Table of Contents
Definitions
What Systems Are There?
How Should They Be Applied?
What Are the Strengths and Weaknesses?
How Do We Know If They Really Work?
How Expensive Will They Be to Implement?
Summary
Picture yourself standing at the base of that metaphorical range, the
Information Mountains, trailhead signs pointing this way and that: Taxonomy,
Automatic Classification, Categorization, Content Management, Portal Management.
The e-buzz of e-biz has promised easy access to any destination along one or
more of these trails, but which ones? The map in your hand seems to bear little
relationship to the paths or the choices before you. Who made those signs?
In general, it's been those
venture-funded systems and their followers, the knowledge management people and
the taxonomy people. Knowledge management people are not using the outlines of
knowledge that already exist. Taxonomy people think you need only a three-level,
uncontrolled term list to manage a corporate intranet, and they generally ignore
the available body of knowledge that encompasses thesaurus construction.
Metadata followers are unaware of the standards and corpus of information
surrounding indexing protocols, including back-of-the-book, online and
traditional library cataloging. The bodies of literature are distinct with very
little crossover. Librarians and information scientists are only beginning to be
discovered by these groups. Frustrating? Yes. But if we want to get beyond that,
we need to learn – and perhaps painfully, embrace – the new lingo. More
importantly, it is imperative for each group to become aware of the other's
disciplines, standards and needs.
We failed to keep up. It would be
interesting to try to determine why and where we were left behind. The marketing
hype of Silicon Valley, the advent of the Internet, the push of the dot com era
and the entry of computational linguists and artificial intelligence to the
realm of information and library science have all played a role. But that is
another article.
The current challenge is to
understand, in your own terms, what automatic indexing systems really do and
whether you can use them with your own information collection. How should they
be applied? What are the strengths and weaknesses? How do you know if they
really work? How expensive will they be to implement? We'll respond to these
questions later on, but first, let's start with a few terms and definitions that
are related to the indexing systems that you might hear or read about.
These definitions are patterned
after the forthcoming revision of the British National Standard for Thesauri,
but do not exactly replicate that work. (Apologies to the formal definition
creators; their list is more complete and excellent.)
Document – Any item, printed or otherwise, that is amenable to cataloging
and indexing, sometimes known as the target text, even when the target is
non-print.
Content Management System (CMS) – Typically, a combination management and
delivery application for handling creation, modification and removal of
information resources from an organized repository; includes tools for
publishing, format management, revision control, indexing, search and retrieval.
Knowledge Domain – A specially linked data-structuring paradigm based on
a concept of separating structure and content; a discrete body of related
concepts structured hierarchically.
Categorization – The process of indexing to the top levels of a
hierarchical or taxonomic view of a thesaurus.
Classification – The grouping of like things and the separation of unlike
things, and the arrangement of groups in a logical and helpful sequence.
Facet – A grouping of concepts of the same inherent type, e.g.,
activities, disciplines, people, natural objects, materials, places, times, etc.
Sub Facet – A group of sibling terms (and their narrower terms) within a
facet having mutually exclusive values of some named characteristics.
Node – A sub-facet indicator.
Indexing – The intellectual analysis of the subject matter of a document
to identify the concepts represented in the document and the allocation of
descriptors to allow these concepts to be retrieved.
Descriptor – A term used consistently when indexing to represent a given
concept, preferably in the form of a noun or noun phrase, sometimes known as the
preferred term, the keyword or index term. This may (or may not) imply a
"controlled vocabulary."
Keyword – A synonym for descriptor or index term.
Ontology – A view of a domain hierarchy, the similarity of relationships
and their interaction among concepts. An ontology does not define the vocabulary
or the way in which it is to be assigned. It illustrates the concepts and their
relationships so that the user more easily understands its coverage. According
to Stanford's Tom Gruber, "In the context of knowledge sharing…the term
ontology…mean(s) a specification of a conceptualization. That is, an ontology is
a description (like a formal specification of a program) of the concepts and
relationships that can exist for an agent or a community of agents."
Taxonomy – Generally, the hierarchical view of a set of controlled
vocabulary terms. Classically, taxonomy (from Greek taxis meaning arrangement or
division and nomos meaning law) is the science of classification according to a
pre-determined system, with the resulting catalog used to provide a conceptual
framework for discussion, analysis or information retrieval. In Web portal
design, taxonomies are often created to describe categories and subcategories of
topics found on a website.
Thesaurus – A controlled vocabulary wherein concepts are represented by
descriptors, formally organized so that paradigmatic relationships between the
concepts are made explicit, and the descriptors are accompanied by lead-in
entries. The purpose of a thesaurus is to guide both the indexer and the
searcher to select the same descriptor or combination of descriptors to
represent a given subject. A thesaurus usually allows both an alphabetic and a
hierarchical (taxonomic) view of its contents. ISO 2788 gives us two definitions
for thesaurus: (1) "The vocabulary of a controlled indexing language, formally
organized so that the a priori relationships between concepts (for example as
'broader' and 'narrower') are made explicit" and (2) "A controlled set of terms
selected from natural language and used to represent, in abstract form, the
subjects of documents."
