to recognise problematic language, and some of this may lead to tools we can use to highlight issues. However, as editors and proofreaders we are looking to improve poor language choices and make it more conscious. Looking at how the Editor function in MS Word and Grammarly have developed, they certainly point to a way forward. While I am not convinced a machine is going to take my job for some time, I can certainly see where it could make progress. I think the challenge of issues like conscious language is that they have too many subtleties, and the human ability to make judgements about these, and even to have a productive discussion with an author about a passage, means a human editor will continue to be able to add something a machine cannot to a piece of writing, for the foreseeable future.

About Andy Coulson

Andy Coulson is a reformed engineer and primary teacher, and a Professional Member of CIEP. He is a copyeditor and proofreader specialising In STEM subjects and odd formats like LaTeX.

 

 

About the CIEP

The Chartered Institute of Editing and Proofreading (CIEP) is a non-profit body promoting excellence in English language editing. We set and demonstrate editorial standards, and we are a community, training hub and support network for editorial professionals – the people who work to make text accurate, clear and fit for purpose.

Find out more about:

 

Photo credits: chess by Bru-nO on Pixabay, robot by mohamed_hassan on Pixabay, Go by Elena Popova on Unsplash.

Posted by Harriet Power, CIEP information commissioning editor.

The views expressed here do not necessarily reflect those of the CIEP.

Talking tech: Find and Replace

In this latest Talking tech post, Andy Coulson looks at how Find and Replace can speed up editing and styling references.

In keeping with this month’s theme of references for The Edit, I’m going to take a look at how we can use one of Word’s most powerful in-built tools – wildcard Find and Replace. References have to conform to tight formatting rules, and these lend themselves to using wildcard Find and Replace to tidy them up. This is particularly handy if you have a paper that was written with one form of referencing that needs to be changed to a different one. I’ll give a brief introduction to wildcards, then share some examples that focus on the type of issues in references and finally I’ll take a quick look at using these with Paul Beverley’s FRedit macro and PerfectIt.

Before we get cracking, a word of warning. Many academic authors use reference management software like Mendeley to produce reference lists. This software manages the references outside of Word and links to the Word document. With Mendeley you see references as form fields in the document. If you make changes, the next time the document is opened with a connection to Mendeley the reference list and links are overwritten, losing your edits. If you think this is the case, make sure you clarify how your client wants references edited.

Find and Replace can also be a blunt instrument, so use it with care. While you are refining your search, work on a copy of your text. And don’t use ‘Replace All’ unless you are very clear what you are replacing. It is safer to step through the things being found by using the ‘Replace’ or ‘Find Next’ (if you want to leave something unchanged) buttons.

Wildcards

Word’s Find and Replace feature has a number of hidden extras. If you’ve not already found these, they can be revealed by clicking the ‘More’ button under the ‘Replace with:’ field.

This opens the menu shown below and, as we are going to look at wildcards, we need to check the ‘Use wildcards’ option.

So, what is a wildcard? It is simply a character that can be used to represent anything else. A very simple example is using the character ‘?’ in a wildcard search. If you have ‘Use wildcards’ selected, put ‘r?n’ in the ‘Find what:’ field and ‘ran’ in the ‘Replace with:’ field then press ‘Replace All’, you would replace all instances of ‘ron’, ‘run’, ‘ren’, etc with ‘ran’. The ‘?’ tells Word to find any letter, so it looks for the pattern ‘r’ followed by any letter, followed by ‘n’. This does require a little thought, because what you have now also done, potentially, is turn ‘iron’ into ‘iran’, and a ‘wren’ would become a ‘wran’.

Now that example should alert you to the problems with this, but this is a very simplistic example and to do something more useful we need to dive deeper. Wildcards allow you to specify more complex patterns in the text, and as we will see in the examples below we can do some quite complex searches, often with a little trickery.

