Topic-oriented writing structures information around topics rather than categories. It allows for easier access, scanability, modular writing by multiple authors, and facilitation of content reuse. Topic types include concept, reference, and task. A topic has a title describing its theme, followed by mixed text and images. The process involves determining topic types, writing topical titles, and describing each topic's theme. This approach can be applied to existing material by analyzing content, determining topic types, rewriting titles, and using tables to remove repetition.
INFORMATION SKILLS: NAVIGATING RESEARCH IN LIBRARYChris Okiki
This document provides information and guidance about navigating research in library facilities. It discusses developing information literacy skills like improving discovery of resources, teaching information literacy courses, and deepening faculty collaboration. The document also addresses shifts in the library profession toward more of a focus on services, people, and enabling users rather than just products, facilities, and mediation. It provides examples of free online resources like Khan Academy and Omeka that libraries can offer. Finally, it offers tips for effective search strategies when using databases and electronic sources, including defining information needs, choosing appropriate sources, and using techniques like keyword searching, limiters, and Boolean operators.
This document discusses entity linking, which is the task of finding topics in text by linking surface forms to topics represented by Wikipedia URIs. It describes a statistical method inspired by DBpedia Spotlight that builds an annotation model from Wikipedia data by extracting statistics on surface forms, topics, and their associations. These statistics are then used to annotate new text by deciding whether to annotate surface forms, which topic they refer to, and addressing challenges like ambiguity through rules and probability adjustments.
The document summarizes notes from a meeting about a grant project to build an intelligent web service for collecting and classifying online educational resources. The project will use a combination of manual and automatic methods, including machine learning classifiers and rule-based classifiers, to identify different types of syllabus materials on websites. It will provide users with sample course sites, overviews of course topic relationships and schedules, and the ability to populate or search for materials based on topics or templates. The goal is to leverage both human and machine intelligence to maximize the usefulness and accuracy of the system.
We research hierarchy of topics extracted from documents (news, publications, discussions etc.).
Our system is targeted at data researchers.
It provides:
-Trend tracking
-Similar and related topics detection
-Topic segmentation, which aims to solve information overload (http://mlvl.github.io/Hierarchie/) problem
The topic model we use is not a collection of tags but is the combination of NLP + statistical analysis.
The Information Mapping Format: A Proven Content StandardInformation Mapping
The Information Mapping Format: A Proven Content Standard. What makes the Information Mapping format work so well? Learn about the elements of the Information Mapping format that are the keys to making it an internationally recognized content standard.
This document outlines 5 steps to improve an organization's management and reuse of content. The steps are to 1) identify areas that follow similar processes and have them share content and practices, 2) examine shared systems and how consistently they are used, 3) examine how different areas create, store, and manage content, 4) look for high-payoff opportunities to centralize content control, and 5) promote a culture of sharing best practices and improvements. Following these steps can improve employee performance, facilitate compliance, reduce costs, and enhance productivity and efficiency.
INFORMATION SKILLS: NAVIGATING RESEARCH IN LIBRARYChris Okiki
This document provides information and guidance about navigating research in library facilities. It discusses developing information literacy skills like improving discovery of resources, teaching information literacy courses, and deepening faculty collaboration. The document also addresses shifts in the library profession toward more of a focus on services, people, and enabling users rather than just products, facilities, and mediation. It provides examples of free online resources like Khan Academy and Omeka that libraries can offer. Finally, it offers tips for effective search strategies when using databases and electronic sources, including defining information needs, choosing appropriate sources, and using techniques like keyword searching, limiters, and Boolean operators.
This document discusses entity linking, which is the task of finding topics in text by linking surface forms to topics represented by Wikipedia URIs. It describes a statistical method inspired by DBpedia Spotlight that builds an annotation model from Wikipedia data by extracting statistics on surface forms, topics, and their associations. These statistics are then used to annotate new text by deciding whether to annotate surface forms, which topic they refer to, and addressing challenges like ambiguity through rules and probability adjustments.
The document summarizes notes from a meeting about a grant project to build an intelligent web service for collecting and classifying online educational resources. The project will use a combination of manual and automatic methods, including machine learning classifiers and rule-based classifiers, to identify different types of syllabus materials on websites. It will provide users with sample course sites, overviews of course topic relationships and schedules, and the ability to populate or search for materials based on topics or templates. The goal is to leverage both human and machine intelligence to maximize the usefulness and accuracy of the system.
We research hierarchy of topics extracted from documents (news, publications, discussions etc.).
Our system is targeted at data researchers.
It provides:
-Trend tracking
-Similar and related topics detection
-Topic segmentation, which aims to solve information overload (http://mlvl.github.io/Hierarchie/) problem
The topic model we use is not a collection of tags but is the combination of NLP + statistical analysis.