Are these old words with clearly
defined meanings? No. They are old words dressed in new definitions and with new
applications. They mean very different things to different groups. People using
the same words but with different understandings of their meanings have some
very interesting conversations in which no real knowledge is transferred. Each
party believes communication is taking place when, in actuality, they are
discussing and understanding different things. Recalling Abbott and Costello's
Who's on First? routine, a conversation of this type could be the basis for a
great comedy routine (SIG/CON perhaps), if it weren't so frustrating – and so
important. We need a translator.
For example, consider the word
index. To a librarian, an index is a compilation of references grouped by topic,
available in print or online. To a computer science person (that would be IT
today), it would refer to the inverted index used to do quick look-ups in a
computer software program. To an online searcher, the word would refer to the
index terms applied to the individual documents in a database that make it easy
to retrieve by subject area. To a publisher, it means the access tool in the
back of the book listed by subject and sub-subject area with a page reference to
the main book text. Who is right? All of them are correct within their own
communities.
Returning to the degrees of
application for these systems and when to use one, we need to address each
question separately.
What Systems Are
There?
What are the differences among
the systems for automatic classification, indexing and categorization? The
primary theories behind the systems are:
·
Boolean rule base variations including keyword or matching
rules
·
Probability of application statistics (Bayesian statistics)
·
Co-occurrence models
·
Natural language systems
New dissertations will bring
forth new theories that may or may not fit in this lumping.
How Should They
Be Applied?
Application is achieved in two
steps. First, the system is trained in the specific subject or vertical area. In
rule-based systems this is accomplished by (1) selecting the approved list of
keywords to be used and, through matching and synonyms, building simple rules
and (2) employing phraseological, grammatical, syntactical, semantical, usage,
proximity, location, capitalization and other algorithms – based on the system –
for building complex rules. This means that, frequently, the rules are
keyword-matched to synonyms or to word combinations using Boolean statements in
order to capture the appropriate indexing out of the target text.
In Bayesian engines the system
first selects the approved list of keywords to be used for training. The system
is trained using the approved keywords against a set of documents, usually about
50 to 60 documents (records, stories). This creates scenarios for word
occurrence based on the words in the training documents and how often they occur
in conjunction with the approved words for that item. Some systems use a
combination of Boolean and Bayesian to achieve the final indexing results.
Natural language systems base
their application on the parts of speech and the nature of language usage.
Language is used differently in different applications. Think of the word
plasma. It has very different meanings in medicine and in physics, although the
word has the same spelling and pronunciation, not to mention etymology.
Therefore, the contextual usage is what informs the application.
In all cases it is clear that a
taxonomy or thesaurus or classification system needs to be chosen before work
can begin. The resulting keyword metadata sets depend on a strong word list to
start with – regardless of the name and format that may be given to that word
list.
What Are the
Strengths and Weaknesses?
The weaknesses of the systems
compared to human indexing are the frequency of what are called false drops.
That is, the keywords selected fit the computer model but do not make sense in
actual use. These terms are considered noise in the system and in application.
Systems work to reduce the level of noise.
The measure of the accuracy of a
system is based on
·
Hits – exact matches to what a human indexer would have
applied to the system
·
Misses – the keywords a human would have selected that a
computerized system did not
·
Noise – keywords selected by the computer that a human would
not have selected
The statistical ratios of Hits, Misses and Noise are the measure of how good
the system is. The cut-off should be at 85% Hits out of a total of 100% accurate
(against human) indexing. That means that Noise and Misses need to be less than
15% combined.
A good system will provide an accuracy rate of 60% initially from a good
foundation keyword list and 85% or better with training or rule building. This
means that there is still a margin of error expected and that the system needs –
and improves with – human review.
Perceived economic or workflow impacts often render this method unacceptable,
leading to the attempt to provide some form of automated indexing. The
mitigation of these results so human indexers are not needed is addressed in a
couple of ways. On the one hand suppose that the keyword list is hierarchical
(the taxonomy view) and goes to very deep levels in some subject areas, maybe 13
levels to the hierarchy. A term can be analyzed and applied only to the final
level and therefore its use is concise and plugged into a narrow application.
On the other hand, it may also be "rolled up" to ever-broader terms until
only the first three levels of the hierarchy are used. This second approach is
preferred in the web-click environment, where popular thinking (and some
mouse-behavior research) indicates that users get bored at three clicks and will
not go deeper into the hierarchy anyway.
These two options make it possible to use any of the three types of systems
for very quick and fully automatic bucketing or filtering of target data for
general placement on the website or on an intranet. Achieving deeper indexing
and precise application of keywords still requires human intervention, at least
by review, in all systems. The decision then becomes how precisely and deeply
you will develop the indexing for the system application and the user group you
have in mind.
