Where in the world are windmills, my man Friday, and love?

This posting describes how a Perl module named Lingua::Concordance allows the developer to illustrate where in the continum of a text words or phrases appear and how often.

Windmills, my man Friday, and love

When it comes to Western literature and windmills, we often think of Don Quiote. When it comes to “my man Friday” we think of Robinson Crusoe. And when it comes to love we may very well think of Romeo and Juliet. But I ask myself, “How often do these words and phrases appear in the texts, and where?” Using digital humanities computing techniques I can literally illustrate the answers to these questions.

Lingua::Concordance

Lingua::Concordance is a Perl module (available locally and via CPAN) implementing a simple key word in context (KWIC) index. Given a text and a query as input, a concordance will return a list of all the snippets containing the query along with a few words on either side. Such a tool enables a person to see how their query is used in a literary work.

Given the fact that a literary work can be measured in words, and given then fact that the number of times a particular word or phrase can be counted in a text, it is possible to illustrate the locations of the words and phrases using a bar chart. One axis represents a percentage of the text, and the other axis represents the number of times the words or phrases occur in that percentage. Such graphing techniques are increasingly called visualization — a new spin on the old adage “A picture is worth a thousand words.”

In a script named concordance.pl I answered such questions. Specifically, I used it to figure out where in Don Quiote windmills are mentiond. As you can see below they are mentioned only 14 times in the entire novel, and the vast majority of the time they exist in the first 10% of the book.

  $ ./concordance.pl ./don.txt 'windmill'
  Snippets from ./don.txt containing windmill:
	* DREAMT-OF ADVENTURE OF THE WINDMILLS, WITH OTHER OCCURRENCES WORTHY TO
	* d over by the sails of the windmill, Sancho tossed in the blanket, the
	* thing is ignoble; the very windmills are the ugliest and shabbiest of 
	* liest and shabbiest of the windmill kind. To anyone who knew the count
	* ers say it was that of the windmills; but what I have ascertained on t
	* DREAMT-OF ADVENTURE OF THE WINDMILLS, WITH OTHER OCCURRENCES WORTHY TO
	* e in sight of thirty forty windmills that there are on plain, and as s
	* e there are not giants but windmills, and what seem to be their arms a
	* t most certainly they were windmills and not giants he was going to at
	*  about, for they were only windmills? and no one could have made any m
	* his will be worse than the windmills," said Sancho. "Look, senor; thos
	* ar by the adventure of the windmills that your worship took to be Bria
	*  was seen when he said the windmills were giants, and the monks' mules
	*  with which the one of the windmills, and the awful one of the fulling
  
  A graph illustrating in what percentage of ./don.txt windmill is located:
	 10 (11) #############################
	 20 ( 0) 
	 30 ( 0) 
	 40 ( 0) 
	 50 ( 0) 
	 60 ( 2) #####
	 70 ( 1) ##
	 80 ( 0) 
	 90 ( 0) 
	100 ( 0)

If windmills are mentioned so few times, then why do they play so prominently in people’s minds when they think of Don Quiote? To what degree have people read Don Quiote in its entirity? Are windmills as persistent a theme throughout the book as many people may think?

What about “my man Friday”? Where does he occur in Robinson Crusoe? Using the concordance features of the Alex Catalogue of Electronic Texts we can see that a search for the word Friday returns 185 snippets. Mapping those snippets to percentages of the text results in the following bar chart:

bar chart
Friday in Robinson Crusoe

Obviously the word Friday appears towards the end of the novel, and as anybody who has read the novel knows, it is a long time until Robinson Crusoe actually gets stranded on the island and meets “my man Friday”. A concordance helps people understand this fact.

What about love in Romeo and Juliet? How often does the word occur and where? Again, a search for the word love returns quite a number of snippets (175 to be exact), and they are distributed throughout the text as illustrated below:

bar chart
love in Romeo and Juliet

“Maybe love is a constant theme of this particular play,” I state sarcastically, and “Is there less love later in the play?”

Digital humanities and librarianship

Given the current environment, where full text literature abounds, digital humanities and librarianship are a match made in heaven. Our library “discovery systems” are essencially indexes. They enable people to find data and information in our collections. Yet find is not an end in itself. In fact, it is only an activity at the very beginning of the learning process. Once content is found it is then read in an attempt at understanding. Counting words and phrases, placing them in the context of an entire work or corpus, and illustrating the result is one way this understanding can be accomplished more quickly. Remember, “Save the time of the reader.”

Integrating digital humanities computing techniques, like concordances, into library “discovery systems” represent a growth opportunity for the library profession. If we don’t do this on our own, then somebody else will, and we will end up paying money for the service. Climb the learning curve now, or pay exorbitant fees later. The choice is ours.

