AutoMap User's Guide

AutoMap is a software tool to analyze text using the method of Network Text Analysis. It performs a specific type of Network Text Analysis called Semantic Network Analysis. Semantic analysis extracts and analyzes links among words to model an authors “mental map” as a network of links. Additionally, Automap supports Content Analysis.

Coding in AutoMap is computer-assisted; the software applies a set of coding rules specified by the user in order to code the texts as networks of concepts. Coding texts as maps focuses the user on investigating meaning among texts by finding relationships among words and themes.

The coding rules in AutoMap involve text pre-processing and statement formation, which together form the coding scheme. Text pre-processing condenses data into concepts, which capture the features of the texts relevant to the user. Statement formation rules determine how to link concepts into statements.

Listed below are the steps a user would follow (in typical order) to use AutoMap:

Pre-Process texts.
Perform Semantic Network Analysis on texts.
Run MetaMatrix Text Analysis and Sub Matrix Text Analysis (Both techniques are sub-types of Map Analysis).
Compare Maps generated with AutoMap.
Compute network analytic measures per texts and words.

Network Text Analysis (NTA)

NTA theory is based on the assumption that language and knowledge can be modeled as networks of words and relations. Network Text Analysis encodes links among words to construct a network of linkages. Specifically, Network Text Analysis analyzes the existence, frequencies, and covariance of terms and themes, thus subsuming classical Content Analysis.

Semantic Network Analysis

In map analysis, a concept is a single idea, or ideational kernel, represented by one or more words. Concepts are equivalent to nodes in Social Network Analysis (SNA). The link between two concepts is referred to as a statement, which corresponds with an edge in SNA. The relation between two concepts can differ in strength, directionality, and type. The union of all statements per texts forms a semantic map. Maps are equivalent to networks.

Social Network Analysis (SNA)

Social Network Analysis is a scientific area focused on the study of relations, often defined as social networks. In its basic form, a social network is a network where the nodes are people and the relations (also called links or ties) are a form of connection such as friendship. Social Network Analysis takes graph theoretic ideas and applies them to the social world. The term "social network" was first coined in 1954 by J. A. Barnes (see: Class and Committees in a Norwegian Island Parish). Social network analysis is also called network analysis, structural analysis, and the study of human relations. SNA is often referred to as the science of "connecting the dots."

Today, the term Social Network Analysis (or SNA) is used to refer to the analysis of any network such that all the nodes are of one type (e.g., all people, or all roles, or all organizations), or at most two types (e.g., people and the groups they belong to). The metrics and tools in this area, since they are based on the mathematics of graph theory, are applicable regardless of the type of nodes in the network or the reason for the connections.

For most researchers, the nodes are actors. As such, a network can be a cell of terrorists, employees of global company or simply a group of friends. However, nodes are not limited to actors. A series of computers that interact with each other or a group of interconnected libraries can comprise a network also.

Where to find out more on SNA?

Scott, John, 2000, Social Networks, Sage (2nd edition)
Wasserman, S. & K. Faust, 1994, Social Network Analysis: Methods and Applications.

Dynamic Network Analysis

Dynamic Network Analysis (DNA) is an emergent scientific field that brings together traditional social network analysis (SNA), link analysis (LA) and multi-agent systems (MAS). There are two aspects of this field. The first is the statistical analysis of DNA data. The second is the utilization of simulation to address issues of network dynamics. DNA networks vary from traditional social networks in that are larger dynamic multi-mode, multi-plex networks, and may contain varying levels of uncertainty.

DNA statistical tools are generally optimized for large-scale networks and admit the analysis of multiple networks simultaneously in which, there are multiple types of nodes (multi-node) and multiple types of links (multi-plex).In contrast, SNA statistical tools focus on single or at most two mode data and facilitate the analysis of only one type of link at a time.

DNA statistical tools tend to provide more measures to the user, because they have measures that use data drawn from multiple networks simultaneously. From a computer simulation perspective, nodes in DNA are like atoms in quantum theory, nodes can be, though need not be, treated as probabilistic. Whereas nodes in a traditional SNA model are static, nodes in a DNA model have the ability to learn. Properties change over time; nodes can adapt: A company's employees can learn new skills and increase their value to the network; Or, kill one terrorist and three more are forced to improvise. Change propagates from one node to the next and so on. DNA adds the critical element of a network's evolution and considers the circumstances under which change is likely to occur.