How Do We Know If They Really Work?
You can talk with people who have tried to implement these systems, but you
might find that (1) many are understandably reluctant to admit failure of their
chosen system and (2) many are cautiously quiet around issues of liability,
because of internal politics or for other reasons. You can review articles,
white papers and analyst reports, but keep in mind that these may be biased
toward the person or company who paid for the work. A better method is to
contact users on the vendor's customer list and speak to them without the vendor
present. Another excellent method is to visit a couple of working
implementations so that you can see them in action and ask questions about the
system's pluses and minuses.
The best method of all is to arrange for a paid pilot. In this situation you
pay to have a small section of your taxonomy and text processed through the
system. This permits you to analyze the quality and quantity of real output
against real and representative input.
How Expensive Will They Be to Implement?
We have looked at three types of systems. Each starts with a controlled
vocabulary, which could be a taxonomy or thesaurus, with or without accompanying
authority files. Obviously you must already have, or be ready to acquire or
build, one of these lists to start the process. You cannot measure the output if
you don't have a measure of quality. That measure should be the application of
the selected keywords to the target text.
Once you have chosen the vocabulary, the road divides. In a rule base, or
keyword, system the simple rules are built automatically from the list for match
and synonym rules, that is, "See XYZ, Use XYZ." The complex rules are partially
programmatic and partially written by human editors/indexers. The building
process averages 4 to 10 complex rules per hour. The process of deciding what
rules should be built is based on running the simple rule base against the
target text. If that text is a vetted set of records – already indexed and
reviewed to assure good indexing – statistics can be automatically calculated.
With the Hit, Miss and Noise statistics in hand the rule builders use the
statistics as a continual learning tool for further building and refinement of
the complex rule base. Generally 10–20% of terms need a complex rule. If the
taxonomy has 1000 keyword terms, then the simple rules are made programmatically
and the complex rules – 100 to 200 of them – would be built in 10 to 50 hours.
The result is a rule base or knowledge extractor or concept extractor to run
against target text.
Bayesian, inference, co-occurrence categorization systems depend on the
gathering of training set documents. These are documents collected for each node
(keyword term) in the taxonomy that represents that term in the document. The
usual number of documents to collect for training is 50. Some require more, some
less. Collection of the documents for training may take up to one hour or more
per term to gather, to review as actually representing the term and to convert
to the input format of the categorization system. Once all the training sets are
collected, a huge systems processing task set is run to find the logical
connections between terms within a document and within a set of documents. This
returns a probability of a set of terms being relevant to a particular keyword
term. Then the term is assigned to other similar documents based on the
statistical likelihood that a particular term is the correct one (according to
the system's findings on the training set). The result is a probability engine
ready to run against a new set of target text.
A natural language system trains the system based on the parts of speech and
term usage and builds a domain for the specific area of knowledge to be covered.
Generally, each term is analyzed via seven methods:
·
Morphological (term form – number, tense, etc.)
·
Lexical analysis (part of speech tagging)
·
Syntactic (noun phrase identification, proper name boundaries)
·
Numerical conceptual boundaries
·
Phraseological (discourse analysis, text structure identification)
·
Semantic analysis (proper name concept categorization, numeric
concept categorization, semantic relation extraction)
·
Pragmatic (common sense reasoning for the usage of the term, such
as cause and effect relationships, i.e., nurse and nursing)
This is quite a lot of work, and it may take up to four hours to define a
single term fully with all its aspects. Here again some programmatic options
exist as well as base semantic nets, which are available either as part of the
system or from other sources. WordNet is a big lexical dictionary heavily used
by this community for creation of natural language systems. And, for a domain
containing 3,000,000 rules of thumb and 300,000 concepts (based on a calculus of
common sense), visit the CYC Knowledge Base. These will supply a domain ready to
run against your target text. For standards evolving in this area take a look at
the Rosetta site on the Internet.
Summary
There are real and reasonable differences in deciding how a literal world of
data, knowledge or content should be organized. In simple terms, it's about how
to shorten the distance between questions from humans and answers from systems.
Purveyors of various systems maneuver to occupy or invent the standards high
ground and to capture the attention of the marketplace, often bringing ambiguity
to the discussion of process and confusion to the debate over performance. The
processes are complex and performance claims require scrutiny against an equal
standard. Part of the grand mission of rendering order out of chaos is to bring
clarity and precision to the language of our deliberations. Failure to keep up
is failure to engage, and such failure is not an option.
We have investigated three major methodologies used in the automatic and
semi-automatic classification of text. In practice, many of the systems use a
mixture of the methods to achieve the result desired. Most systems require a
taxonomy in order to start and most systems tag text to each keyword term in the
taxonomy as metadata in the keyword name or in other elements as the resultant.
Access Innovations for Document abstracting and indexing ·
Document conversion · Business Taxonomies · Machine Aided Indexing
All rights reserved. Copyright ©
2006 Access Innovations, Inc.
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