The Information Mapping Format: A Proven Content StandardInformation Mapping
The Information Mapping Format: A Proven Content Standard. What makes the Information Mapping format work so well? Learn about the elements of the Information Mapping format that are the keys to making it an internationally recognized content standard.
This document outlines 5 steps to improve an organization's management and reuse of content. The steps are to 1) identify areas that follow similar processes and have them share content and practices, 2) examine shared systems and how consistently they are used, 3) examine how different areas create, store, and manage content, 4) look for high-payoff opportunities to centralize content control, and 5) promote a culture of sharing best practices and improvements. Following these steps can improve employee performance, facilitate compliance, reduce costs, and enhance productivity and efficiency.
The document discusses the three core topic types of concept, task, and reference for organizing information. It describes the characteristics of well-formed topics, including using heading syntax to indicate topic type, focusing on one question, and linking to related topics. The document provides examples of restructuring topics to better fit the core types and improve usability.
Process Re-engineering for Topic Based AuthoringRob Hanna, ECMs
Presented at STC Summit in Atlanta, GA in May 2009.
Presented at Spectrum 2008 in Rochester, NY by Rob Hanna. Discussion of the implied changes moving to a topic-based writing system from a book-based paradigm.
Structured design: Modular style for modern contentChristopher Hess
The document discusses structured content modeling and its relationship to structured design. It advocates for intentionally recoupling structured content and presentation through content modeling. Content models can enable great design by making content clear, useful, and available. The document provides examples of different topic types like informational concepts, tasks, and stories. It emphasizes finding patterns in content, defining types of content structures, and establishing relationships between content types to share the content model.
This document discusses using qualitative research software like WebCT and N6 to collect and analyze online discussion data. It outlines a three stage data collection strategy including open, axial, and selective coding. Advantages of computer assisted qualitative data analysis include organization, systematic approaches, and time savings. Disadvantages include complex software, loss of context, and potential data loss. The document demonstrates exporting discussion data, open coding to develop categories and properties, transforming free nodes to a tree structure, and using text searching to support research variables in analysis.
Using Computer as a Research Assistant in Qualitative ResearchJoshuaApolonio1
This document discusses using qualitative research software to collect and analyze online discussion data. It demonstrates exporting discussion data from WebCT into N6 for coding. A three-stage data collection strategy is outlined, beginning with open coding to generate categories and properties, then axial coding to interconnect categories, and ending with selective coding to build a theoretical model. Advantages of this approach include organization of large data sets and time savings, while disadvantages include complexity of software and potential to lose sight of data contexts.
Presented at DocTrain East 2007 by Joe Gelb, Suite Solutions -- Designing, building and maintaining a coherent information architecture is critical to proper planning, creation, management and delivery of documentation and training content. This is especially true when your content is based on a modular or topic-based model such as DITA and SCORM or if you are migrating to such a model.
But where to start? Terms such as taxonomy, semantics, and ontology can be intimidating, and recognized standards like RDF, OWL, Topic Maps (XTM) and SKOS seem so abstract. This pragmatic workshop will provide an overview of the standards and concepts, and a chance to use them hands-on to turn the abstract into tangible skills. We will demonstrate how a well-designed information architecture facilitates reuse and how the information model is integrally connected to conditional and multi-purpose publishing.
We will introduce an innovative, comprehensive methodology for information modeling and content development called SOTA Solution Oriented Topic Architecture. SOTA does not aim to be yet another new standard, but rather a concrete methodology backed up with open-source and accessible tools for using existing standards. We will demonstrate ֖and practice—hands-on—how this powerful methodology can help you organize and express information, determine which content actually needs to be created or updated, and build documentation and training deliverables from your content based on the rules you define.
This workshop is essential for successfully implementing topic models like DITA and SCORM, multi-purpose conditional publishing, and successfully facilitating content reuse.
This document provides an introduction to NVivo, a qualitative data analysis software. It describes how NVivo can be used to organize, analyze, and find insights in unstructured qualitative data like documents, interviews, and social media posts. The document outlines the basic NVivo workspace and functions for importing data sources, coding data, running queries, and visualizing results. It also provides guidance on setting up an NVivo project and includes some example tasks for getting started with the software.
1. The document summarizes a capstone project on automatic text summarization using both extractive and abstractive techniques.
2. It discusses motivations for summarization, approaches to extractive and abstractive summarization, data collection and analysis, classification methods, and evaluation metrics.
3. The project uses a BBC news dataset and develops sequence-to-sequence and GloVe embedding models to generate abstractive summaries that are evaluated using ROUGE scores against human-written references.