Illustrative problems that people in the DNA area work on -

Developing metrics and statistics to assess and identify change within and across networks.
Developing and validating simulations to study network change, evolution, adaptation, decay...
Developing and validating formal models of network generation and evolution.
Developing and testing theory of network change, evolution, adaptation, decay...
Developing techniques to visualize network change overall or at the node or group level.
Developing statistical techniques to see whether differences observed over time in networks are due to simply different samples from a distribution of links and nodes or changes over time in the underlying distribution of links and nodes.
Developing control processes for networks over time.
Developing algorithms to change distributions of links in networks over time.
Developing algorithms to track groups in networks over time.
Developing tools to extract or locate networks from various data sources such as texts.
Developing statistically valid measurements on networks over time.
Examining the robustness of network metrics under various types of missing data.
Empirical studies of multi-mode multi-link multi-time period networks.
Examining networks as probabilistic time-variant phenomena.
Forecasting change in existing networks Identifying trails through time given a sequence of networks. Identifying changes in node criticality given a sequence of networks anything else related to multi-mode multi-link multi-time period networks.

Automap Graphical User Interface

AutoMap's graphical user interface (GUI) is divided into four primary quadrants (or panels): they will be referred to as P1 (top left), P2 (bottom left), P3 (top right) and P4 (bottom right).

The drop-down menu bar provides access to various analysis tools and utilities. The browse menu bar allows you to quickly navigate between loaded texts.

Index cards, or "tabs," provide a tabular interface allowing you to navigate each panel respectively. The GUI reads any changes dynamically from the XML file. To do that, the user needs to refresh the tool.

The screen shot below highlights primary features of the Automap GUI and where to find them:

It is important to note that P2 can be edited. The other panels (P1, P3, P4) cannot be edited. Information displayed on P1 to P4 always relates to each other. The Text Browse Menu relates to all panels at the same time.

Window sizes do not have an upper threshold. AutoMap will automatically set window size to largest text size upon user’s request. This is a new button on the Analysis Settings panel. This enables text set specific maximum window sizes, which also enhances efficiency.

The Action Tracer Panel in P4 will log preprocessing utilities applied to your text. This is a handy way to keep track of changes and actions relating to your text.

Tool Tips

In the various pre-processing panels, such as utilities, "tool tips," provide more information on certain routines. Tool tips become visible when you slide he mouse over that particular tool.

Examples

This user's guide provides illustrative examples for all AutoMap functions. The sample texts below will be used throughout this guide.

Tip! it is suggested you follow along using the examples below. To do so, simply copy and paste the texts below into wordpad or notepad and save as a .txt file in the same folder.

Our Text Example

Mr. Cray's brown dog ate the lotus blossom at 10 am. Mrs. Brown was unhappy with the dog. She yelled at it saying "You impossible dog!" But the dog kept eating the flowers and weeds. She asked Mr. Cray to stop the dog. He couldn't.
Mrs. Brown planted roses and weeded the garden. The silly dog % dug up the roses looking for a vole on June 12, 1880. Weeding was no longer needed.

Prof. Darren, Mrs. Brown & Mr. Cray met the next day to concoct a plan. John Darren and Mrs. Brown put up a scarecrow. She thought it would scare the dog. Mr. Cray put up a fence. Problem solved. Then Mrs. Brown planted lotus, carnations, daffodils, and roses.

Our Text Example II

Mr. Cray's brown dog stopped eating the lotus blossom at 12 pm the next day. Mrs. Brown was now happy with the dog. She said "You good dog!" The dog no longer ate the flowers and weeds. Mr. Cray was pleased too.

Mrs. Brown watered the roses and fertilized the garden on June 13, 1880.

Prof. Darren, Mrs. Brown & Mr. Cray met over dinner and discussed how the plan had worked. John Darren and Mrs. Brown would take down the scarecrow the following week. She thought it was too scary for the dog. Mr. Cray painted his fence. Then Mrs. Brown watered lotus, carnations, daffodils, and roses.

Load Input

1. To open a single text

To open a single text file into Automap, proceed as follows from the Automap menu bar:

File Open > Open single file

A file chooser will pop up (screen shot below).

Double click on the file that you wish to analyze > Select the Open button.

The text will be displayed in P1 on tab No. 1. Original Text.

The loading of .TXT files (caps) is now enabled.

2. To open a set of texts

Should you wish to analyze multiple texts at the same time, they must be stored in one folder. To do so, proceed as follows from the Automap menu bar:

File menu > Open multiple files

After you select "Open multiple files" a folder chooser will pop up. Again, be sure that correct folder is selected in the folder chooser. The black ellipses in the screen shot below highlight where you should be looking in the file chooser:

Choose the folder that contains the texts you wish to analyze and single click on it. The folder will be highlighted. Do not double click on the folder. Select the Open button (see above screen shot). The first text will be displayed in panel P1, tab no. 1. Original Text . You can browse through the texts by using the Browse Menu.

