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Text processing on the command line - sharing my tools


I'm quite fond of the command-line and spend a larger chunk of my life in a terminal emulator than I dare admit. I try to embrace the unix philosophy of using tools that "do one thing only, and do it well" and "interconnect largely via plain text formats". Following this philosophy, the output of one program can typically be the input to the next, for instance through the pipe operator (|) in a command-line shell.

Chaining multiple heterogeneous tools in this way gives a great amount of power and flexibility, something that's much harder to achieve through complex monolithic GUIs. It allows to quickly automate things in shell scripts and do batch processing, even if the underlying tools are written in different languages.

For text processing and data science, the unix shell and environment kind of form the lingua franca of data science; a common foundation upon which we can build our data processing pipelines. This environment is usually a unix-like system such as Linux or macOS that offers a (POSIX-compliant) shell like bash and a set of core utilities such as provided by GNU coreutils and friends, by FreeBSD/OpenBSD/NetBSD/macOS itself or by busybox. All of these are different implementations of the same core utilities, but following some standard specification (POSIX). Even Windows users have access to such a command-line environment via the WSL, or alternatively via Cygwin.

I'll first mention some of these standard unix tools, then some additional tools, and finally I'll move on to what is the main subject of this writing: my tools; the text processing tools for the command-line which I myself developed (or co-developed) and want to share with you.

Basic standard tools

Out of the box, a unix environment usually gives you text processing tools like:

  • awk - pattern scanning and processing language
  • cat - print or concatenate text (or tac to do it in reverse)
  • cut - extract/remove columns from lines
  • column - format input into multiple columns
  • colrm - remove columns from a file
  • comm - compare two sorted files line by line
  • dos2unix - convert DOS/Windows line endings to UNIX (\r\n -> \n)
  • diff - compare files line by line and show where they differ
  • echo/printf - print text
  • expand - convert tabs to spaces
  • fold - wrap each input line to fit in a specified width
  • head/tail - extract the first/last lines of a text
  • grep - print lines that match patterns (regular expressions)
  • nl - assign numbers to lines in a file
  • paste - merge lines from multiple files into one
  • rev - reverse lines character-wise
  • tr - translate or delete characters (here translation is just a mapping between character, not between natural languages)
  • sed - stream editor for filtering and transforming text
  • shuf - shuffle lines in a random order
  • split - split a file into several equally-sized pieces
  • sort - sort lines
  • uniq - remove duplicate lines
  • wc - word count, line count, byte count

They shine when used together. Take the example of having a plain text file, and wanting to extract a top 1000 frequency list of the words in it, all lower-cased. All this can be captured in the following one-liner:

$ sed -E 's/\W+/\n/g' text.txt | tr '[:upper:]' '[:lower:]' | tr -s '\n' | sort | uniq -c | sort -rn | head -n 1000

An analysis of the many possibilities of these common unix tools for text processing and data analysis is well out of scope for this writing. I do not intend to write a tutorial here. If you are interested in such a thing, I can warmly recommend the O'Reilly book Data Science at the Command Line by Jeroen Janssens, freely accessible on-line. It also covers some of the tools I mention in the next section, and more.

Common additional tools

In addition to these standard unix tools that are often part of the core system, there are more specialised tools I can recommend for text & data processing. The following few work with structured data like JSON, CSV or XML and overlap partially in functionality:

  • jq - like sed but specifically for extracting and manipulating JSON data, written in C.
  • Miller - mlr - like awk, sed, cut, join, and sort for name-indexed data such as CSV and tabular JSON. Written in Go.
  • xsv - CSV command line toolkit, written in Rust.
  • dasel - to query and modify JSON/CSV/YAML/XML , written in Go.
  • csvkit - suite of utilities for working with CSV (csvlook, csvcut, csvgrep,csvjson, csvsql, csvstat), written in Python.
  • xmllint in libxml2 - to validate and query XML files (xpath), written in C.

For visualisation purposes, I first have to give an honourable mention to less and more, both of which do more or less the same (pun intended). They serve as the so-called pager. For a richer experience, I can recommend the following:

  • bat - a cat clone and/or less alternative with fancy features like syntax highlighting; great for viewer all kinds of files, written in Rust.
  • glow - a Markdown viewer, written in Go.
  • csvlook from csvkit - viewer for CSV files, written in Python.

