Findability is the ease of which someone can locate the information they want. Often, it is confused with search – but search is just one method of achieving findability. Search allows people to enter in words that they hope are contained in the content they want to retrieve. Findability includes any method of locating this content, including but not limited to searching. Pingar DiscoveryOne improves findability.

1. How Text Analytics
Increases Search
Relevance
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2. Users care about findability, not search
Findability is the ease of which someone can locate
the information they want. Often, it is confused
with search – but search is just one method of
achieving findability. Search allows people to enter
in words that they hope are contained in the
content they want to retrieve. Findability includes
any method of locating this content, including but
not limited to searching. Pingar DiscoveryOne
improves findability.
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3. Significantly, findability includes Facetted Search. Facetted
search allows people to filter a search by various categories
and topics to remove irrelevant search results and more
rapidly spot the content they are looking for. Facets can also
be used to filter lists and views as well as search results.
Studies1 show that users evaluated facetted search as the
most desirable feature to improve findability.
Example of a facetted search by Category
1 Divoli, A. and Medelyan, A. Search interface feature evaluation in biosciences, HCIR 2011,
Google Mountain View, CA, USA Workshops
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4. By removing the irrelevant content, facetted search
improves search relevance – the number of useful
documents on the first page of search results.
Without facetted search, your investment in
enterprise search cannot deliver its full potential.
Facetted search however relies on documents being
categorized and tagged with keywords and phrases
associated with them – this is called metadata.
Without metadata, there can be no facetted search.
Unfortunately your staff do not enter metadata.
Some systems, such as email, may not even allow
users to enter metadata. This is why we created
DiscoveryOne – it’s an automated way to add
metadata.
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5. Users get most benefit from facetted
search with key phrases
Key words and phrases are the most beneficial metadata
for facetted search. When searching to gather specific
information or to find facts, people prefer a few relevant
facets of Pingar generated keyphrases. If you have
facetted search in your Enterprise Content Management
System (ECMS) or Enterprise Search engine, then facetted
search on keywords is critical.
As employees are unlikely to record keyphrases, they
must be automatically identified by a machine system
such as Pingar DiscoveryOne. DiscoveryOne reads a
document and identifies the words and phrases that best
describes the topics inside a document.
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6. Document categories can also be useful
In addition to keyphrases, organizations define metadata
such as what project a document belongs to or which
client or product line, etc. Matching these to a document
allows this metadata to be used with facetted search as
well.
Unlike traditional technology, DiscoveryOne has two
advanced forms of categorizing content automatically:
• By topic (e.g. product or projects or known issues)
• By content-type (e.g. employment contract or financial
statement)
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7. Categorizing by topic with taxonomies
Categorizing content by topic uses taxonomies. Taxonomies are a
pre-defined set of categories.
Taxonomies can be flat lists Taxonomies can have hierarchy
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8. Taxonomy categorization works well when you:
• Have a clear idea of the categories you want
• Can determine the words and phrases that a
document will have to indicate what category it
matches
This is where Pingar text analytics expertise
becomes useful. Pingar does more than match the
names of the categories when it categorizes by
topic.
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9. It also:
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10. Traditional systems tried to use arcane rules that
your employees would have to learn and enter in,
however the modern text analytics developed by
Pingar does not require that, so it’s faster and less
expensive to implement.
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11. Categorizing by content type with
statistical models
Taxonomy categorization does not work well with
determining what the nature of the content is – is
it a letter or a brochure or a contract or financial
statement? Statistical models are far superior
when categorizing documents by content type and
traditional technologies do not allow for this.
Statistical models are also useful when you don’t
know in advance what words are going to occur.
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12. Text categorization uses a statistical model
built specially for the categories and Pingar
tools enable this. In order to generate the
model, example documents of each category
are fed into the tool and the tool learns what
makes documents in each category the same.
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13. w w w . p i n g a r. c o m
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Sunnyvale, USA
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+64 9 950 3299
Thank you
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