Text Analysis Utilities

Automap contains text analysis utilities to help you in the pre-processing and data analysis of your text examples. Take time to become familiar with them as they allow you to quickly work with your text examples.

1. Browse through texts
This function enables you to quickly jump from text example to another. All panels are synchronized in the Browse Menu. A series of screen shots below the instruction ad emphasis on how to access text Automap's text browsing features.

How to use the Browse Menu:

To go backward or forward text by text:

Click the ">" button or the "<" button in the browse menu bar.

To go to first or last text in the text set:

Click the ">>" button or the" <<" button.

To go to a specific text:

Enter the text number in text field right next to the Go to command and hit OK.

The name of the currently selected text is displayed on the Browse menu.

These files can be browsed:

All input texts (P1, No. 1. Input Text index card).
All texts after each stage of pre-processing (all index cards of P1).
Concept lists per text (original input text or texts after each stage of pre-processing).
Map (P3) and Statistics (P4) outputs that relate to the text currently displayed on the index cards of P1.

1.1 Example for Browse Menu

The example shows a part of the Our Text 1.txt in panel P1 tab no. 1 Original Texts.

The browse menu tells you several important facts:

The data set opened contains two texts.
The first of the loaded texts is displayed.
The filename of the displayed text.

2. Concept List
The Concept List is displayed in panel P2 tab No. 1 Concept List. The Concept List is created automatically once a text or a set of texts are loaded or modified in Automap.

The concept list tells you several important facts about your text:

The number of concepts found in the text displayed in P1 tab No. 1 Original Text.
Related frequencies of those concepts.
Cumulated unique concepts and total concepts contained in the data set.

Tip! The number of unique concepts considers each concept only once, whereas the number of total concepts considers repetitions of concepts.

By default, the Concept List is sorted by decreasing frequency of concepts. To sort the list alphabetically, click on the first-column header Concept. In order to resort the list, click on the header of the second-column header Frequency.

2.1 Example for Concept List
The example below shows a part of the Concept List for the text displayed in the browse menu. The Table is ordered by Frequency. The concept list contains more entries than the interface can display:

3. Create and refresh Union Concept List

The Union Concept List, found in panel P2, differs from the Concept List (tab no. 1) in that it considers concepts across all texts loaded in Automap, rather than one single text file. There are several key pieces of information the Union Concept List tells you:

Concepts contained in all loaded text sets.
Related, cumulative frequencies of concepts in all text sets.
Cumulative unique and total concepts.

Union Concept List results are displayed on tab No. 2 Union Concept List in P2. However, you must first refresh the union concept list from the file menu, before viewing your results on the o. 2 Union Concept tab. The union concept list can be refreshed after each step of pre-processing in order to visualize the impact of pre-processing operations on the union of concepts.

To refresh the Union Concept, from the drop-down menu bar:

File menu > Refresh Union Concept List.

The ellipse in the screen shot below shows were to access the Refresh Union Concept List from the drop-down menu bar:

The call out box in the screen shot below, shows where to locate the Union Concept tab, which will contain the results of the Refresh Union Concept List analysis:

Your results will be displayed in tab No 2. Union Concept List. The black ellipse in the screen shot below highlights where to find this tab in the Automap GUI.

By default, the list is sorted by decreasing frequency of concepts. In order to sort the list alphabetically click on the first-column header Concept. To re-sort the list, click on the second-header column Frequency.

Note: The number of unique concepts considers each concept only once, whereas the number of total concepts also considers repetitions of concepts.

3.1 Save Union Concept List

To save a Union Concept List follow these steps:

File menu > Save Union Concept List.

A file chooser will pop up. The black ellipse below highlights how to save a Union Concept List from the drop-down menu bar.

3.2 Example for Union Concept List

Let us walk through an example of creating a Union Concept List working with our text examples from above. They are restated below for you convenience:

1) Mr. Cray's brown dog ate the lotus blossom at 10 am. Mrs. Brown was unhappy with the dog. She yelled at it saying "You impossible dog!" But the dog kept eating the flowers and weeds. She asked Mr. Cray to stop the dog. He couldn't. Mrs. Brown planted roses and weeded the garden. The silly dog % dug up the roses looking for a vole on June 12, 1880. Weeding was no longer needed.