When it comes to conversion of texts, I recommend the following:

  • pandoc - A universal document converter. Converts between many document formats such as LaTeX/Markdown/ReStructuredText/plain text/Asciidoc/Word/Epub/Roff/etc... Written in Haskell.
  • iconv - convert plain text from one character encoding to another. Written in C.
  • uconv from icu - convert plain text from one character encoding to another. Written in C.

To combine these and all kinds of other data processing tools into larger automated pipelines, the most basic solution is to write a shell script. Alternatively, you can write a Makefile that builds targets using make. The latter also offers a certain degree of parallelisation using the -j parameter. Another useful tool to consider for parallelisation is parallel and/or xargs with the -P parameter. The latter, xargs, is also a basic standard tool useful when building sequential pipelines.

Remembering how to invoke all these command line tools may be rather daunting, and nobody expects you to remember everything anyway. Of course, a manual page should be available by simply running man followed by the command you need help on. You should also be able to get usage information from the tool itself by passing -h and/or --help. If you're lazy like me and prefer to quickly get some very short concise usage examples for a number of common use cases for a tool, then I can recommend tldr (internet slang for "too long; didn't read").

Intermezzo: What about Python/Julia/R etc...?

Bear with me before I finally get to show you my own tools. At this point some readers may wonder: All this command-line stuff is fine, but why not just use Python, or R, or Julia or whatever other open-source language implementation you prefer? (this excludes proprietary ecosystems like Matlab and Wolfram which I explicitly condemn). Languages like Python/Julia/R come with a solid standard library and on top of that a wide range of third party libraries to accommodate all kinds of text processing needs or whatever else a data scientist might desire. Furthermore, they too come with an interactive shell or can be used in the web-browser in the form of Notebooks (Jupyter/Pluto/RStudio), Some may find the latter more appealing than a terminal, especially for data stories and teaching.

Indeed, I say in response, these are all perfectly fine solutions and great projects. Such language ecosystems tend to give you tighter coupling between components and cleaner abstraction than the unix shell can. The shell often has more archaic syntax and interoperability is not always optimal because each tool can be very different. The shell, however, does provide a kind of lowest-common denominator that no other can. It offers a great deal of flexibility with regards to the tools you use. It's been around for decades and likely will last a while longer. Whether your command-line interface is written in C, C++, Rust, Go, Zig, Hare, R, Python, Perl, Haskell, Lisp, Scheme, or God forbid, even Java, it can all be readily mixed. Just by virtue of having a command line interface and reading either from file or standard input, and outputting to file or standard output. Committing yourself to a higher-level language, on the other hand, adds an extra layer, often in the form of a high-level language interpreter which has its own overhead and limits certain choices. This has both benefits and drawbacks. The trade-off is often between diversity and uniformity.

This is not a question of one method being inherently superior to the other, it all depends on your use case, the complexity thereof, the technologies you and those working with you are familiar and comfortable with, and your intended audience; the users. You then choose the form of interface that fits these conditions best; whether it is invoking tools via command-line interfaces, calling functions in software libraries via an API, or WebAPI, or even clicking buttons in GUIs.

My tools

In this writing, I would like to introduce some of the tools I wrote over the years for text processing, often in line of my work. As mentioned in the introduction, these tools are largely specialised in doing one thing, and doing it well, as per the unix philosophy. As the years go by, I'm more and more drawn to simpler solutions, where simple entails:

  • keeping the scope of a tool limited, constraining the amount of features
  • keeping a codebase maintainable by not letting it grow too large
  • limiting the number of dependencies (and in doing so limiting security vulnerabilities as well)
  • getting the job done with optimal performance, reducing the amount of unnecessary overhead.

Rust is my language of choice nowadays, as it compiles to highly performant native code on a variety of platforms and processor architectures. It offers important safety guarantees that prevents a whole range of common memory bugs that are prevalent in other system's programming languages as C and C++. You will therefore find a lot of my tools are written in Rust, often with a Python binding on top for accessibility for researchers and developers from Python. All software I write is free open source software available, almost always under the GNU General Public License v3.