Ngrams, concordances, and librarianship

This posting describes how the extraction of ngrams and the implementation of concordances are integrated into the Alex Catalogue of Electronic Texts. Given the increasing availability of full-text content in libraries, the techniques described here could easily be incorporated into traditional library “discovery systems” and/or catalogs, if and only if the library profession were to shift its definition of what it means to practice librarianship.

Lingua::EN::Bigram

During the past couple of weeks, in fits of creativity, one of the things I spent some of my time on was a Perl module named Lingua::EN::Bigram. At version 0.03, it now supports not only bigrams, trigrams, and quadgrams (two-, three-, and four-word phrases, respectively), but also ngrams — multi-word phrases of an arbitrary length.

Given this enhanced functionality, and through the use of a script called ngrams.pl, I learned that the 10 most frequently used 5-word phrases and the number of times they occur in Henry David Thoreau’s Walden seem to surround spacial references:

  • a quarter of a mile (6)
  • i have no doubt that (6)
  • as if it were a (6)
  • the other side of the (5)
  • the surface of the earth (4)
  • the greater part of the (4)
  • in the midst of a (4)
  • in the middle of the (4)
  • in the course of the (3)
  • two acres and a half (3)

Whereas the same process applied to Thoreau’s A Week on the Concord and Merrimack Rivers returns lengths and references to flowing water, mostly:

  • a quarter of a mile (8)
  • on the bank of the (7)
  • the surface of the water (6)
  • the middle of the stream (6)
  • as if it were the (5)
  • as if it were a (4)
  • is for the most part (4)
  • for the most part we (4)
  • the mouth of this river (4)
  • in the middle of the (4)

While not always as clear cut as the examples outlined above, the extraction and counting of ngrams usually supports the process of “distant reading” — a phrase coined by Franco Moretti in Graphs, Maps, Trees: Abstract Models for Literary History (2007) to denote the counting, graphing, and mapping of literary texts. With so much emphasis on reading in libraries, I ask myself, “Ought the extraction of ngrams be applied to library applications?”

Concordances

Concordances are literary tools used to evaluate texts. Dating back to as early as the 12th or 13th centuries, they were first used to study religious materials. Concordances take many forms, but they usually list all the words in a text, the number of times each occurs, and most importantly, places where each word within the context of its surrounding text — a key-word in context (KWIC) index. Done by hand, the creation of concordances is tedious and time consuming, and therefore very expensive. Computers make the work of creating a concordance almost trivial.

Each of the full text items in the Alex Catalogue of Electronic Texts (close to 14,000 of them) is accompanied with a concordance. They support the following functions:

  • list of all the words in the text starting with a given letter and the number of times each occurs
  • list the most frequently used words in the text and the number of times each occurs
  • list the most frequently used ngrams in a text and the number of times each occurs
  • display individual items from the lists above in a KWIC format
  • enable the student or scholar to search the text for arbitrary words or phrases (regular expressions) and have them displayed in a KWIC format

Such functionality allows people to answer many questions quickly and easily, such as:

  • Does Mark Twain’s Adventures of Huckleberry Finn contain many words beginning with the letter z, and if so, how many times and in what context?
  • To what extent does Aristotle’s Metaphysics use the word “good”, and maybe just as importantly, how is the word “evil” used in the same context?
  • In Jack London’s Call of the Wild the phrase “man in the red sweater” is one of the more frequently used. Who was this man and what role does he play in the story?
  • Compared to Shakespeare, to what extent does Plato discuss love, and how do the authors’ expositions differ?

The counting of words, the enumeration of ngrams, and the use of concordances are not intended to short-circuit traditional literary studies. Instead, they are intended to supplement and enhance the process. Traditional literary investigations, while deep and nuanced, are not scalable. A person is not able to read, compare & contrast, and then comprehend the essence of all of Shakespeare, all of Plato, and all of Charles Dickens through “close reading”. An individual simply does not have enough time. In the words of Gregory Crane, “What do you do with a million books?” Distant reading, akin to the proceses outlined above, make it easier to compare & contrast large corpora, discover patterns, and illustrate trends. Moreover, such processes are reproducible, less prone to subjective interpretation, and not limited to any particular domain. The counting, graphing, and mapping of literary texts makes a lot of sense.

The home page for the concordances is complete with a number of sample texts. Alternatively, you can search the Alex Catalogue and find an item on your own.