Prof. Darren, Mrs. Brown & Mr. Cray met the next day to concoct a plan. John Darren and Mrs. Brown put up a scarecrow. She thought it would scare the dog. Mr. Craye putup a fence. Problem solved. Then Mrs. Brown planted lotus, carnations, daffodils, and roses.

2) Mr. Cray's brown dog stopped eating the lotus blossom at 12 pm the next day. Mrs. Brown was now happy with the dog. She said "You good dog!" The dog no longer ate the flowers and weeds. Mr. Cray was pleased too.

Mrs. Brown watered the roses and fertilized the garden on June 13, 1880.

Prof. Darren, Mrs. Brown & Mr. Cray met over dinner and discussed how the plan had worked. John Darren and Mrs. Brown would take down the scarecrow the following week. She thought it was too scarry for the dog. Mr. Craye painted his fence. Then Mrs. Brown watered lotus, carnations, daffodils, and roses.

The first step is to load in your multiple texts from which we will create and save a Union Concept List. An empty AutoMap GUI is shown below before any text is loaded into it. This is what you will see when you first run AutoMap:

From the drop-down menu:

File > Open Multiple Files > (select location of folder on applicaple drive containing your text examples)

Select "Open"

The screen shot below displays the Automap GUI after our text examples have been loaded into it.

To run the Create and Refresh Union Concept Utility based on multiple texts:

File > Create and Refresh Union Concept List

The following series of screen shots present a step-by-step on how to create an refresh a union concept list:

Select "Save Union Concept List" from the drop-down menu:

The result is shown in the screen shot below:

The table is ordered by Frequency. The concept list contains more entries than the interface can display.
The displayed Union Concept List indicates there are 100 unique concepts and 229 total concepts in the data set.

Text Pre-Processing

1. Introduction to Text Pre-Processing in AutoMap
Pre-processing reduces the data to terms relevant to you.

Tip! All pre-processing techniques in AutoMap are optional.

There are some points to consider before you begin Pre-Processing:

Namely, Map Analysis can be run without any prior data pre-processing.
Meta Matrix Text Analysis and Sub Matrix Text Analysis require pre-processing.

Pre-processing is semi-automated and iterative and involves several key processes:

Named-Entity Recognition
Named-Entity Recognition is an Automap feature that allows you to retrieve proper names (e.g. names of people, organizations, places), numerals, and abbreviations from texts (Magnini, Negri, Prevete & Tanev, 2002). The AutoMap Named-Entity Recognition functionality detects:
- Single words that are capitalized.
  - Example: Copenhagen.
- Adjacent words that are capitalized.
  - Example: The New York City Police Department.
- A string of adjacent words that are capitalized, but can be intervened by one non-capitalized word. The first and the last word in this string are capitalized.
  - Example: Canadian Department of National Defense.
Stemming
Stemming detects inflections and derivations of concepts in order to convert each concept into the related morpheme (Jurafsky & Martin, p.83, 654). AutoMap offers 2 stemmers:

The Porter Stemmer (Porter, 1980). This stemmer uses the Porter Stemming algorithm. Additionally, it converts irregular verbs into the verb's infinitive.
- Example: The phrase "Hospitals switched to using emergency generators," will be stemmed to "Hospit switch to be us emerg gener."
KSTEM or KROVETZ stemmer (Krovetz, 1995). An inflection and derivation-based stemmer. The KSTEM or KROVETZ stemmer can be customized.

Collocation Identification
A word's collocates are words appearing next to or near to it.

Tip! Collocations occuring with high frequency are powerful indicators of a pattern of meaning in a text.

Collocations are helpful to construct thesauri in AutoMap. AutoMap can identify collocations of size 2 (Bigrams) as shown below:

mobile phones
shopping centre

Deletion
Deletion removes non-content bearing conjunctions and articles from texts (Carley, 1993). Non-content bearing concepts to be deleted from the texts are denoted in a Delete List. When applying a Delete List, AutoMap searches the text(s) for concepts specified in the Delete List and delete matches from the text(s). Example:

Original input text: The New York City Police Department said a number of people were trapped in elevators for awhile.
Entries in the Delete List: the, a, of, were, in, for, awhile.
Text after deletion: New York City Police Department said number people trapped elevators.

Thesauri
A thesaurus associates concepts with more abstract concepts. When applying a thesaurus, AutoMap searches the text set for the text-level concepts denoted in the thesaurus and translates matches into the corresponding concept. The terminology of a thesaurus depends on the content and the subject of the data set (Burkart, 1997: 163; Zuell & Alexa, 2001: 313).

Generalization Thesaurus.