I will be listing the tools in approximate order from simpler to more complex tools:

  • charfreq - Tiny tool that just counts unicode character frequencies from text it receives on standard input.
  • hyphertool - Hyphenation tool, performs wordwrap at morphologically sensible places for a number of languages.
  • ssam - Split sampler to draw train/development/test samples from plain text data using random sampling.
  • lingua-cli - Language Detection
  • sesdiff - Computes a Shortest Edit Script that shows the difference between two strings on a character-level
  • lexmatch - Matching texts against one or more lexicons

The above tools were all fairly small, now we're moving on to bigger software projects, mostly developed in the scope of my work at the KNAW Humanities Cluster and Radboud University Nijmegen, often under the umbrella of the CLARIN-NL and CLARIAH projects.

  • stam - Toolkit for stand-off annotation on text
  • ucto - Unicode tokeniser
  • frog - NLP suite for Dutch
  • analiticcl - Fuzzy string-matching system used e.g. for spelling correction, text normalisation or post-OCR correction.
  • colibri-core - Efficient pattern extraction (n-grams/skipgrams/flexgrams) and modelling from text.
  • folia-tools - Toolkit for working with the FoLiA XML format.

Last, a small tool that is a bit of an odd-one-out in this list, but which I wanted to include anyway:

  • vocage - Vocabulary training with flashcards (spaced-repetition system aka Leitner)

I'll briefly discuss each of the mentioned programs. Click the above links to quickly jump to the relevant section.


charfreq is a very simple tiny CLI tool that just computes (unicode) character frequencies from text received via standard input. Written in Rust.

You can find charfreq on Sourcehut or Github.


hyphertool does hyphenation and builds upon the third party hypher library, which in turn uses rules from the TeX hyphenation library. It is written in Rust.

$ hyphertool --language nl --width 15 test.txt
Dit is een test-
bestand. Kan je
dit bestand mooi
voor mij verwer-
Ik hoop op
een positief re-

It can also also output all syllables using hyphenation rules:

$ hyphertool --language nl test.txt
Dit is een test-be-stand. Kan je dit be-stand mooi voor mij ver-wer-ken?
Ik hoop op een po-si-tief re-sul-taat.

Or with character offsets:

$ hyphertool --language nl --standoff test.txt
Text	BeginOffset	EndOffset
Ik	68	70
hoop	71	75
op	76	78
een	79	82
po	83	85
si	85	87
tief	87	91
re	92	94
sul	94	97
taat	97	101

For more information, see hyphertool on Sourcehut, Github or


I wrote ssam, short for Split sampler, as a simple program that splits one or more text-based input files into multiple sets using random sampling. This is useful for splitting data into a training, test and development sets, or whatever sets you desire. This software was written in Rust.

It works nicely with multiple files when entries one the same lines correspond (such as sentence-aligned parallel corpora). Suppose you a sentences.txt and a sätze.txt with the same sentences in German (i.e. the same line numbers correspond and contain translations). You can then make a dependent split as follows:

$ ssam --shuffle --sizes "0.1,0.1,*" --names "test,dev,train" sentences.txt sätze.txt

For more information, see ssam on Sourcehut, Github or


sesdiff is a small and fast command-line tool and Rust library that reads a two-column tab separated input from standard input and computes the shortest edit script (Myers' diff algorithm) to go from the string in column A to the string in column B. In other words, it computes how strings difer. It also computes the edit distance (aka levenshtein distance). It builds upon the dissimilar library by David Tolnay for the bulk of the computations.

$ sesdiff < input.tsv
hablaron        hablar     =[hablar]-[on]                  2
contaron        contar     =[contar]-[on]                  2
pidieron        pedir      =[p]-[i]+[e]=[di]-[eron]+[r]    6
говорим         говорить   =[говори]-[м]+[ть]              3

The output is in a four-column tab separated format (reformatted for legibility here, the first two columns correspond to the input).

It can also do the reverse, given a word and a edit recipe, compute the other string.

It was initially designed to compute training data for a lemmatiser.

For more information, see sesdiff on Sourcehut, Github or


lingua-cli is a command-line tool for language detection. Given a text, it will predict what language a text is in. It supports many languages. It can also predict per line of the input, as the underlying algorithm is particularly suited to deal with shorter text, or it can actively search the text for languages and return offsets. This program is a mostly a wrapper around the lingua-rs library by Peter M. Stahl. It is written in Rust. The 3rd party library is also available for python, go, and Javascript via WASM.

