WHITE PAPER

OpenText Magellan Text Mining
helps users gain insight from
unstructured content
How to uncover insights and information that optimizeorganizations' content.

Contents
Introduction: Mining text for meaning

3

Definitions

4

Creating and using semantic metadata

6

Automated metadata assignment

6

Semi-automated metadata assignment

6

Methodology

7

Statistical patterns

7

Grammatical patterns

7

Machine learning

8

Decision trees

8

Post-processing algorithms

8

Knowledge engineering

9

Magellan Text Mining modules and architecture

9

Concept Extractor

10

Named Entity Extractor

10

Categorizer

10

Summarizer

11

Sentiment Analyzer

11

Language Detector

12

Additional Magellan Text Mining components

12Conclusion

13

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OpenText Magellan Text Mining helps users gain insight from
unstructured content. OpenText Magellan Text Mining enables
enterprises to take control of their knowledge assets to manage
and grow their business efficiently. Using thoughtfully selected
text analytics techniques, such as metadata federation or
crawlers to access data from multiple repositories, this tool can
extract from content the meaningful pieces of information andhelp users connect with the content most relevant to them.
This white paper focuses on how Magellan Text Mining streamlines and speeds
up a key task of content analytics, the semantic annotation of content, to makedocuments more “findable” and usable for uses ranging from indexing and content
curation to claim form processing and creating new, value-added content products.
It takes on the task of tagging content with semantic metadata, traditionally done
manually, and frees up workers from many hours of repetitive labor to exert morejudgment in content management.
But what is semantic metadata? How is it found by a machine and how does themachine know what to look for? Once semantic metadata is found, how can it beused? This white paper explores the OpenText approach to deriving this valuable
information. It will discuss the methodologies used in Magellan Text Mining andthe various approaches to identifying patterns and trends and explore the larger
issue of how artificial intelligence (AI) and machine learning can play a role in an
organization’s business processes.

Introduction: Mining the text for meaning
Unstructured data includes emails, social media posts and business documents—
essentially anything that contains textual content (i.e. natural language in writtenform). This kind of data has historically been harder to manage and extract useful
insights from than structured data (i.e. figures and entries that fit in databases),because it is more complex and ambiguous, requiring human understanding of
language in order to analyze its meaning.
Now, any information-rich organization can use Magellan Text Mining to analyze
unstructured data and other content, highlight its value and enhance comprehension
on what is going on within the organization. With machine learning (ML) techniquesthat let it quickly and flexibly annotate massive amounts of unstructured data withsemantic metadata, Magellan Text Mining improves content discovery, lowering
operating costs and helping users reach new insights more easily.

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The following discussion of how Magellan Text Mining works uses terms that arecommon in the field of content analytics but may not be familiar to all readers.
For example, “text mining” and “text analytics” are sometimes used interchangeably.
Although they are both methods of extracting valuable information from written
text, they are not quite the same thing. Other definitions are in the box below.
Name

Definition

Text mining

The process of deriving high-quality information and relationships from textual
content. The information and relationships derived are often expressed in the formof metadata and relationships between metadata. Text mining seeks to deduceand extract a variety of information from textual content, such as importantconcepts, named entities (people, companies, geographical locations) and topics.
It operates at the level of a single document or other piece of text.

Text analytics

In contrast with text mining, this term applies to a collection of texts, drawing insights
and finding patterns that exist both at the individual document level and at themulti-document level.

Machine learning

A branch of AI (artificial intelligence) concerned with the design and developmentof algorithms that allow computers to improve their performance on specific tasks,
learning by examples from real data, such as sensor data or database information.
ML algorithms use input data and statistical analysis to predict an output value,
based on previous experience or data. ML techniques can be grouped under two
types: supervised and unsupervised learning.

Supervised learning

Includes approaches where the machine learning classification models or
algorithms are trained on known data prior to being used on target content.
This approach is best leveraged when the results of learning are known.
Supervised methods allow careful supervision of the learning process by selecting
the teaching materials—choosing examples that “teach” or train the ML model.
For example, internal emails that are about a given topic even if they do not containpre-identified keywords. After the first supervised learning cycle, if users feel thealgorithms are not identifying the targets accurately enough, they can provide moretraining targets. They can also complement these evolving classification models
with explicit rules created by domain experts. Then they can repeat the cycle until
they are satisfied with the results and finally apply the models to real-world data.