A generalization thesaurus typically is a two-columned collection that associates text-level concepts with higher-level concepts. The text-level concepts represent the content of a data set, and the higher-level concepts represent the text-level concepts in a generalized way (Burkart, 1997; Klein 1997: 256; Popping & Roberts 1997: 382).

Example: Copenhagen will be associated with the higher-level concept City.
Related type of Analysis: Map Analysis.

Meta-Matrix Thesaurus

A Meta-Matrix Thesaurus associates text-level concepts with meta-matrix categories. Since one concept might need to be translated into several meta-matrix categories, a meta-matrix thesaurus can consist of more than two columns. For example, the concept “commander” corresponds with the categories agent and knowledge.

Example: City will be associated with and translated into Location.
Related type of Analysis: Meta-Matrix Analysis.

For the meta-matrix thesaurus, column headers start with concept knowledge. The order AND naming of column headers of the meta-matrix thesaurus can be changed in the XML file.

Sub-Matrix Selection. The Sub-Matrix Selection denotes which Meta-Matrix Categories should be retranslated into concepts used as input for the meta-matrix thesaurus.

Example: Location will be selected and translated into Copenhagen, Oskarshamn and Ringhals, if those concepts were associated with the Meta-Matrix category "Location" in the Meta-Matrix Thesaurus.
Related type of Analysis: Sub-Matrix Analysis.

2. Hierarchy of Pre-Processing Techniques

If you apply a pre-processing technique of a lower order prior to a technique of higher order, the pre-processing will be maintained through all following procedures of higher order. You can un-apply each technique after applying it, if needed.

Tip! All pre-processing techniques are optional.

If you wish to apply multiple pre-processing techniques, do this in the following order:

Named-Entity Recognition: This is an utility that does not impact the data. Can be used before any type of analysis is run. Can be used before or after Stemming.
Collocation/ bigram Identification: This is an utility that does not impact the data. Can be used before any type of analysis is run.
Stemming: Can be used before any type of analysis is run. Can be used before or after Named-Entity Recognition.
Deletion: Can be used before any type of analysis is run.
Thesauri:
1. Generalization Thesaurus
  Can be applied before Semantic Network Analysis is run. Can be applied before Meta-Matrix Thesaurus is applied.
2. Meta-Matrix Thesaurus
  Has to be applied if Meta-Matrix Analysis should be run.
3. Sub-Matrix Selection
  Can only be performed if Meta-Matrix Thesaurus was applied. Has to be applied if Sub-Matrix Analysis should be run.
The numbering of the index card tabs on P1 and P2 reflect this hierarchy in order to make the sequence of the pre-processing steps more intuitive.

Numbering of index card tabs on P1:
Numbering of index card tabs on P2:

3. NLP Utilities

3.1 Named-Entity Recognition
More information about Named-Entity Recognition in AutoMap.
To create a list of all Name-Entities that are contained in the data set opened, go to Utilities (tab no. 3) in P2 and click the Create and save Named Entities List button in the Named-Entities Field. The resulting list will be automatically saved under NamedEntities.csv in the root directory of AutoMap.

The Named-Entity Recognition interface:

The black ellipses in the screen shot below below highlight where to find the NamedEntities.csv file in your root directory.

3.1.1 Example for Named-Entity Recognition

Resulting NamedEntities.csv file
after the following Small Predefined Delete List was applied with rhetorical adjacency:

a
an
some
many
this
that
these
those
the
all
one
every

John Darren and Mrs. Brown

Mr. Cray

He couldn't. Mrs. Brown

Prof. Darren Mrs. Brown & Mr. Cray

13-Jun

But

Mr. Cray's

Mr. Craye

Problem solved. Then Mrs. Brown

Then Mrs. Brown

12-Jun

Weeding

She

She asked Mr. Cray

Mrs. Brown

Redundant concepts can be converted to one word by stemming. Concepts not relevant to the user can be eliminated by deletion.

3.2 Symbol Removal

In the lower left panel (P2) you will find an option under tab no. 3 Pre-Processing Utilities labeled on tab no. 1 as Symbol Removal. This routine removes or strips off all characters that are neither a letter nor a number. It maintains sentence marks. It converts question marks and exclamation marks into sentence marks. This helps replace the delchar option on the delete list in a more user-friendly fashion. The overall purpose of this routine is to do a very thorough cleaning of the data in a fully automated, easy to use fashion. This routine can be unapplied by using the “un-apply” button, which is located close the “apply” button.

Symbol Removal

3.21 N-gram Identification: Bigrams

More information about Collocation Identification in AutoMap.
To create a list of all bigrams that are contained in the data set opened, go to the Utilities, tab no. 3, in P2 and select the Create Bigram (Correlation) List button in the N-gram Detection. The resulting list will be automatically saved under CorrelationList.csv in the root directory of AutoMap.