$ echo -e "bonjour à tous\nhola a todos\nhallo allemaal" | lingua-cli --per-line --languages "fr,de,es,nl,en"
fr      0.9069164472389637      bonjour à tous
es      0.918273871035807       hola a todos
nl      0.988293648761749       hallo allemaal

Output is TSV and consists of an iso-639-1 language code, confidence score, and in line-by-line mode, a copy of the line. In --multi mode, it will detect languages in running text and return UTF-8 byte offsets:

$ lingua-cli --multi --languages fr,de,en < /tmp/test.txt
0       23      fr      Parlez-vous français? 
23      73      de      Ich spreche ein bisschen spreche Französisch ja. 
73      110     en      A little bit is better than nothing.

For more information, see lingua-cli on Sourcehut, Github or


lexmatch is a simple lexicon matching tool that, given a lexicon of words or phrases, identifies all matches in a given target text, returning their exact positions. It can be used compute a frequency list for a lexicon, on a target corpus. The implementation uses either suffix arrays or hash tables. The lexicon is in the form of one word or phrase per line. Multiple lexicons are supported. It is written in Rust.

$ lexmatch --lexicon lexicon.lst corpus.txt

Rather than take a lexicon, you can also directly supply lexical entries (words/phrases) on the command line using --query:

$ lexmatch --query good --query bad /nettmp/republic.short.txt 
Reading text from /tmp/republic.short.txt...
Building suffix array (this may take a while)...
good    4       193     3307    3480    278
bad     3       201     3315    3488

By default, you get a TSV file with a column for the text, the occurrence count, and one with the begin position (UTF-8 byte position) for each match (dynamic columns). In --verbose both you get cleaner TSV with separate UTF-8 byte offsets for each match:

$ lexmatch --verbose --query good --query bad /nettmp/republic.short.txt
Text    BeginUtf8Offset EndUtf8Offset
Reading text from /tmp/republic.short.txt...
Building suffix array (this may take a while)...
good    193     197
good    3307    3311
good    3480    3484
good    278     282
bad     201     204
bad     3315    3318
bad     3488    3491

For more information, see lexmatch on Sourcehut, Github or


STAM Tools is a collection of command-line programs for working with stand-off annotations on plain text. These all make use of the STAM data model and accompanying library for representing annotations, which can also be exported as simple CSV or TSV. They are written in Rust.

Each can be accessed through the executable stam and a subcommand. I mention just a few for brevity:

  • stam align - Align two similar texts, mapping their coordinate spaces. This looks for similar strings in each text using the Needleman-Wunsch or Smith-Waterman algorithm.
  • stam fromxml - Turn text and annotations from XML-based formats (like xHTML, TEI) into plain text with separate stand-off annotations following the STAM model. This effectively 'untangles' text and annotations.
  • stam query or stam export - Query an annotation store and export the output in tabular form to a simple TSV (Tab Separated Values) format. This is not lossless but provides a decent view on the data. It provides a lot of flexibility by allowing you to configure the output columns as you see fit.
  • stam validate - Validate a STAM model, tests if all annotation offsets still point to the intended text selections.
  • stam tag - Regular-expression based tagger on plain text, it also serves as a good tokeniser.
  • stam transpose - Given an alignment between text (as computed by stam align), converts annotations in one coordinate space to another.
  • stam view - View annotations as queried by outputting to HTML (or ANSI coloured text).

A demo video is available that demonstrates all these and more:

STAM demo

See STAM for more information about the data model and the general project, and stam-tools for information specifically about these command-line tools. A python binding is also available (pip install stam).


Ucto is a regular-expression based tokeniser. It comes with tokenisation rules for about a dozen languages. It is written in C++, initially by me but then in much larger part by my colleague Ko van der Sloot.

Ucto demo

See the Ucto website for more information. A Python binding is also available (pip install ucto).


Frog is a tool that integrates various NLP modules for Dutch. Such as a tokeniser (ucto), part of speech tagger, lemmatiser, dependency parser, named-entity recogniser, dependency parser and more. It is written in C++ by Ko van der Sloot as lead developer, and by myself, Antal van den Bosch and in its initial stages Bertjan Busser. It has a long history containing various components that are also made possible by Walter Daelemans, Jakub Zavrel, Sabine Buchholz, Sander Canisius, Gert Durieux and Peter Berck.