Unsupervised learning

Refers to ML approaches where users know what data is given to the algorithm,
but not what results to expect.

Clustering and association algorithms

Techniques in unsupervised learning that can detect duplicates or similar/
replicated material in a document collection. Clustering and association can be
helpful in use cases such as eDiscovery or marking redundant documents fordeletion in content migration.

Metadata

Data that provides information about other data, such as a file’s size, type,author, main topic, names of people mentioned and date it was created or edited.
Metadata comes in two basic types: Semantic (or editorial) and system metadata.

Semantic (editorial) metadata

A major subset of metadata that conveys meaning from somewhere within the text,
such as identifying names, dates, places and topics. The metadata can then be
searched or automatically matched to assure that users connect with content thatis most relevant to them.

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Name

Definition

System metadata

The other major subset, system metadata covers features outside the meaningof the text, such as file size, type and the person who last edited it.

Named entities

Items in the larger world that are identifiable by proper names: People, places,
dates, products, organizations and titles such as CEO. For example, the noun "city"
is not a named entity, but "Waterloo" is a named entity.

Personally identifiable information (PII)

Information such as full names, addresses, ID numbers and health conditions that
could identify an individual person. PII is a type of named entity. From a practicalstandpoint, it is an especially important type because laws in many countries,
including the U.S., Canada, the UK and the European Union protect PII fromunauthorized sharing. This means organizations need to know where it is in theirstores of information.

Token

A series of characters separated by spaces that comprise a word, phrase,
sentence, symbol or similar element of meaning. Two or more tokens canbe combined into concepts, such as the term "Human Resources."
Magellan Text Mining allows the user to easily call upon complex natural language
processing methods and algorithms, combining statistical methods, linguistics insights
and machine learning, to yield value from unstructured content. (In the second halfof this paper, we will describe the functionalities within each module of Magellan
Text Mining and how they contribute to the overall results.) The broader Magellan
ecosystem of AI-enhanced analytics further allows the user to combine the resulting
metadata with structured data to answer deeper and broader analytic queries.
Magellan Text Mining can help organizations through the automation and semiautomation of a number of semantic metadata:• Concepts: Key terms that are considered the most relevant for a given document• Named Entities: Any term or sequence of terms that can be referred to (personalnames, companies, brands, PII, etc.)• Classifications/Topics: The different topics (such as “finance”) represented ina document; also classifications (such as “annual financial reports”) to which the
document belongs• Sentiment and Emotion: The overall tone of the document and each sentence
within (i.e. positive, negative, neutral), whether it expresses facts or opinions
referred to individually as "subjectivity", and the extent to which it expresses
specific emotions (anger, anticipation, disgust, fear, joy, sadness, surprise, trust)• Summary: A summary of a document’s content• Languages: The language or languages in the document• Named Entity and Relationships: Any term, or sequence of terms, that can bereferred to and related to each other

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Creating and using semantic metadata
Assigning semantic metadata throughout a text or group of texts is one of thefundamental techniques Magellan Text Mining uses to derive useful insights andhelp users connect with the content that’s most relevant to them. For easier reading,we will simply say “metadata” here, keeping in mind that non-semantic types of
metadata are also relevant from a broader analytics perspective. The metadata can
be classified and searched, quickly highlighting aspects of the text that humans
could only find by reading carefully. Once metadata is added to a document, useful
relationships can be built. For example, a user can create the list of all publisheddocuments for a given author and, in turn, retrieve all documents by that author.
Users routinely create metadata manually during the process of authoring, editing andsharing content. A basic example is when an author creates a document and gives it atitle. Or a user can manually add key terms associated with a document in an EnterpriseContent Management system, perhaps for search engine optimization (SEO) or easier
retrieval. However, manually creating metadata is very time-consuming and not always
on-target, so organizations seek text analysis tools that can automate the process tomake it faster and smarter.
Magellan Text Mining can assign metadata to a document either semi-automaticallyor completely automatically, depending on which mode the user prefers.
Automated metadata assignment
In completely automated mode, Magellan Text Mining can automatically process
textual data, storing the metadata so it can be used as is, without necessary revision.
For instance, it could verify that documents or emails do not contain specificpersonally identifiable information (PII), such as full legal names and credit card
purchase histories.
Combining different semantic metadata is an excellent way to improve searchefficiency. For example, a user may only be interested in content that was authoredby Mary J. Jones, contains a credit card number and mentions OpenText. Having all
this metadata ahead of time reduces the number of documents searched, delivering
the information that users need much faster.
Semi-automated metadata assignment
In semi-automatic assignment, Magellan Text Mining can spot and recommend
important keywords for each document, which can then be reviewed by the author
or a content manager. For example, Magellan Text Mining could suggest what key
phrases constitute variations on entity names, such as New York City, the Big Apple,
Manhattan or New York. Then, a reviewer can select what is or is not a relevantorganization to extract.
During the installation of Magellan Text Mining, an organization can configure its
preferences for fully or semi-automated metadata assignment depending on the usecase. Fully automated metadata assignment is better for tasks that require highly
reliable retrieval of all responsive documents even if that also brings up some nonresponsive ones, such as complying with a consumer’s request under the GDPR
to hand over all their PII. In information retrieval terms, this is known as high recall.