4. Stemming
More information about Stemming.

To stem a text (or text set), go to the Stemming (tab no. 4) in P2.

Porter Stemmer:
To apply the Porter Stemmer select the Apply button next to Porter Stemmer and stemming of irregular verbs for English. The stemmed text(s) will be displayed on the tab no. 2 Stemmed Text in P1.

To unstem the texts, go to tab no. 4. Stemming in P2 and select the Un-Apply button. Tab no. 2. Stemmed Text in P1 will be cleared.

Krovetz Stemmer:

For the Krovetz stemmer, several customization options are offered:

Decide whether capitalized words should be stemmed or not.
Use radio buttons in the interface to make your selection. By default, capitalized words are stemmed.
Define words to be modified by the stemmer.
These words are collected in a protection list, named selfdefined_protected_concepts.txt, stored in the AutoMap root directory under utilities\KStem. To avoid stemming certain words put them in this list, one word per line, without any line delimiter.
Define specific stems for certain words.
These words are collected in a list of pairwise associations, named selfdefined_pairs.txt, which are stored in the AutoMap root directory under utilities\KStem. To stem a certain word into a pre-defined term, put the pair (first word / pre-defined stem) in the list, one pair per line, without any line delimiter. The selfdefined_pairs.txt list that comes along with AutoMap contains already such pairs, which handle the correct stemming or irregular verbs in English.

To apply the Krovetz stemmer:
Select the Apply button next to K-stem. The stemmed text(s) will be displayed on tab no. 2. Stemmed Text index card in P1.

To unstem the texts, go to the tab no. 4 Stemming in P2 and select the Un-Apply button. The tab no. 2 Stemmed Text in P1 will be cleared.

4.1 Example for Stemming (Porter)

Stemmed text in P1 and interface of Stemming index card on P2:

5. Deletion
The Delete List is not case sensitive.
More information about Deletion in AutoMap.
You can use the predefined Delete Lists that AutoMap offers or create your own Delete List. All lists can be edited.

5.1 Open a Delete List
Click the File menu, select Open Delete List and choose one of the following options:

Open from file. A file chooser will appear. Select a delete list and hit the Open button.

Open small predefined Delete List. AutoMap's predefined small delete list will be opened.

Open extensive predefined Delete List. AutoMap's extensive small delete list will be opened.

The black ellipses in the screen shot below shows where to access the Delete List utility:

The Delete List will be displayed in P2, tab no. 5. Delete List index card:

Interface of the Delete List index card:

The Delete List can be edited.

5.1.1 Small predefined Delete List
The Delete List is compiled of words that occur most frequently in English: a, an, and, some, many, this, that, these, those, the, all, one, every.

The Small Delete List can be edited.

5.1.2 Extensive predefined Delete List
An Extensive Delete List is based on words occurring most frequently in English: a, an, and, as, at, but, for, he, her, hers, him, his, i, it, its, me, mine, my, nor, of, or, our, she, so, that, the, their, theirs, them, they, to, us, we, who, whoever, whom, whomever, will, would, you, your, yours, yourself. As the name indicates, the Extensive Delete list contains more words than the Small Predefined Delete List.

The Extensive Delete List can be edited.

5.2 Create a Delete List
There are two ways to create a Delete List:

1. Within AutoMap:
Go to the Delete List index card. The general structure of a Delete List is one single concept per line. Add concepts by typing one concept per line. Hit enter after entering a concept. Avoid empty lines. See the example for more information.

2. Outside of AutoMap:
Use a text editor to create a Delete List. Please consider these instructions to create a Delete List:

The general structure of a Delete List is one single Concept per line.
Avoid empty lines.
The Delete List is not case sensitive.
Save the List.
Open the Delete List in AutoMap.
You can edit the Delete List in AutoMap if you wish.

5.3. Edit a Delete List
On the Delete List index card you can:

Add concepts: Type one concept per line. Hit enter after entering a concept.
Modify concepts: Go the the line and retype concept.
Drop concepts: Mark the concept and hit the delete key.

5.4 Apply a Delete List
If you wish to apply a Delete List and a Thesaurus we recommend first applying a Delete List and then a Thesaurus. Next, follow these steps:

Before applying a delete list, an adjacency option can be chosen on the Delete List index card. Adjacency can be either direct (default) or rhetorical. If the user does not change the adjacency option, AutoMap uses direct adjacency for deletion and analysis.
To delete the concepts specified in the Delete List from all texts loaded click the Apply Delete List button on the Delete Concepts Index card.
See the pre-processed texts in P1, Delete List (tab no. 3).