This document provides guidance on writing research articles and publications. It discusses the different types of scientific publications including theses, conference papers, and journal articles. It provides tips for choosing where to publish, including rankings and review processes of journals and conferences. The document also offers guidance on structuring papers, including sections for introduction, related work, methods, and conclusion. It emphasizes writing in a reader-oriented way and provides examples of reference lists and formatting.
This document provides an overview of NVivo and how it can be used for literature reviews. It discusses NVivo as a qualitative data analysis software that allows users to organize and analyze unstructured data. The document then outlines an 8 step process for using NVivo for literature reviews: 1) Create an NVivo project, 2) Import references, 3) Name and classify references, 4) Identify important bits to code, 5) Code them, 6) Combine similar codes, 7) Develop themes, 8) Write up findings while writing memos and using queries. Key functions of NVivo explained include importing data, coding, memo writing, and running queries to facilitate analysis.
Presentation focused on processes used to analyze content for reuse and suggests criteria for rewrite. Includes the content audit and process, content context and an audit that provides a content snap-shot in time.
Topic based and structured authoring - slidesNeil Perlin
The document discusses topic-based and structured authoring approaches. It begins with introductions from the presenter and an overview of the contents. Section 1 defines topic-based authoring as authoring content in topics rather than documents, with each topic answering a single question. Structured authoring is defined as authoring with consistent sectional and stylistic rules. Section 2 discusses rationales for using these approaches, such as flexible reuse of content and consistency. Section 3 covers strategy, such as defining goals, and tactics, including project management, standards, and tools.
Topic based and structured authoring - slidesNeil Perlin
The document discusses topic-based and structured authoring approaches. It begins with introductions from the presenter and an overview of the contents. Section 1 defines topic-based authoring as authoring content in topics rather than documents, with each topic answering a single question. Structured authoring is defined as authoring with consistent sectional and stylistic rules. Section 2 discusses rationales for using these approaches, such as flexible reuse of content and consistency. Section 3 covers strategy, such as defining goals, and tactics like project management and standards.
Basic Usability Survey1. Briefly describe why this document is u.docxgarnerangelika
Basic Usability Survey
1. Briefly describe why this document is used.
2. Evaluate the content:
· Identify any irrelevant information.
· Indicate any gaps in the information.
· Identify any information that seems inaccurate.
· List other problems with the content.
3. Evaluate the organization:
· Identify anything that is out of order or hard to locate or follow.
· List other problems with the organization.
4. Evaluate the style:
· Identify anything you misunderstood on first reading.
· Identify anything you couldn’t understand at all.
· Identify expressions that seem wordy, inexact, or too complex.
· List other problems with the style.
5. Evaluate the design:
· Indicate any headings that are missing, confusing, or excessive.
· Indicate any material that should be designed as a list.
· Give examples of material that might be clarified by a visual.
· Give examples of misleading or overly complex visuals.
· List other problems with design.
6. Identify anything that seems misleading or that could create legal problems or cross-cultural misunderstanding.
7. Please suggest other ways of making this document easier to use.
AUDIENCE ANALYSIS CHART
Aside from purpose, audience is perhaps the most important consideration in planning, writing, reviewing and distributing a technical document. Lack of audience analysis is one of the root causes of most of the problems you find in professional and technical documents—particularly instructions where the smallest mistake can mean a law suit, injury, or death.
Item:
Deliberate and Considered Answer:
Type of Audience:
· Expert
· Novice
· Non-expert
Name this audience:
Audience Background:
· Knowledge
· Experience
· Training
Audience Needs:
· What do they need to know about the topic?
· What might they want to know about the topic?
· What might they already know about the topic?
Writer:
· What do you think they need to know over and above what is already listed in the Needs category?
Adaptations: What additional information do your readers need to know in order to understand the information?
· Definitions of key terms
· Descriptions of tools
· Background information
· Examples
· Graphics/charts/visuals
Design: What design items might you incorporate to help your reader understand the material?
· Shorter sentences
· Chunking information
· Headings
· Subheadings
· Warnings
· Bold text
· Lists
· graphics
· white-space
UNIT 3 DELIVERABLES: Technical Instruction Set
Introduction
One of the most important tasks a technical writer will face is the task of writing a clear set of instructions. Instructions are step-by-step explanations: how to build, operate or repair something, or how to complete a procedure. According to our text, instructions come in three categories:
· General Instructions describing how to assemble something or use something—a toy, a swing set, a book shelf, a DVR, etc.
· Specifications are used by engineers, technicians, and architects to describe in great.
Modular Documentation Joe Gelb Techshoret 2009Suite Solutions
Designing, building and maintaining a coherent content model is critical to proper planning, creation, management and delivery of documentation and training content. This is especially true when implementing a modular or topic-based XML standard such as DITA, SCORM and S1000D, and is essential for successfully facilitating content reuse, multi-purpose conditional publishing and user-driven content.