As this is a (relatively) short article I’m not going to be able to go into all of the possibilities. The best way to learn how to use these is to experiment. If you want some help, there are a number of resources available:

Examples

Let’s have a look at a couple of reference-related examples in detail so we can see how these work. For the referencing gurus out there, I am going to omit some required information from the references for clarity and play a bit fast and loose with referencing styles.

Example 1: Initials in names

Different referencing systems use different conventions for citing authors’ names in the reference list. So, you may have Hartley, J.R. (APA style), Hartley JR (Vancouver style) or even J.R. Hartley. Usually a reference list will be (largely) consistent, so it has a pattern we can find and a pattern we can replace it with. We will start with these three references:

A.N. Author. (1986). Writing for beginners (2nd ed.). Jones Books

S. Editor. (2021). Editing for fun and profit (1st ed.). MyPub Ltd

I.S.B. Nash. (2007). Cataloguing books (3rd ed.). Big Books Inc.

With Find and Replace we need to break problems down into manageable chunks, and sometimes multiple searches, that can be implemented by Find and Replace. Let’s assume we need to change author-name style in the list to Vancouver. The first issue we can tackle is the structure of the author names – setting them after the surname.

To do this we use the ‘Find what:’ string¹

^013([A-Z.]@) ([A-z]@).

What this does is:

  1. Looks for a line break: ^013 (‘^’ tells Word the number following is a character code. Note that these are for Windows and may be different on a Mac. You can find a list of these in the Wildcard Cookbook and macro book mentioned above).
  2. Looks for one or more initials: ([A-Z.]@) – the round brackets are grouping together and are important when we come to replace things; the [A-Z.] looks for capital letters or a full stop and the @ tells Word to look for one or more occurrences of these. Note that there is a space after this term, like in the text.
  3. Now looks for a capitalised word: ([A-z]@) – a combination of upper- and lower-case letters.

Now we replace the surname first and the initials after using this ‘Replace with:’ string:

^p\2 \1

This replaces the text as follows:

  1. We put the line break back in: ^p – note that we are using a different code here. ‘Why?’ you may ask. Because Word …
  2. Next we put the surname in: \2 – the \2 tells Word to use the second item in round brackets, what we found with item 3 above.
  3. Finally, we add the initials back in after a space – \1 – using the first bracketed item we found in item 2 above.

This leaves us with:

Author A.N. (1986). Writing for beginners (2nd ed.). Jones Books

Editor S. (2021). Editing for fun and profit (1st ed.). MyPub Ltd

Nash I.S.B. (2007). Cataloguing books (3rd ed.). Big Books Inc.

Now we need to remove the extra full points. We have to do that in two steps, by taking out all the relevant full points and then adding back the one after the final name.

So, removing the full points we use this ‘Find what:’ string, which simply finds one capital letter followed by one full point.

([A-Z]).

We then put the capital letter back in using this ‘Replace with:’ string:

\1

This gives us:

Author AN (1986). Writing for beginners (2nd ed.). Jones Books

Editor S (2021). Editing for fun and profit (1st ed.). MyPub Ltd

Nash ISB (2007). Cataloguing books (3rd ed.). Big Books Inc.

Now we add the final full point back in before the bracket with the year. That bracket gives us a pattern we can identify to put the full point in the right place. So, we use the ‘Find what:’ string:

([A-Z]) \(

As before, the round brackets contain a string to find one capital letter; this is followed by a space and finally by \(. ‘What is that?’ you may ask. Well, we use brackets to create a sequence in the search string that we can return to later, so in wildcard searches round brackets (and a number of other symbols) work as commands. In order to refer to those symbols we need to escape it, which means adding a backslash in front, so \( finds an opening round bracket. We can then use the following ‘Replace with:’ string to add the full point.

\1. ^40

As before \1. adds the initial back with the full point and ^40 puts an open bracket back. Again, note the different way that replace refers to the character, but that’s just the way it works I’m afraid. This then gives us:

Author AN. (1986). Writing for beginners (2nd ed.). Jones Books

Editor S. (2021). Editing for fun and profit (1st ed.). MyPub Ltd

Nash ISB. (2007). Cataloguing books (3rd ed.). Big Books Inc.