Library “discovery systems” and/or catalogs

The amount of full text content available to libraries has never been greater than it is today. Millions of books have been collectively digitized through Project Gutenberg, the Open Content Alliance, and the Google Books Project. There are thousands of open access journals with thousands upon thousands of freely available scholarly articles. There are an ever-growing number of institutional repositories both subject-based as well as institutional-based. These too are rich with full text content. None of this even considers the myriad of grey literature sites like blogs and mailing list archives.

Library “discovery systems” and/or catalogs are designed to organize and provide access to the materials outlined above, but they need to do more. First of all, the majority of the profession’s acquisitions processes assume collections need to be paid for. With the increasing availability of truly free content on the Web, greater emphasis needs to be placed on harvesting content as opposed to purchasing or licensing it. Libraries are expected to build collections designed to stand the test of time. Brokering access to content through licensing agreements — one of the current trends in librarianship — will only last as long as the money lasts. Licensing content makes libraries look like cost centers and negates the definition of “collections”.

Second, library “discovery systems” and/or catalogs assume an environment of sacristy. They assume the amount of accessible, relevant data and information needed by students, teachers, and researchers is relatively small. Thus, a great deal of the profession’s efforts go into enabling people to find their particular needle in one particular haystack. In reality, current indexing technology makes the process of finding relavent materials trivial, almost intelligent. Implemented correctly, indexers return more content than most people need, and consequently they continue to drink from the proverbial fire hose.

Let’s turn these lemons into lemonade. Let’s redirect some of the time and money spent on purchasing licenses towards the creation of full text collections by systematic harvesting. Let’s figure out how to apply “distant reading” techniques to the resulting collections thus making them, literally, more useful and more understandable. These redirections represent a subtle change in the current direction of librarianship. At the same time, they retain the core principles of the profession, namely: collection, organization, preservation, and dissemination. The result of such a shift will result in an increased expertise on our part, the ability to better control our own destiny, and contribute to the overall advancement of our profession.

What can we do to make these things come to fruition?

Lingua::Concordance (version 0.01)

Below is a man page describing a Perl I module I recently wrote called Lingua::Concordance (version 0.01).

Given the increasing availability of full text books and journals, I think it behooves the library profession to aggressively explore the possibilities of providing services against text as a means of making the proverbial fire hose of information more useful. Providing concordance-like functions against texts is just one example.

The distribution is available from this blog as well as CPAN.

NAME

Lingua::Concordance – Keyword-in-context (KWIC) search interface

SYNOPSIS

  use Lingua::Concordance;
  $concordance = Lingua::Concordance->new;
  $concordance->text( 'A long time ago, in a galaxy far far away...' );
  $concordance->query( 'far' );
  foreach ( $concordance->lines ) { print "$_\n" }

DESCRIPTION

Given a scalar (such as the content of a plain text electronic book or journal article) and a regular expression, this module implements a simple keyword-in-context (KWIC) search interface — a concordance. Its purpose is to return lists of lines from a text containing the given expression. See the Discussion section, below, for more detail.

METHODS

new

Create a new, empty concordance object:

  $concordance = Lingua::Concordance->new;

text

Set or get the value of the concordance’s text attribute where the input is expected to be a scalar containing some large amount of content, like an electronic book or journal article:

  # set text attribute
  $concordance->text( 'Call me Ishmael. Some years ago- never mind how long...' );

  # get the text attribute
  $text = $concordance->text;

Note: The scalar passed to this method gets internally normalized, specifically, all carriage returns are changed to spaces, and multiple spaces are changed to single spaces.

query

Set or get the value of the concordance’s query attribute. The input is expected to be a regular expression but a simple word or phrase will work just fine:

  # set query attribute
  $concordance->query( 'Ishmael' );

  # get query attribute
  $query = $concordance->query;

See the Discussion section, below, for ways to make the most of this method through the use of powerful regular expressions. This is where the fun it.

radius

Set or get the length of each line returned from the lines method, below. Each line will be padded on the left and the right of the query with the number of characters necessary to equal the value of radius. This makes it easier to sort the lines:

  # set radius attribute
  $concordance->radius( $integer );

  # get radius attribute
  $integer = $concordance->query;

For terminal-based applications it is usually not reasonable to set this value to greater than 30. Web-based applications can use arbitrarily large numbers. The internally set default value is 20.

sort

Set or get the type of line sorting:

  # set sort attribute
  $concordance->sort( 'left' );

  # get sort attribute
  $sort = $concordance->sort;

Valid values include:

  • none – the default value; sorts lines in the order they appear in the text — no sorting
  • left – sorts lines by the (ordinal) word to the left of the query, as defined the ordinal method, below
  • right – sorts lines by the (ordinal) word to the right of the query, as defined the ordinal method, below
  • match – sorts lines by the value of the query (mostly)