During this presentation we will review basic concepts and methods for implementing information architecture. We will then introduce an innovative, comprehensive methodology for information modeling and content development that employs recognized XML standards for representation and interchange of knowledge, such as Topic Maps and SKOS. In this way, semantic technologies designed for taxonomy and ontology development can be brought to bear for creating and managing technical documentation and training content, and ultimately impacting the usability and findability of technical information.
The document summarizes key topics from a recommender systems conference, including:
1. Many major companies like Netflix, Quora, and Amazon consider recommendations to be a core part of their user experience.
2. Adaptive and interactive recommendations were discussed, including how Netflix personalizes content rows based on a user's predicted mood.
3. Text modeling algorithms like word2vec were discussed for generating recommendations from content like tweets, search queries, or product descriptions.
The document discusses the three core topic types of concept, task, and reference for organizing information. It describes the characteristics of well-formed topics, including using heading syntax to indicate topic type, focusing on one question, and linking to related topics. The document provides examples of restructuring topics to better fit the core types and improve usability.
Process Re-engineering for Topic Based AuthoringRob Hanna, ECMs
Presented at STC Summit in Atlanta, GA in May 2009.
Presented at Spectrum 2008 in Rochester, NY by Rob Hanna. Discussion of the implied changes moving to a topic-based writing system from a book-based paradigm.
Structured design: Modular style for modern contentChristopher Hess
The document discusses structured content modeling and its relationship to structured design. It advocates for intentionally recoupling structured content and presentation through content modeling. Content models can enable great design by making content clear, useful, and available. The document provides examples of different topic types like informational concepts, tasks, and stories. It emphasizes finding patterns in content, defining types of content structures, and establishing relationships between content types to share the content model.
This document discusses using qualitative research software like WebCT and N6 to collect and analyze online discussion data. It outlines a three stage data collection strategy including open, axial, and selective coding. Advantages of computer assisted qualitative data analysis include organization, systematic approaches, and time savings. Disadvantages include complex software, loss of context, and potential data loss. The document demonstrates exporting discussion data, open coding to develop categories and properties, transforming free nodes to a tree structure, and using text searching to support research variables in analysis.
Using Computer as a Research Assistant in Qualitative ResearchJoshuaApolonio1
This document discusses using qualitative research software to collect and analyze online discussion data. It demonstrates exporting discussion data from WebCT into N6 for coding. A three-stage data collection strategy is outlined, beginning with open coding to generate categories and properties, then axial coding to interconnect categories, and ending with selective coding to build a theoretical model. Advantages of this approach include organization of large data sets and time savings, while disadvantages include complexity of software and potential to lose sight of data contexts.
Presented at DocTrain East 2007 by Joe Gelb, Suite Solutions -- Designing, building and maintaining a coherent information architecture is critical to proper planning, creation, management and delivery of documentation and training content. This is especially true when your content is based on a modular or topic-based model such as DITA and SCORM or if you are migrating to such a model.
But where to start? Terms such as taxonomy, semantics, and ontology can be intimidating, and recognized standards like RDF, OWL, Topic Maps (XTM) and SKOS seem so abstract. This pragmatic workshop will provide an overview of the standards and concepts, and a chance to use them hands-on to turn the abstract into tangible skills. We will demonstrate how a well-designed information architecture facilitates reuse and how the information model is integrally connected to conditional and multi-purpose publishing.
We will introduce an innovative, comprehensive methodology for information modeling and content development called SOTA Solution Oriented Topic Architecture. SOTA does not aim to be yet another new standard, but rather a concrete methodology backed up with open-source and accessible tools for using existing standards. We will demonstrate ֖and practice—hands-on—how this powerful methodology can help you organize and express information, determine which content actually needs to be created or updated, and build documentation and training deliverables from your content based on the rules you define.
This workshop is essential for successfully implementing topic models like DITA and SCORM, multi-purpose conditional publishing, and successfully facilitating content reuse.
This document provides an introduction to NVivo, a qualitative data analysis software. It describes how NVivo can be used to organize, analyze, and find insights in unstructured qualitative data like documents, interviews, and social media posts. The document outlines the basic NVivo workspace and functions for importing data sources, coding data, running queries, and visualizing results. It also provides guidance on setting up an NVivo project and includes some example tasks for getting started with the software.
1. The document summarizes a capstone project on automatic text summarization using both extractive and abstractive techniques.
2. It discusses motivations for summarization, approaches to extractive and abstractive summarization, data collection and analysis, classification methods, and evaluation metrics.
3. The project uses a BBC news dataset and develops sequence-to-sequence and GloVe embedding models to generate abstractive summaries that are evaluated using ROUGE scores against human-written references.