When applying a Delete List AutoMap does three things:

Search the text(s) for concepts specified in the Delete List.
Delete matches from the text(s).
Display the resulting text(s) in P1, Delete List (tab no. 3).
If direct adjacency was chosen, concepts specified in the delete list are simply deleted from texts and concepts left and right to deleted concepts will appear adjacent to each other in terms of visualization and statement formation.

If rhetorical adjacency was chosen placeholders (xxx) are inserted where a concept was deleted. The placeholders retain original distances of maintained concepts for purposes of visualization and analysis.

To apply multiple delete lists load the first one in, apply it, then load in the next, apply it, and so on.

5.4.1 Direct Adjacency
If direct adjacency is chosen, concepts in the text that match concepts specified in the delete list will be deleted from texts. As a result concepts left and right of a deleted concept move together and will be treated as directly adjacent to each other for visualization and analysis.

To apply direct adjacency check the radio button in the Delete List index card. Then apply the delete list.
If the user does not change the adjacency option, AutoMap uses direct adjacency for deletion and analysis.

5.4.2 Rhetorical adjacency
If rhetorical adjacency is chosen placeholders "xxx" are inserted where a concept was deleted. The placeholders retain the original distances of the maintained concepts visually for analysis.

To apply direct adjacency check the button on the Delete List tab. Then apply the delete list.
If the user does not change the adjacency option, AutoMap uses direct adjacency for deletion and analysis.

5.5 Un-Apply a Delete List
To un-apply a Delete List that was applied to the data, in P2 go to the Delete List (tab no. 5) index card and select the Un-Apply button. The tab no. 3 Delete List index card on P1 will be cleared.

5.6 Save an applied Delete List
To save a Delete List that you have applied to the data, click the File menu, select Save Delete List as. A file chooser will pop up.

5.7 Save text(s) after application of Delete List
To save the text(s) after the application of the Delete List, click the File menu, select Save Text(s) after Delete List applied. All texts are automatically saved in a folder called "preprocessed" in the root directory of AutoMap. The filename will be "after_DL_NameOfYourText.txt".

5.8 Examples for the application of a Delete List

Input text	Tool used	Setting	Resulting text
Mr. Cray's brown dog ate the lotus blossom at 10 am. Mrs. Brown was unhappy with the dog. She yelled at it saying "You impossible dog!" But the dog kept eating the flowers and weeds. She asked Mr. Cray to stop the dog. He couldn't. Mrs. Brown planted roses and weeded the garden. The silly dog % dug up the roses looking for a vole on June 12, 1880. Weeding was no longer needed. Prof. Darren, Mrs. Brown & Mr. Cray met the next day to concoct a plan. John Darren and Mrs. Brown put up a scarecrow. She thought it would scare the dog. Mr. Craye put up a fence. Problem solved. Then Mrs. Brown planted lotus, carnations, daffodils, and roses.	AutoMap's extensive Delete List: a, an, and, as, at, but, for, he, her, hers, him, his, i, it, its, me, mine, my, nor, of, or, our, she, so, that, the, their, theirs, them, they, to, us, we, who, whoever, whom, whomever, will, would, you, your, yours, yourself	Direct Adjacency (default)	Mr. Cray's brown dog ate lotus blossom 10 am. Mrs. Brown was unhappy with dog. yelled saying "You impossible dog!" dog kept eating flowers weeds. asked Mr. Cray stop dog. couldn't. Mrs. Brown planted roses weeded garden. silly dog % dug up roses looking vole on June 12, 1880. Weeding was no longer needed. Prof. Darren, Mrs. Brown & Mr. Cray met next day concoct plan. John Darren Mrs. Brown put up scarecrow. thought scare dog. Mr. Craye put up fence. Problem solved. Then Mrs. Brown planted lotus, carnations, daffodils, roses.
		Rhetorical Adjacency	Mr. Cray's brown dog ate xxx lotus blossom xxx 10 am. Mrs. Brown was unhappy with xxx dog. xxx yelled xxx xxx saying "You impossible dog!" xxx xxx dog kept eating xxx flowers xxx weeds. xxx asked Mr. Cray xxx stop xxx dog. xxx couldn't. Mrs. Brown planted roses xxx weeded xxx garden. xxx silly dog % dug up xxx roses looking xxx xxx vole on June 12, 1880. Weeding was no longer needed. Prof. Darren, Mrs. Brown & Mr. Cray met xxx next day xxx concoct xxx plan. John Darren xxx Mrs. Brown put up xxx scarecrow. xxx thought xxx xxx scare xxx dog. Mr. Craye put up xxx fence. Problem solved. Then Mrs. Brown planted lotus, carnations, daffodils, xxx roses.
	The following concepts were added to the Delete List. Then the Delete List was applied again. from, in, what, was, with	Direct Adjacency (default)	Mr. Cray's brown dog ate lotus blossom 10 am. Mrs. Brown unhappy dog. yelled saying "You impossible dog!" dog kept eating flowers weeds. asked Mr. Cray stop dog. couldn't. Mrs. Brown planted roses weeded garden. silly dog % dug up roses looking vole on June 12, 1880. Weeding no longer needed. Prof. Darren, Mrs. Brown & Mr. Cray met next day concoct plan. John Darren Mrs. Brown put up scarecrow. thought scare dog. Mr. Craye put up fence. Problem solved. Then Mrs. Brown planted lotus, carnations, daffodils, roses.
		Rhetorical Adjacency	Mr. Cray's brown dog ate xxx lotus blossom xxx 10 am. Mrs. Brown xxx unhappy xxx xxx dog. xxx yelled xxx xxx saying "You impossible dog!" xxx xxx dog kept eating xxx flowers xxx weeds. xxx asked Mr. Cray xxx stop xxx dog. xxx couldn't. Mrs. Brown planted roses xxx weeded xxx garden. xxx silly dog % dug up xxx roses looking xxx xxx vole on June 12, 1880. Weeding xxx no longer needed. Prof. Darren, Mrs. Brown & Mr. Cray met xxx next day xxx concoct xxx plan. John Darren xxx Mrs. Brown put up xxx scarecrow. xxx thought xxx xxx scare xxx dog. Mr. Craye put up xxx fence. Problem solved. Then Mrs. Brown planted lotus, carnations, daffodils, xxx roses.