Example 2: Adding styling

I realise this is not proper Vancouver referencing, but I want to show you how we can add styling using wildcards. In this example we will apply italics to the book titles. As before, we need a pattern to recognise which part is the book title. In this case we have the end of the year ‘). ’ and the start of the edition ‘ (’. However, in order to find the title we have to find more text, the two brackets before and after, which we don’t want in italics. This means we need to be a bit cunning!

To do this we use this ‘Find what:’ string:

(\). )([A-z .]@)(\([0-9])

  1. (\). ) finds a closing bracket \), followed by a period and a space and we want to keep those, so we group them.
  2. ([A-z .]@) looks for a mix of upper- and lower-case letters, spaces and full stops – our surname and initials.
  3. (\([0-9]) looks for an open bracket \( plus a number – the characters at the start of the edition.

If we then replace this with:

\1%%\2%%\3

we put %% before and after the characters of the title that we want to italicise:

Author AN. (1986). %%Writing for beginners %%(2nd ed.). Jones Books

Editor S. (2021). %%Editing for fun and profit %%(1st ed.). MyPub Ltd

Nash ISB. (2007). %%Cataloguing books %%(3rd ed.). Big Books Inc.

We now have the title clearly marked, so can then style that. We search for the modified title with %% before and after.

%%([A-z .]@)%%

We then replace that with just the title text, which we have put in round brackets, so \1 goes in the ‘Replace what:’ field. Before we replace this, we need to tell Word to italicise this text. If you tap on the ‘More’ button in the bottom left you will see a ‘Format’ button. Pressing on this pops up the menu shown below. If you select ‘Font’ the font dialogue box pops up and you can select ‘Italic’. You will also see ‘Font: Italic’ appears under the ‘Replace with:’ field.

Running that Find and Replace gives us our final list:

Author AN. (1986). Writing for beginners (2nd ed.). Jones Books

Editor S. (2021). Editing for fun and profit (1st ed.). MyPub Ltd

Nash ISB. (2007). Cataloguing books (3rd ed.). Big Books Inc.

Integrating with Macros and PerfectIt

Wildcard Find and Replace searches like this are real timesavers, but there’s no obvious way of saving these and using them again and again. There is a short history for both the ‘Find what:’ and ‘Replace with:’ fields if you click the down arrow at the right of each, but I don’t find this particularly helpful.

Both Paul Beverley’s FRedit macro and PerfectIt support using wildcards, so offer a way to reuse multiple Find and Replace searches. As the point of using things like macros and wildcards is to save you time sometimes the investment of time to set up those searches in a macro or PerfectIt may not add up compared to just running the searches. For example, I do some work on papers for academic journals that are about 6,000 words long. I get material for multiple different journals, so it is quicker for me to just use a few Find and Replace searches rather than setting up, say, FRedit. However, a book or multiple papers for the same journal would change that, and setting up FRedit or PerfectIt would then be worthwhile. Having said that, writing this has convinced me to create a file of Find and Replace searches I can refer back to. I will probably format this as a FRedit list so I can use these with that macro.

PerfectIt allows you to perform wildcard searches in the ‘Wildcard’ tab. This lets you use all the features of wildcards in Word Find and Replace and adds a couple of neat features. The first of these is that you can add an instruction or prompt that explains what the search is doing, because, as we saw above, patterns can crop up in unexpected places. The second of these is that you can add exceptions. PerfectIt’s manual page uses the example of apostrophes being added to numbers followed by ‘s’, so ‘we have 3s, 4s and 5s chosen’ is correct. However, if we talk about ‘Page 4’s content’ we need the apostrophe. We can make numbers after the word ‘Page’ an exception.