This document provides guidance on writing research articles and publications. It discusses the different types of scientific publications including theses, conference papers, and journal articles. It provides tips for choosing where to publish, including rankings and review processes of journals and conferences. The document also offers guidance on structuring papers, including sections for introduction, related work, methods, and conclusion. It emphasizes writing in a reader-oriented way and provides examples of reference lists and formatting.
This document provides an overview of NVivo and how it can be used for literature reviews. It discusses NVivo as a qualitative data analysis software that allows users to organize and analyze unstructured data. The document then outlines an 8 step process for using NVivo for literature reviews: 1) Create an NVivo project, 2) Import references, 3) Name and classify references, 4) Identify important bits to code, 5) Code them, 6) Combine similar codes, 7) Develop themes, 8) Write up findings while writing memos and using queries. Key functions of NVivo explained include importing data, coding, memo writing, and running queries to facilitate analysis.
Presentation focused on processes used to analyze content for reuse and suggests criteria for rewrite. Includes the content audit and process, content context and an audit that provides a content snap-shot in time.
Topic based and structured authoring - slidesNeil Perlin
The document discusses topic-based and structured authoring approaches. It begins with introductions from the presenter and an overview of the contents. Section 1 defines topic-based authoring as authoring content in topics rather than documents, with each topic answering a single question. Structured authoring is defined as authoring with consistent sectional and stylistic rules. Section 2 discusses rationales for using these approaches, such as flexible reuse of content and consistency. Section 3 covers strategy, such as defining goals, and tactics, including project management, standards, and tools.
Topic based and structured authoring - slidesNeil Perlin
The document discusses topic-based and structured authoring approaches. It begins with introductions from the presenter and an overview of the contents. Section 1 defines topic-based authoring as authoring content in topics rather than documents, with each topic answering a single question. Structured authoring is defined as authoring with consistent sectional and stylistic rules. Section 2 discusses rationales for using these approaches, such as flexible reuse of content and consistency. Section 3 covers strategy, such as defining goals, and tactics like project management and standards.
Basic Usability Survey1. Briefly describe why this document is u.docxgarnerangelika
Basic Usability Survey
1. Briefly describe why this document is used.
2. Evaluate the content:
· Identify any irrelevant information.
· Indicate any gaps in the information.
· Identify any information that seems inaccurate.
· List other problems with the content.
3. Evaluate the organization:
· Identify anything that is out of order or hard to locate or follow.
· List other problems with the organization.
4. Evaluate the style:
· Identify anything you misunderstood on first reading.
· Identify anything you couldn’t understand at all.
· Identify expressions that seem wordy, inexact, or too complex.
· List other problems with the style.
5. Evaluate the design:
· Indicate any headings that are missing, confusing, or excessive.
· Indicate any material that should be designed as a list.
· Give examples of material that might be clarified by a visual.
· Give examples of misleading or overly complex visuals.
· List other problems with design.
6. Identify anything that seems misleading or that could create legal problems or cross-cultural misunderstanding.
7. Please suggest other ways of making this document easier to use.
AUDIENCE ANALYSIS CHART
Aside from purpose, audience is perhaps the most important consideration in planning, writing, reviewing and distributing a technical document. Lack of audience analysis is one of the root causes of most of the problems you find in professional and technical documents—particularly instructions where the smallest mistake can mean a law suit, injury, or death.
Item:
Deliberate and Considered Answer:
Type of Audience:
· Expert
· Novice
· Non-expert
Name this audience:
Audience Background:
· Knowledge
· Experience
· Training
Audience Needs:
· What do they need to know about the topic?
· What might they want to know about the topic?
· What might they already know about the topic?
Writer:
· What do you think they need to know over and above what is already listed in the Needs category?
Adaptations: What additional information do your readers need to know in order to understand the information?
· Definitions of key terms
· Descriptions of tools
· Background information
· Examples
· Graphics/charts/visuals
Design: What design items might you incorporate to help your reader understand the material?
· Shorter sentences
· Chunking information
· Headings
· Subheadings
· Warnings
· Bold text
· Lists
· graphics
· white-space
UNIT 3 DELIVERABLES: Technical Instruction Set
Introduction
One of the most important tasks a technical writer will face is the task of writing a clear set of instructions. Instructions are step-by-step explanations: how to build, operate or repair something, or how to complete a procedure. According to our text, instructions come in three categories:
· General Instructions describing how to assemble something or use something—a toy, a swing set, a book shelf, a DVR, etc.
· Specifications are used by engineers, technicians, and architects to describe in great.
