LISTSERV mailing list manager LISTSERV 16.5

Help for CODE4LIB Archives


CODE4LIB Archives

CODE4LIB Archives


CODE4LIB@LISTS.CLIR.ORG


View:

Message:

[

First

|

Previous

|

Next

|

Last

]

By Topic:

[

First

|

Previous

|

Next

|

Last

]

By Author:

[

First

|

Previous

|

Next

|

Last

]

Font:

Proportional Font

LISTSERV Archives

LISTSERV Archives

CODE4LIB Home

CODE4LIB Home

CODE4LIB  September 2019

CODE4LIB September 2019

Subject:

Re: Identifying description sources across a large corpus of MARC records

From:

Eric Lease Morgan <[log in to unmask]>

Reply-To:

Code for Libraries <[log in to unmask]>

Date:

Fri, 20 Sep 2019 11:07:15 -0500

Content-Type:

text/plain

Parts/Attachments:

Parts/Attachments

text/plain (85 lines)

Eric Morgan wrote:

>> [I also put this on AUTOCAT. Apologies if you also follow that. This
>> falls at the intersection of hand-cataloging, data processing and
>> simple AI.]...
> 
> Tim, your's is a perfect example of a supervised machine learning classification process. The process works very much like your computer's spam filter. Here's how:
> 
>   1. collect a set of data that you know is
>      library-written
> 
>   2. collect a set of data that you know is
>      publisher-sourced
> 
>   3. count, tabulate, and vectorize the
>      features of your data -- measure the data's
>      characteristics and associate them with
>      a collection
> 
>   4. model the data -- use any one of a number
>      of clustering algorithms to associate
>      the data with one collection or another,
>      such as Naive Bayes
> 
>   5. optionally, test the accuracy of the model
> 
>   6. save the model


The crucial part of a supervised machine learning process is the training step, and each sub-step can (and probably should) be tweaked given one's particular situation. There are a number of things to consider:

  * Identifying correct & accurate sets of training data is difficult. First, many times data does not fall neatly into one or more distinct categories. While a book may be written by a single individual, the book may fall into a number of different subjects or genres. Second, the distinction between one category and another may be so subtle, that even a computer, given a very large set of sample data, may not be able to consistently choose between one category and another. Third, binary classification is easy (spam versus ham). Classification into a flat list of categories is not too difficult. But hierarchal classification is very difficult.

 * Measuring the data -- counting, tabulating, and vectorizing -- is fraught with nuance. For example, what are you going to count? Individual words? Phrases? Numbers? Will you exclude stop words? Are you going to stemmatize the features? Maybe you will lemmatize the words? Maybe you will do neither. Will you merely count and tabulate the words, or maybe you will use something like an algorithm called TFIDF to create a more "relevant" list of words and scores? To what degree will you test the accuracy of the data, and if to a high degree, then what technique will you use?

  * Modeling the data - This is the "magic happen here" step. What algorithm are you going to use, and how are you going to parameterize it? Your choices will depend on many things, such as: the size & scope of the data, whether the data is numeric or not, the desire for a true/false classification or a degree of certainty, the size & scope of your computer(s), the degree of real distinctiveness of the different data sets, etc. Entire dissertations are written on this topic.

Not ironically, there are computer processes that help with the writing of these sorts of computer programs; there are techniques used to determine which of the various combinations -- "turning the knobs" -- are the most efficient. Computer programs used to create... machine learning programs. Yikes!!

When it comes to the use case alluded to in the original posting, this is what I would do:

  1) Identify a "large" set of library-written MARC
     records, at least 50.

  2) Identify a similarly large set of publisher
     -sourced MARC records.

  3) Loop through each MARC record, read the 520
     field, and save the result as a file in a
     directory named "library" or a directory named
     "publisher", accordingly.

  4) Run train.py against the directories.

  5) Identify a set of MARC records which contain
     values in the 520 field.

  6) Loop through each of these additional records,
     read the 520 field, and save the result as a
     file in a directory, called, say "unclassified"

  7) Run classify.py against the unclassified
     directory.