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By contrast, semi-automated assignment is desirable where the use case requires
identifying and retrieving the most relevant documents, for example, in replying to acustomer support ticket, where the organization wants to make sure the client getsthe most helpful answer as soon as possible. This is known as high precision. It also
allows the user to tweak and refine the search criteria, giving rounds of feedback
that make the Magellan Text Mining instance more accurate.
By offering a flexible choice of either semi-automated or fully automated metadataassignment, Magellan makes it easier to create an effective metadata management
strategy. This helps:• Increase the value of content through the creation of new points of accessor re-purposing.
• Create new products and/or services (e.g. for tailored market segments, which
couple content with customer usage across different brand silos, etc.).
• Retrieve information more quickly, reliably and cost-effectively, speeding
time-to-value.
• Give organizations better insight into the overall number, range and contentsof their various files.
• Reduce the amount of redundant, outdated, trivial and/or risky information
being retained.
• Improve the customer/user experience.
• Track and protect personal or sensitive information more effectively.
• Drive traffic and business to websites.
• Find and access information from many locations (e.g. search engines,
new portals, content aggregators, etc.)
Magellan Text Mining

Enterprise• Enterprise search: Portal design collaboration
searchWorkflow• Competitive
intelligence: Monitor blogs andPortal design, collaborationsocialmedia
Management• Risk assessment: Content compliance
Automatic categorization• Content management: Automation based onmeaning

Digital asset• Website navigation: Search Engine Magellan
management
Text Mining
Optimization (SEO)•Assets
Records
reuse,management:
monetization Content lifecycle
management• Digital asset management: Assets reuse,
Records
monetization

Competitive
intelligence
Monitor blogs and social media

Risk
assessment
Content compliancemanagement

Content
management

Content lifecycle management

Automation based on meaning• Workflow management: Automatic Website
categorization
navigation
Search Engine Optimization (SEO)

OpenText Magellan Text Mining streamlines a wide range of work processes
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Methodology
Magellan Text Mining uses a variety of carefully chosen methodologies thatcomplement each other, so that it can identify and extract the most relevant
semantic metadata quickly and accurately.
Linguistic patterns
Based on statistical patterns of the frequency and position of low-level textual
features such as tokens, Magellan Text Mining looks for linguistic patterns to
identify words and phrases (structured sequences of words) that are likely to beconcepts and entities.
This is where Magellan Text Mining transforms the tokens and n-grams from“sequences of items” to real lexical units (parts of speech such as nouns, verbs,
adjectives, etc.) that are found within their syntactic environment. Magellan
recognizes lexical units, groups them, filters out functional words, such as
conjunctions or prepositions, and finally exposes the most conceptually loaded
terms of text, namely the syntactic categories Nouns and Noun Phrases.
This is how Magellan Text Mining can identify the patterns of value that contributeto having the machine understand what the text is about, exactly as people do. For
example, when someone reads an article about the last Toronto Raptors-Golden State
Warriors game, how do they know it is about basketball? They recognize specificwords or words sequences used in the article: basketball, two teams, five players,
backboard, defender’s hoop, free throws, NBA. Likewise, the tools in Magellan Text
Mining extract only what is relevant for textual comprehension.
Machine learning
Magellan uses some of the most powerful algorithms in machine learning to extract
relevant insight from text. Some of the approaches include:
Decision trees
Magellan Text Mining uses decision trees to enhance machine learning processes
and resolve certain language ambiguities, increasing the accuracy of extractions.
From a computing perspective, decision trees are a decision support tool commonlyused in business processes, specifically in decision analysis, to help identify astrategy most likely to reach a certain goal.
Post-processing algorithms
Users can configure Magellan Text Mining to apply post-processing algorithms that
select relevant semantic metadata extractions. “Post-processing” refers to actions
a reviewer takes after the automated or semi-automated metadata assignment
process described above. This is important because it allows the users reviewing
a collection of text to distinguish what is truly meaningful from what is not. These
post-processing algorithms can expose the metadata according to different factors,
such as relevancy, frequency, engine confidence and position.