Modular Documentation Joe Gelb Techshoret 2009Suite Solutions
Designing, building and maintaining a coherent content model is critical to proper planning, creation, management and delivery of documentation and training content. This is especially true when implementing a modular or topic-based XML standard such as DITA, SCORM and S1000D, and is essential for successfully facilitating content reuse, multi-purpose conditional publishing and user-driven content.
During this presentation we will review basic concepts and methods for implementing information architecture. We will then introduce an innovative, comprehensive methodology for information modeling and content development that employs recognized XML standards for representation and interchange of knowledge, such as Topic Maps and SKOS. In this way, semantic technologies designed for taxonomy and ontology development can be brought to bear for creating and managing technical documentation and training content, and ultimately impacting the usability and findability of technical information.
The document summarizes key topics from a recommender systems conference, including:
1. Many major companies like Netflix, Quora, and Amazon consider recommendations to be a core part of their user experience.
2. Adaptive and interactive recommendations were discussed, including how Netflix personalizes content rows based on a user's predicted mood.
3. Text modeling algorithms like word2vec were discussed for generating recommendations from content like tweets, search queries, or product descriptions.
2. 2
5/18/2016
What is topic-oriented writing?
An information architecture that:
For users
— Allows easier access and scanability of information
— Helps factor out supporting concepts and reference information, where
they can be read if required and ignored if not (easier to digest)
For writers
— Allows for easy and consistent design of new information
— Ensures the right design gets used for a particular type of information
(step procedures in tasks, but not in concept information)
— Eliminates unimportant or redundant information
— Identifies common or reusable material
— Allows for modular writing and facilitates work by several authors on one
project
— Facilitates the creation of indexing/topic maps
— Provides a structure for books, chapters, and chapter sections
— Allows for a mapping of user stories to topics
— Facilitates the move to XML
3. 3
5/18/2016
Where does topic-oriented writing fit in
and how are topics involved?
Where is topic-oriented writing used?
• With DITA.
What is DITA?
• Darwin Information Typing Architecture is an
XML-based set of design principles for creating
"information-typed" modules at a topic level.
• DITA provides a core Document Type Definition
(DTD) and schema, which define a topic.
• DITA uses “specialization” to handle different
topic types for the content, and different delivery
maps for the output, such as a Help set, a pdf,
or a readme document. Specialization provides
a better, more overt, and more manageable
mechanism for managing both the data and the
mechanisms for transforming the data.
• Conceived over several years at IBM and now
being managed by a technical committee at
OASIS.
4. 4
5/18/2016
Where does topic-oriented writing fit in
and how are topics involved?
What is a topic?
• Unit of information that describes a single task, concept,
or reference item.
How is a topic constructed?
• Starts with a title followed by a mix of text and images.
How is a topic used?
• Topics are organized into container sections (chapters,
maps), and they can also nest.
5. 5
5/18/2016
What is the process used for creating
topic-oriented writing?
1. Determine the topic type for a section or “chunk” of
content (sections of information in a chapter,
heading1 or heading2), using one of three types:
concept, reference, task. Topic types determine the
structure of their content.
2. Write a title for the topic that describes the topic’s
theme and not its category. Verify that each topic
makes sense standing on its own.
3. Write a short description that defines the theme of
topic.
6. 6
5/18/2016
Topic Type—Applying information typing
• What is it? The practice of classifying content by topic types. Content
that answers different kinds of questions is categorized as different
topic types.
• This approach is based on extensive research and experience,
including Robert Horn's Information Mapping, Hughes Aircraft's STOP
(Sequential Thematic Organization of Proposals), and DITA.
Suggestion for division into information topics types:
• Three topic types provided by DITA (concept, task, reference). Other
systems, use six to eight topic types, but all could be narrowed down
to this DITA-based triad.
• Keep topic types as separate entities. Do not place task or reference
content in a concept topic.
7. 7
5/18/2016
Topic Type—Comparing info type schemas
Info Mapping
• Procedure: task, tell how to do something
• Process: tell what happens, how it works
• Structure: show what it looks like; diagrams,
screenshots
• Concept: tell what it is, how it works
• Principle: provide guidelines, requirements,
policies
• Fact: provide specifications
• Classification: explains types and categories
Longhorn Help
• Conceptual: the most generic of all the content types
• FAQ: use to create frequently asked question (FAQ)
documents (reference)
• Glossary: (reference)
• Procedural: use to create topics consisting of one or
more sets of numbered steps that show how to perform
a particular task
• Reference: use to create developer reference topics
(coding, reference)
• Reusable content: use to combine multiple documents
by defining blocks of linkable content
• Troubleshooting: (reference)
• Tutorial: use to provide instructions for performing
tasks that exceed the scope of a procedure. Typically, a
tutorial encompasses two or more shorter procedures
DITA (Darwin Information Typing Architecture)
• Concept: An extended definition
(description) of a topic. Typically a concept
contains a title, some text, and maybe an
example or a graphic.