FRedit is a scripted version of Find and Replace, so runs multiple Find and Replace searches from a list. It uses all the forms in Word Find and Replace, but has a few little tweaks you need to use in the file of searches we set up. FRedit doesn’t present us with the dialogue boxes that Word Find and Replace does. So in the file we use ‘|’ to separate the ‘Find what:’ and ‘Replace with:’ terms on a line and add ‘~’ at the start of the line if we are using wildcards. We can also add formatting easily. I sometimes use FRedit to quickly highlight things so I can then take my time on a read-through to check the context. For example, if you have an app called Balance it needs capitalising, but if you also talk about keeping your balance it doesn’t, so you have a mix, but the context will determine which you use.

Hopefully this has given you some ideas and encouraged you to go and experiment. I can honestly say learning how to use wildcards and Find and Replace efficiently has helped speed up my editing enormously. Combining these with FRedit or PerfectIt speeds things up even more where you have longer pieces or house styles you use regularly.


1 Paul Beverley has flagged that while ‘[A-z]@’ will find any letter it does not pick up on accented letters. A better solution is ‘[A-Za-z]@’.

About Andy Coulson

Andy Coulson is a reformed engineer and primary teacher, and a Professional Member of CIEP. He is a copyeditor and proofreader specialising In STEM subjects and odd formats like LaTeX.

 

 

About the CIEP

The Chartered Institute of Editing and Proofreading (CIEP) is a non-profit body promoting excellence in English language editing. We set and demonstrate editorial standards, and we are a community, training hub and support network for editorial professionals – the people who work to make text accurate, clear and fit for purpose.

Find out more about:

 

Photo credits: magnifying glass by towfiqu barbhuiya on Canva, joker by Roy_Inove on Pixabay.

Posted by Harriet Power, CIEP information commissioning editor.

The views expressed here do not necessarily reflect those of the CIEP.

Talking tech: Automating style sheets

In this Talking tech column, Andy Coulson considers the options available for automating style sheets.

The theme of February’s member newsletter, The Edit, is editorial judgement and, as I’m sure you’ll have noticed, this is something computers are not good at! While the latest artificial intelligence systems are great at applying rules consistently and often ruthlessly, they are not good at making those subtle judgements that convey nuanced meaning or reflect an author’s voice. So how can technology help us?

Our main tool for recording our professional editorial judgement is the style sheet, whether that is something created specifically for a job, or by making additions to a style sheet from a client. We have a number of tools that we can use to support our creation of style sheets.

Exploiting your computer’s strengths

So, if our computer is not good at judgement, what is it good for? Computers are very good at following rules and recognising consistency. We can make good use of that to spot patterns in the materials we are working on and inform decisions that we can then record in a style sheet. We can also use these to help us see where exceptions are, as these can be important too (for example judgment in British English refers to a legal judgment; judgement is what you are applying in choosing the right spelling of it!).

One thing I’m not going to look at is the Editor tool in Word itself. In theory this should be able to do a lot of the things I’m going to talk about, but I’m afraid I just can’t get on with it. I find that configuring it is too fiddly when you are working on material where the style can change from job to job. I’m going to look at PerfectIt and Paul Beverley’s macros, as both of these will ultimately allow you to do things quicker and, to my mind, more accurately as editors and proofreaders.

PerfectIt

PerfectIt is a proofreading and consistency add-in for Word. Many of us use it to speed up our workflow and it can be used to help with identifying style sheet issues with the text you are editing. When you run PerfectIt, the content of the document is compared against a number of tests. You can use the results to identify what needs to be included in the style sheet.

Let’s have a look at an example. I’ve got a journal article that the client needs to be in US English and they prefer to use the Merriam-Webster dictionary for spelling. I’ve selected the basic ‘US Spelling’ style in PerfectIt, but you may be able to find (or create) a more comprehensive style sheet that is more applicable. If you regularly use PerfectIt it would be worth keeping an eye on the ‘PerfectIt Users’ group on Facebook, as they are developing a collaborative style sheet project.