Though parts of the Frog have been superseded by more recent advancements in NLP, it is still a useful tool in many use-cases:

Frog demo

I also wrote a python binding so you can use Frog directly from Python (pip install frog).

See the Frog website for more information.


Analiticcl is an approximate string matching system. It can be used for spelling correction or text normalisation and post-OCR correction. It does comparisons against one or more lexicons. This may sound similar to lexmatch which I introduced earlier. However, analiticcl goes a lot further than lexmatch. It does not require exact matches with the lexicon but is designed to detect spelling variants, and to do so efficiently. It is written in Rust and builds upon earlier research by Martin Reynaert.

$ analiticcl query --lexicon examples/eng.aspell.lexicon --alphabet examples/simple.alphabet.tsv --output-lexmatch
--json < input.tsv > output.json
    { "input": "seperate", "variants": [
        { "text": "separate", "score": 0.734375, "dist_score": 0.734375, "freq_score": 1, "lexicons": [ "examples/eng.aspell.lexicon" ] },
        { "text": "desperate", "score": 0.6875, "dist_score": 0.6875, "freq_score": 1, "lexicons": [ "examples/eng.aspell.lexicon" ] },
        { "text": "operate", "score": 0.6875, "dist_score": 0.6875, "freq_score": 1, "lexicons": [ "examples/eng.aspell.lexicon" ] },
        { "text": "temperate", "score": 0.6875, "dist_score": 0.6875, "freq_score": 1, "lexicons": [ "examples/eng.aspell.lexicon" ] },
        { "text": "serrate", "score": 0.65625, "dist_score": 0.65625, "freq_score": 1, "lexicons": [ "examples/eng.aspell.lexicon" ] },
        { "text": "separated", "score": 0.609375, "dist_score": 0.609375, "freq_score": 1, "lexicons": [ "examples/eng.aspell.lexicon" ] },
        { "text": "separates", "score": 0.609375, "dist_score": 0.609375, "freq_score": 1, "lexicons": [ "examples/eng.aspell.lexicon" ] }
    ] }

Analiticcl demo

See the analiticcl source repository for more information. A python binding is also available (pip install analiticcl).


Colibri Core is software to quickly and efficiently count and extract patterns from large corpus data, to extract various statistics on the extracted patterns, and to compute relations between the extracted patterns. The employed notion of pattern or construction encompasses ngrams, skipgrams and flexgrams (an abstract pattern with one or more gaps of variable-size).

N-gram extraction may seem fairly trivial at first, with a few lines in your favourite scripting language, you can move a simple sliding window of size n over your corpus and store the results in some kind of hashmap. This trivial approach however makes an unnecessarily high demand on memory resources, this often becomes prohibitive if unleashed on large corpora. Colibri Core tries to minimise these space requirements.

Colibri Core is written in C++, a python binding is also available.

A demo video is available:

Colibri Core demo

See the Colibri Core website for more information. A journal article "Efficient n-gram, Skipgram and Flexgram modelling with colibri-core" was published in the Journal of Open Research Software (2016), and was also included in my PhD dissertation.


FoLiA tools is a set of numerous command-line tools I wrote for working with the FoLiA format. FoLiA is an XML-based Format for Linguistic Annotation. It is written in Python (pip install folia-tools). My colleague Ko van der Sloot wrote complementary C++ tooling to work with the same format, these are called foliautils.

Both contain a wide variety of programs such as validators, converters from and to other document and annotation formats (TEI, PageXML, Abby, plain text, ReStructuredText, CONLL, STAM) etc.


Vocage may be a bit of an odd-one out in this list as it is not really a text processing tool as-such, but rather a TUI (Terminal User Interface) for studying language vocabulary (which still counts as text so is excuse enough for inclusion here). I wrote it in Rust.

Vocage works by presenting flashcards using a spaced-repetition algorithm, which means it shows cards (e.g. words) you know well less frequently than cards you do not know yet.

Vocage example

The difference with more common software like Anki is that vocage is fully terminal-based, has vim keybindings, and it stores its data in a simple TSV format (both the wordlists as well as the learning progress go in the same TSV file).

It was featured in a video by Brodie Robertson in 2021.

For more information, see vocage on Sourcehut, Github or


I hope I have been able to convey some of my enthusiasm for the unix command-line for text processing and natural language processing. Moreover, I hope you find some of my software useful for some of your own projects.

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