• Task (or procedure, instruction): A number
of steps, describing how to do something.
• Reference: An overview of the constituent
parts (characteristics) of a product, an
organization, or an application. A reference
contains mostly data-oriented (rather than
text-oriented) information. (This combines the
process, principle, structure, and fact of
InfoMapping.)
8. 8
5/18/2016
Topic Type—Why use DITA topic types?
Easy to structure content into one of three types: concept, task, and reference
information.
This division covers the basic topics we are trying to present in our
documentation, which answer these questions:
1. WHAT IS IT? (Topic title suggestion: How X works)
Concept: Narrative of what it is and how it works, accompanied by process graphics.
2. HOW DO YOU DO IT? (Topic title suggestion: *ing (gerund) an element of X)
Task: Specific step procedures on how to do something, accompanied by screen
shots where appropriate.
3. WHAT DO YOU NEED TO DO WITH IT & WHY?
(Topic title suggestion: The y elements of X)
Reference: Everything that is not concept or task. It could be a set of best practices,
requirements, FAQs, or material for appendixes.
9. 9
5/18/2016
Applying topic titles to bring out the theme
of a topic
Category vs. topical
• Each topic needs a title. Topic titles should not describe
the category but the theme of the topic, taking into
consideration the audience and purpose of the topic.
Thus, the major shift is from a categorical arrangement of
information to a “topical” arrangement of information.
What happens with topical titles:
• the purpose of the topic becomes apparent
• the scope of a group of topics is revealed
• the information can be approached from a non-linear
perspective
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Seeing how topic titles reveal more
information by describing the theme
This is a category TOC
(Note naming which is relevant only within the
linear context and which describes the category
of content. A four-level deep hierarchy.)
Automatic Detection
Introduction
Automatic Detection Techniques
Introduction
External Radar Phenomena
CNFAR Quantization
Multilevel Thresholds
Adaptive Environment Control
Parallel Channels
Adjustments
Pattern Analysis
This is a topical TOC
(Note topic titles that describe the theme. A two-level
deep hierarchy with more top levels to the hierarchy):
Design Approach for Automatic Detection
Automatic Detection Principles
Techniques For Automatic Target Detection
Considerations Of Radar Environment
Hughes Video Processing Methods
Adaptive Control Methods in Automatic Detection
Statistical Video Quantizer Concept
Ability To Verify CNFAR Threshold With Range
Performance Data On Model Statistical Quantizer
Use Of Parallel Channels For Automatic See-Through
Means Of Combating Friendly Interference
Adjusting False Alarm Rate To Obtain See-Above
Adjusting Detection Criteria For Broken-Up Clutter
Pattern Analysis Methods in Automatic Detection
Preventing False Target Reports By Hit-Pattern Analysis
Automatic Reports With Moving-Window Detection
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Advice for writing topic titles
The topic title must characterize and introduce the thematic content and not
merely categorize the content. Topic titles work better if they are constructed
as sentence fragments and rewritten after composition of the content.
Topic containers, the highest level (a book or chapter), can stand as a noun
group: Product X User Guide, as it is not written to. However, for topics,
which are written to, follow these suggestions:
• Always answer the question “What about?” the topic.
— With the chapter heading Basic Concepts ask: What about them?
Change to: Understanding basic concepts or something similar.
• Make topic heading a phrase or sentence fragment of 4-8 words containing:
— Prepositions: Nonsystematic errors => Reduction of nonsystematic
errors
— Infinitives: Ways to simplify antenna design
— Gerunds: Controlling characteristic impedance
• If you can take a position, show attitude with qualitative words:
— Advantages of ….
— Limitations of…
— Pitfalls of …
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Definitions in topic-oriented writing
• Topic Container: a high-level division of information; namely, a
chapter/map. Used as a categorical devise to orient the reader to
the topics.
• Topic: basic standalone unit comprising a topic title and topic
content that may be divided into labeled chunks. A topic conforms
to one of three topic types.
• Topic type: a category or information type that determines the type
of information contained in a topic and the way the information is
presented. We are using three topic types: concept, reference,
task.
• Topic title: a title for a topic, usually Heading1, that describes the
theme and not the category of the topic.
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How to apply topic-oriented writing
to existing material
Take an existing chapter, analyze it, and change it in the
following manner:
• Look at the content in each section divided by headers
and determine the topic types (Concept, Reference, or
Task). Be sure that there is no mixture of types.
• Rewrite topic titles to describe the theme of the topic.
• Use tables, particularly in task procedures and with
reference data, to remove repetitive information.