Here one of the tests is checking the consistency of hyphenation. As we can see, the author has used ‘photogenerated’ five times and ‘photo-generated’ once. This flags that we need to check and make a judgement. This isn’t in Merriam-Webster, but ‘photoactivated’ and ‘photometered’ are, so I feel comfortable going with the single word and can justify the choice, and that goes on the style sheet.

Hopefully you can see how you would go through and build your style sheet in this way, using the computer’s strength around consistency checking.

Macros

I’ve written before about Paul Beverley’s macros (archivepub.co.uk/index.html) – they are a brilliant resource and Paul contributes so much to the community with these. I’m not going to give detailed instructions about using the macros highlighted as Paul’s book and videos do that really well. I’ll concentrate on an overview of the tools you can use to create a style sheet at the start of a job. Paul also lists his editing process in the book or in his Macro-aided book editing video, and this includes using the macros to identify potential style sheet items.

Overall, I think I prefer this approach to using PerfectIt, although I do use both tools for different jobs. I work on a lot of school textbooks and I find that the macros are able to do a lot more tidying up of formatting than PerfectIt, so they suit my workflow better.

Two key macros to start with are DocAlyse and HyphenAlyse. DocAlyse is a ‘Swiss army knife’ tool that looks at a range of features of the document, such as how numbers appear, approximate US and UK (and -is/-iz) spelling counts, Oxford (serial) comma counts and so on. All of these give you a broad view of your author’s preferences. Paul also has the UKUScount and IZIScount macros that provide more accurate counts if the language and spelling choices are unclear, and SerialCommaAlyse that counts serial comma use more accurately. You can then apply your judgement to the results and record those in the style sheet.

Next up is HyphenAlyse, which looks at hyphen and en dash usage in the document and creates a list of hyphenated phrases along with their open equivalents as well as commonly hyphenated prefixes (for example, net-zero and net zero or coordinate and co-ordinate). The output gives you counts of each usage, helping you to narrow down your choice and again build the style sheet.

SpellAlyse can then be used to make a list of potential spelling issues – this will help identify spellings specific to the topic or flag words that need checking. SpellAlyse has a number of other tricks up its sleeve and Paul’s book explains these. In addition, ProperNounAlyse and CapitAlyse try to identify proper nouns and capitalised words, again helping to inform the choices you make, which can be added to the style sheet.

Unlike PerfectIt you don’t see these in context, but Paul has a number of highlighting macros that can help with this. Because all of these macros produce outputs in Word files it is quick to add things to a style sheet. It is also fairly easy to create a file to use with Paul’s FRedit macro, which performs a scripted find and replace on your file. As well as building a list of corrections to apply to the files you could also use FRedit to highlight a range of issues in the document so you can make decisions about them.

Macros do take a little getting into, but the time savings that they can provide make this time well spent. Over a few jobs you will be able to identify a set of macros that help you create an efficient process, and you will be able to allocate keystrokes to them and create backups.

Hopefully I’ve convinced you to invest a bit of time in learning these tools, as in the longer term they can be real time savers. As ever, back up your customised PerfectIt style sheets (.pft files) and FRedit script files as you can often use and adapt them. There are also lovely people out there sharing other resources like this that they have created. Among the places you can find these are the CIEP forums, where there’s a dedicated ‘Macros’ forum; and the ‘PerfectItUsers’ group on Facebook.

About Andy Coulson

Andy Coulson is a reformed engineer and primary teacher, and a Professional Member of CIEP. He is a copyeditor and proofreader specialising In STEM subjects and odd formats like LaTeX.

 

 

About the CIEP

The Chartered Institute of Editing and Proofreading (CIEP) is a non-profit body promoting excellence in English language editing. We set and demonstrate editorial standards, and we are a community, training hub and support network for editorial professionals – the people who work to make text accurate, clear and fit for purpose.

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Photo credit: laptop by Skitterphoto on Pexels.

Posted by Harriet Power, CIEP information commissioning editor.

The views expressed here do not necessarily reflect those of the CIEP.