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Authority files:• Organization names• Person names• Geopolitical locations• Geophysical names• Trademark• Events• Life sciences

For example, they could be set to look for articles that use the word “tomatoes”
often enough to confirm that they are about growing that particular crop, rather
than a quick roundup of plants recommended for a summer vegetable garden.
Specifically, this would look like:
MTM = Cat: <tomato> Weight: 82.5
Post-Processing: Expose only categories > 85.0 Result: Document not shown to user
They can also filter out “stop words,” such as “and,” “the,” or “who,” which are too
common to be valuable search terms.
Knowledge engineering
It is important to separate ML from knowledge engineering. A number of Magellan
Text Mining’s modules use ML to improve their accuracy by predicting extraction
results based on statistical observations, without being explicit definitions. The
knowledge engineering functionalities embedded in Magellan Text Mining empowerusers to create and manage their own taxonomy and authority files and their own
classification or extraction rules, which can call upon different condition types.

• Features

Magellan Text Mining modules and architecture• Currency, date and time

Magellan Text Mining comprises six modules, each with its own configurations
and providing different types of semantic metadata.

• Computer and internet• Twitter• Personally identifiable
information (PII)

These six modules can be leveraged independently or together to extract meaningfrom various types of content (user-generated content, product reviews, legal
contracts, HR documents, etc.) reaching across multiple industries, domains andlevels of formality.

• Concept extraction: Extract keywords and key phrases• Named entity recognition: Extract and normalize entities (authority files)
Concept extraction

Named Entity Recognition

key phrases

entities (Authority files)• Textclassification:
Classify content
Extract
keywords andExtract(taxonomies)
and normalize

Text classification
Classify content(Taxonomies)• Sentiment and emotion analyis: Detect subjectivity, tone, and emotion• Text summarization: Extract most relevant sentences• Language detection: Detect the language(s) used in the document
Sentiment and emotion analysis
Detect subjectivity, tone,
and emotion