This is good for looking for patterns in texts, such as collocations (phrases, bi-grams, and n-grams). Again, see the Discussion section for hints.

ordinal

Set or get the number of words to the left or right of the query to be used for sorting purposes. The internally set default value is 1:

  # set ordinal attribute
  $concordance->ordinal( 2 );

  # get ordinal attribute
  $integer = $concordance->ordinal;

Used in combination with the sort method, above, this is good for looking for textual patterns. See the Discussion section for more information.

lines

Return a list of lines from the text matching the query. Our reason de existance:

  @lines = $concordance->lines;

DISCUSSION

[Elaborate upon a number of things here such as but not limited to: 1) the history of concordances and concordance systems, 2) the usefulness of concordances in the study of linguistics, 3) how to exploit regular expressions to get the most out of a text and find interesting snippets, and 4) how the module might be implemented in scripts and programs.]

BUGS

The internal _by_match subroutine, the one used to sort results by the matching regular expression, does not work exactly as expected. Instead of sorting by the matching regular expression, it sorts by the string exactly to the right of the matched regular expression. Consequently, for queries such as ‘human’, it correctly matches and sorts on human, humanity, and humans, but matches such as Humanity do not necessarily come before humanity.

TODO

  • Write Discussion section.
  • Implement error checking.
  • Fix the _by_match bug.
  • Enable all of the configuration methods (text, query, radius, sort, and ordinal) to be specified in the constructor.
  • Require the text and query attributes to be specified as a part of the constructor, maybe.
  • Remove line-feed characters while normalizing text to accomdate Windows-based text streams, maybe.
  • Write an example CGI script, to accompany the distribution’s terminal-based script, demonstrating how the module can be implemented in a Web interface.
  • Write a full-featured terminal-based script enhancing the one found in the distribution.

ACKNOWLEDGEMENTS

The module implements, almost verbatim, the concordance programs and subroutines described in Bilisoly, R. (2008). Practical text mining with Perl. Wiley series on methods and applications in data mining. Hoboken, N.J.: Wiley. pgs: 169-185. “Thanks Roger. I couldn’t have done it without your book!”

Last of the Mohicans and services against texts

Here is a word cloud representing James Fenimore Cooper’s The Last of the Mohicans; A narrative of 1757. It is a trivial example of how libraries can provide services against documents, not just the documents themselves.

scout  heyward  though  duncan  uncas  little  without  own  eyes  before  hawkeye  indian  young  magua  much  place  long  time  moment  cora  hand  again  after  head  returned  among  most  air  huron  toward  well  few  seen  many  found  alice  manner  david  hurons  voice  chief  see  words  about  know  never  woods  great  rifle  here  until  just  left  soon  white  heard  father  look  eye  savage  side  yet  already  first  whole  party  delawares  enemy  light  continued  warrior  water  within  appeared  low  seemed  turned  once  same  dark  must  passed  short  friend  back  instant  project  around  people  against  between  enemies  way  form  munro  far  feet  nor  

About the story

While I am not a literary scholar, I am able to read a book and write a synopsis.

Set during the French And Indian War in what was to become upper New York State, two young women are being escorted from one military camp to another. Along the way the hero, Natty Bumppo (also known by quite a number of other names, most notably “Hawkeye” or the “scout”), alerts the convoy that their guide, Magua, is treacherous. Sure enough, Magua kidnaps the women. Fights and battles ensue in a pristine and idyllic setting. Heroic deeds are accomplished by Hawkeye and the “last of the Mohicans” — Uncas. Everybody puts on disguises. In the end, good triumphs over evil but not completely.

Cooper’s style is verbose. Expressive. Flowery. On this level it was difficult to read. Too many words. In the other hand the style was consistent, provided a sort of pattern, and enabled me to read the novel with a certain rhythm.

There were a couple of things I found particularly interesting. First, the allusion to “relish“. I consider this to be a common term now-a-days, but Cooper thought it needed elaboration when used to describe food. Cooper used the word within a relatively short span of text to describe condiment as well as a feeling. Second, I wonder whether or not Cooper’s description of Indians built on existing stereotypes or created them. “Hugh!”

Services against texts

The word cloud I created is simple and rudimentary. From my perspective, it is just a graphical representation of a concordance, and a concordance has to be one of the most basic of indexes. This particular word cloud (read “concordance” or “index”) allows the reader to get a sense of a text. It puts words in context. It allows the would-be reader to get an overview of the document.

This particular implementation is not pretty, nor is it quick, but it is functional. How could libraries create other services such as these? Everybody can find and get data and information these days. What people desire is help understanding and using the documents. Providing services against texts such as word clouds (concordances) might be one example.