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Example 1: Redoing a concept topic
Before
Categorical headings
Two or more topics
Diffuse: References to more info
After
Topical heading
Single topic
Concentrated: Self-contained info
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Example 2: Redoing a task topic
Before
Mixture of concept and task
Diffuse: option details interrupts flow of procedures
Repetition
After
Task only
Concentrated: option details do not interrupt flow of
procedures
Repetition avoided through the use of a table
17. DITA Short Description Guidelines
•How do short descriptions work?
•Why care about writing good short descriptions?
•Topic types considered
— Concept
— Task
— Reference
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18. What is the DITA short description?
• A short description is content that appears in the
<shortdesc> DITA element.
• The <shortdesc> element goes directly after the topic title
element.
• Short descriptions serve the following purposes:
— They are the first paragraph in a topic.
— They appear in popup link text when you hover over a link to that
topic.
— They appear after child-link titles.
— They appear in Internet search results.
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23. What to avoid for a short description
• Don’t repeat the title
— What’s the point of simply repeating the title?
• Don’t use a phrase
— All short descriptions must be full sentences to aid translation.
• Don’t say too much
— Short descriptions should be short. Give only enough information so that
the user knows whether to read on. Also, if possible, give just enough
information so that advanced users can get what they need from the
short description and not need to read more.
• Don’t be self-referential
— Don’t refer to the topic in the short description. Example: “This topic will
describe [or discuss or cover] things and stuff.” Short descriptions should
not be self-referential.
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24. What to write for a short description
• Short descriptions should contain 50 words or fewer in
one or two sentences. Try to keep short descriptions to
about 25 words.
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25. Concept topic
Concept short descriptions provide:
— Answers to what is this? or why should I care about this?
— Definitions
Bad
“Crawlers”
— This topic is about crawlers, which are programs that search for
information.
Good
“Crawlers”
— Crawlers are programs that search for information on the Web, in
databases, or in other data sources. The information that the
crawlers gather is added to the search engine index.
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26. Task topic
Task short descriptions define:
— What the topic helps you accomplish
— The benefits of the task
— The purpose of the task
— Situations when the task is useful or necessary
Bad
“Changing data types”
You can use the ALTER NAME statement to change the data type of a
column.
Good
“Changing data types”
You can change the data type of a column so that your data types are
consistent across tables. Use the ALTER NAME statement to change the
data type of a column.
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27. Reference topic
Reference topic short descriptions show:
— What the reference object does
— How the referenced item works
— What the referenced item is used for
Bad
“CatStatsCache log file”
This log file describes the cat statistics cache.
Good
“CatStatsCache log file”
The CatStatsCache log file describes the cache that holds the cat
statistics. You can use the information that is in this log file to find
problems with servers that are in other tiers.
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28. Reference topic: What’s new ….
• A What's new topic describes the latest updates to a product for a specific release. In
the short description, you can mention one or more of the following items:
— Two or three of the most important new features
— Where users can find information about other components in the product
— What component the new features pertain to
Bad
“What's new in Kitty Manager for z/OS?”
Version 8.3 continues to deliver a real return on investment to customers. Version 8.3
focuses on five areas: integration, open systems, autonomic systems, resiliency,
and ease of use. These highlights, and other enhancements to the Version 8.3
product, are summarized below: .
Good
“What's new in Kitty Manager for z/OS?”
Version 8.3 enhancements focus on five areas: integration, open systems, autonomic
systems, resiliency, and ease of use.
.
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29. Reference topic: Troubleshooting
• A troubleshooting container topic should introduce the collection of
troubleshooting topics.
• Container topics serve as navigation aids.
• Provide enough information so that users understand the type of
troubleshooting topics that will follow the container topic.
Bad
“Troubleshooting the resource manager”
This section provides troubleshooting information to help you solve some
common resource manager problems.
Good
“Troubleshooting the resource manager”
Resource manager failures can include problems with the database, secure
sockets layer (SSL), and other component connections of the kitty
management system.
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30. Quiz: Correct the short descriptions
Starting the system administration client on AIX
— You can start the system administration client on AIX.
— You can start the system administration client so that you can manage
your deployment server. Use the cmadmin.sh command to start the
server.
Search collections
— A search collection is a set of data sources that users can search with a
single query. You can build new collections or continue to update existing
collections. A search collection can contain data from the following
sources:
— A search collection is a set of data sources that users can search with a
single query. A search collection can contain data from Web sites, file
systems, and databases.
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31. Quiz: Correct the short descriptions
autorestart - Auto restart enable configuration parameter
— Specifies whether the database manager can automatically call
the restart database utility.
— This parameter determines whether the database manager can
automatically run the restart database utility when an application
connects to a database.
User Preferences: Mail window
— From here, set your mail window preferences.
— Use this window to select a default address book, to change how
you save sent mail, or to specify how you forward and receive
mail automatically.
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