Text summarization
Extract most
relevant sentences

Language detection
Detect the language(s)used in the document

Content analytic modules

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Concept Extraction
Concept Extraction identifies meaningful information from documents using special
grammatical, pattern-based algorithms to extract key concepts.
Concepts: A keyword (simple concept) or key phrase (complex concept) foundwithin text. For example, ‘financial statement’ would be a key phrase, while ‘goal’
would be a keyword. Magellan Text Mining’s Concept Extraction uses a linguistic
approach to concept extraction. Conceptually, it operates in three steps:
1. Word cropping: A first analysis is performed distinguishing keywords fromnumbers, abbreviations, delimiters, etc.
2. Part of speech tagging: For each keyword, a tag for its grammatical role isassigned (e.g. verb, noun, pronoun, adjective, etc.)
3. Extraction of part of speech patterns: Relevant grammatical patterns (e.g. nounnoun, adjective-noun, etc.) are located and exposed.
This approach allows configuration for specific uses (e.g. news/magazinestories, photo captions, legal content, business memos, etc.). For example,
fully grammatical sentences in legal content or forms might require a different
configuration than one for user-generated content in social media photo captions.
Typical uses include the creation of on-the-fly document clusters, automatic
population of a document’s keywords metadata field and increased search engine
optimization (SEO) rankings, by automatically populating metadata within HTMLheaders, URLs, etc.
Named Entity Recognition
Named Entity Recognition (NER) locates and extracts places, people, organizations
and anything else with a name.
Named entities: Concepts that belong to specific, pre-defined subject
categories, typically people’s names, geographic locations, organization names
and trademarks/products. For example, “Open Text Corporation” would be anorganization entity; “North America” is a geographic entity and “Albert Einstein” is aperson name entity. Magellan’s entity extractor includes normalization algorithms toensure standardized entity assignments across all content.
The software can be taught to recognize variations of different names for thesame entity. For example, a document containing an entity labeled “RedDot” (nowa subsidiary of OpenText) or an entity labeled “OpenText Systems Inc.” can beindexed with the standard Open Text Corporation version of the entity, allowing
consistent and relevant search results across all content. The same applies toexamples like “John Smith” and “J. Smith” or “United Nations,” “UN” and “U.N.”
These algorithms rely on advanced machine learning techniques and ISO/NISO
standard authority files, which can be managed using OpenText™ Magellan™ Text
Mining Annotation Studio.
Client-specific authority files can also be imported in NER and managed using
Magellan Text Mining Annotation Studio. More details, including a list of authority
file topics included with the software, appear below. Further, users can create theirown new authority files in the Magellan Text Mining Annotation Studio.
Entities highlight the who, where and what of documents. Typical uses include
inline tagging, creation of live content indexes and lists of “The most talked about…”
companies, persons, locations or products. Uses also include increased SEO
rankings, by automatically populating metadata within HTML headers, URLs, etc.
OpenText Magellan Text Mining helps users gain insight from unstructured content

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Text Classification• Business and finance

Text Classification indexes and sorts documents by classification and provides
relevancy rankings. It creates document profiles by analyzing concepts andquerying them against an extensible knowledge base.

• General business• International Press
Telecommunications
Council• Industry Classification
Benchmark• Library of Congress
Thesaurus for Graphic
Materials• Energy Technology Data
Exchange andInternational Nuclear
Information System• Generally accepted
accounting principles(GAAP)• Records management• Résumés• Retention

Classifications: A specific division in a system of classification. The system canbe a taxonomy, a thesaurus or a controlled vocabulary. For example, “football” is aclassification referring to the sport, while “biology” would refer to the science.
Classifications express the topic of a document. For example, if the biology
classification is assigned to a document, it means this document is about biology,
even if the keyword biology is not mentioned in it. That means when someone islooking for a document about biology, using the classification metadata, they canfind relevant documents. By contrast, a full-text search would only yield results
where the keyword is mentioned, which can lead to irrelevant results.
The Text Classification module within Magellan Text Mining can manage anunlimited number of taxonomies at the same time. As first mentioned in the“Creating Semantic Metadata” section on page 6, this can be set up as a completely
automated service where precision is typically favored (e.g. batch processing
documents overnight or in real time with little to no manual review) or a semiautomated service, where recall is typically favored (e.g. a manual review of all orsome of the recommendations is performed). The Text Classification module canalso be configured to auto-assess the quality of its classifications by providing a
confidence score. This score can be used to highlight the documents that should be
reviewed. This enables the setup of a cost-efficient production workflow, where thereview team can focus on less accurate results.
Magellan Text Mining offers many out-of-the-box knowledge bases covering
important business and scientific topics, to speed up automated categorization ofyour content.
Further, it allows users to train their own organization-specific knowledge basesin the Annotation Studio. This is extremely useful for custom classification of
documents in a repository or as part of document workflow.
Sentiment and Emotion Analysis
Sentiment analysis detects the tone of documents and can determine whether adocument, or even a sentence, expresses an overall mood (negative, positive or
neutral) and whether that statement is subjective (opinion) or objective. This feature
can also identify the overall sentiment attached throughout the text to a namedentity, such as a product or organization. Emotion analysis determines the emotions
expressed within the text (anticipation, anger, disgust, fear, joy, sadness, surprise,trust), along with their classifications based upon the weight for each.
Typical uses include the automatic clustering and/or moderation of social media,
user-generated content (UGC), including surveys, emails, forums, blogs, chats
and tweets, Voice of the Customer tracking, measuring public opinion and otherbusiness intelligence applications.

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Supported languages

Text Summarization

Arabic, Bulgarian, Catalan,

Text Summarization identifies key sentences in a document and uses them to createa summary.