  8) The result will be a list of labels/filenames
     -- classifications.

You will then want to repeat the whole process for the purposes of "turning the knobs". For example:

  * increase the size of your datasets but keep
    them similarly sized; not as easy as you might
    think

  * use different techniques to measure your data

  * use different modeling algorithms

What is really cool about this whole process is that it is immensely scalable. For example, one could classify a whole set of documents, and one could feel okay about the result. Then, a year later, given more expertise and additional sets of data, the process could be tweaked, and the whole lot could be re-classified. The computer doesn't care about touching each item more than once. It will touch it as many times as you tell it. Yes, there is a lot of work up front, the work requires additional skills, but the result can definitely supplement & enhance the work that is already being done. 

We, as a profession, need to go beyond the use of computers to merely automate things. We need -- ought -- to learn how to exploit computers to really & truly take advantage of their ability to store vast amounts of data, organize it into information, widely share the information, consume ("read") the information, analyze the information, and output knowledge which is then verified by a person as true, useful, relevant, understandable, etc.

(Again, the whole lot of this posting has been saved in a tarball temporarily accessible at http://dh.crc.nd.edu/tmp/classification.zip)

--
Eric Lease Morgan, Librarian

Top of Message | Previous Page | Permalink

Advanced Options


Options

Log In

Log In

Get Password

Get Password


Search Archives

Search Archives


Subscribe or Unsubscribe

Subscribe or Unsubscribe


Archives

October 2019
September 2019
August 2019
July 2019
June 2019
May 2019
April 2019
March 2019
February 2019
January 2019
December 2018
November 2018
October 2018
September 2018
August 2018
July 2018
June 2018
May 2018
April 2018
March 2018
February 2018
January 2018
December 2017
November 2017
October 2017
September 2017
August 2017
July 2017
June 2017
May 2017
April 2017
March 2017
February 2017
January 2017
December 2016
November 2016
October 2016
September 2016
August 2016
July 2016
June 2016
May 2016
April 2016
March 2016
February 2016
January 2016
December 2015
November 2015
October 2015
September 2015
August 2015
July 2015
June 2015
May 2015
April 2015
March 2015
February 2015
January 2015
December 2014
November 2014
October 2014
September 2014
August 2014
July 2014
June 2014
May 2014
April 2014
March 2014
February 2014
January 2014
December 2013
November 2013
October 2013
September 2013
August 2013
July 2013
June 2013
May 2013
April 2013
March 2013
February 2013
January 2013
December 2012
November 2012
October 2012
September 2012
August 2012
July 2012
June 2012
May 2012
April 2012
March 2012
February 2012
January 2012
December 2011
November 2011
October 2011
September 2011
August 2011
July 2011
June 2011
May 2011
April 2011
March 2011
February 2011
January 2011
December 2010
November 2010
October 2010
September 2010
August 2010
July 2010
June 2010
May 2010
April 2010
March 2010
February 2010
January 2010
December 2009
November 2009
October 2009
September 2009
August 2009
July 2009
June 2009
May 2009
April 2009
March 2009
February 2009
January 2009
December 2008
November 2008
October 2008
September 2008
August 2008
July 2008
June 2008
May 2008
April 2008
March 2008
February 2008
January 2008
December 2007
November 2007
October 2007
September 2007
August 2007
July 2007
June 2007
May 2007
April 2007
March 2007
February 2007
January 2007
December 2006
November 2006
October 2006
September 2006
August 2006
July 2006
June 2006
May 2006
April 2006
March 2006
February 2006
January 2006
December 2005
November 2005
October 2005
September 2005
August 2005
July 2005
June 2005
May 2005
April 2005
March 2005
February 2005
January 2005
December 2004
November 2004
October 2004
September 2004
August 2004
July 2004
June 2004
May 2004
April 2004
March 2004
February 2004
January 2004
December 2003
November 2003

ATOM RSS1 RSS2



LISTS.CLIR.ORG

CataList Email List Search Powered by the LISTSERV Email List Manager