Chinese, Croatian, Czech,
Danish, Dutch, English,
Estonian, Finnish, French,
German, Greek, Hebrew,
Hungarian, Icelandic, Irish,
Italian, Japanese, Latvian,
Lithuanian, Norwegian,
Persian, Polish, Portuguese,Romanian, Russian, Slovak,
Slovene, Spanish, Swedish,Turkish, Ukrainian and
Vietnamese
Level of language support
varies per language. For more
information,contact us at
magellan@opentext.com.

Summary: A shortened version of the original document, highlighting the mostimportant sentences.
Typical uses include the creation of document teasers to fuel content memberships
and document summaries to enable the quick review/sorting/selection of important
assets. Uses also include increased SEO rankings by allowing the organic creationof optimized HTML code, increasing the code to content ratios or by automatically
populating metadata within HTML headers, etc.
Language Detection
Language Detection finds the language of text or paragraphs within text.
This module is statistically trained on n-grams coming from a variety of languages.
Language scores are provided per document or per paragraph. This module can
automatically classify documents based on their language; the metadata canalso be used in SEO use cases. Magellan Text Mining supports Arabic, Chinese(simplified), Dutch, English, French, German, Hebrew, Italian, Japanese, Portuguese,and Spanish with full, or most, functionality, and dedicated natural language
processing. It also supports basic concept and entity extraction in other languages
that use Latin alphabets, such as Polish, Romanian and Vietnamese and non-Latinalphabets, including Greek, Russian and Ukranian—even non-alphabetic languages.
Additional Magellan Text Mining components
These components round out the functionality of the complete Magellan Text
Mining platform.
Core Engine: The Core Engine is the binary package that contains configuration
files and Java libraries.
Data packs: Magellan Text Mining offers a range of knowledge bases in key
subjects, with industry-specific terminology, providing a handy head start for newcontent analytics projects. The data packs include foundational metadata specificto each language your text is in. Users can further expand these data packs withspecific configurations aligned to business needs.
Magellan Text Mining Annotation Studio: Magellan Text Mining Annotation Studioallows users to:• Build and manage taxonomies and vocabularies.
• Train a text classification against a given taxonomy.
• Have an on-the-fly interaction with the content tagging process.
• Maintain customized controlled vocabularies as they change over timeor over evolving content strategies.
• Search through data structures.
• Automate data structures.
REST APIs: Most of the functionality of Magellan Text Mining is exposed via a REST API.
This widely used interface enables an organization to embed text mining capabilities
into other enterprise applications. For example, as part of a loan processing workflow,
an organization might want to call Magellan Text Mining to classify incoming documents.

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Conclusion
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This paper has provided an overview of the various machine learning algorithms
and content analytic techniques on which Magellan Text Mining is based, as well as
the specific functions of each module. This analytics package offers a wide rangeof techniques for enriching unstructured text with structured metadata, including
extracting concepts, named entities, categories, sentiments, and emotions toimprove its “findability” and usability.
Magellan Text Mining includes a unique synthesis of statistical and grammaticalanalysis, a choice of more than 30 languages, and the flexibility to combine fully
automated extraction processes with human-guided metadata and configure
search parameters. It is also easily embeddable into other applications, making text
analytics simpler and more approachable for many industries and use cases.
Magellan Text Mining can transform the way an organization identifies meaningacross a wide range of text sources and collections, unlocking the power of
information to fuel data-driven business decisions and more efficient businessprocesses.
To learn more about how Magellan Text Mining can unlock new value and insightsfrom enterprise content, consider the Semantic Strategy Workshop. Participants
will work with an OpenText computational linguist on site to get an overview of how
Magellan works and explore various content challenges that can be addressed.
Email PortfolioAnalyticsPS@opentext.com for further details.

About OpenText
OpenText, The Information Company, enables organizations to gain insight throughmarket leading information management solutions, on-premises or in the cloud. For
more information about OpenText (NASDAQ: OTEX, TSX: OTEX) visit: opentext.com.

Connect with us:• OpenText CEO Mark Barrenechea’s blog
• Twitter | LinkedIn

opentext.com/contact
Copyright © 2021 Open Text. All Rights Reserved. Trademarks owned by Open Text.
For more information, visit: https://www.opentext.com/about/copyright-information • 05.2021 | 17629 EN

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