6. Generalization Thesaurus
More information about thesauri.
The Generalization Thesaurus is not case sensitive.

6.1. Open a Generalization Thesaurus
Click the File menu, select Open Generalization Thesaurus. A file chooser will pop up. Double click the thesaurus you wish to wish to open or single click the thesaurus and then hit the Open button. The thesaurus will be displayed on P2, 6. Generalization Thesaurus index card.

Generalization Thesaurus Interface
index card:

6.2. Create a Generalization Thesaurus
There are two ways to create a Thesaurus:

1. Within AutoMap:
Go to P2, (tab no. 6) Generalization Thesaurus (see also the interface of the Generalization Thesaurus index card for an example).
Use the Text Area on this Index card.
Build and edit a thesaurus.

AutoMap supports users in building a generalization thesaurus by loading the union of concepts from the highest level of pre-processing applied into the Generalization Thesaurus field. This is found on the Generalization Thesaurus index card and can be used upon demand.

Follow these steps to load the union concept list into the Generalization Thesaurus field:

Create or refresh the Union Concept List.
Hit the Load Union Concept List button on the Generalization Thesaurus index card.

This concept list loaded into AutoMap can be refined by applying Named-Entity Recognition and Deletion prior to Generalization.

Here is an example for multi-step pre-processing:

To further illustrate multi-step pre-processing techniques, copy the text passages below, then save as a TXT file as "Our Text I.txt" and "Our Text II.txt " respectivley. Load these examples into Automap to follow along.

Input texts:

Our Text I.txt

Mr. Cray's brown dog ate the lotus blossom at 10 am. Mrs. Brown was unhappy with the dog. She yelled at it saying "You impossible dog!" But the dog kept eating the flowers and weeds. She asked Mr. Cray to stop the dog. He couldn't. Mrs. Brown planted roses and weeded the garden. The silly dog % dug up the roses looking for a vole on June 12, 1880. Weeding was no longer needed. Prof. Darren, Mrs. Brown & Mr. Cray met the next day to concoct a plan. John Darren and Mrs. Brown put up a scarecrow. She thought it would scare the dog. Mr. Craye put up a fence. Problem solved. Then Mrs. Brown planted lotus, carnations, daffodils, and roses.

Our Text II.txt:

Mr. Cray's brown dog stopped eating the lotus blossom at 12 pm the next day. Mrs. Brown was now happy with the dog. She said "You good dog!" The dog no longer ate the flowers and weeds. Mr. Cray was pleased too. Mrs. Brown watered the roses and fertilized the garden on June 13, 1880. Prof. Darren, Mrs. Brown & Mr. Cray met over dinner and discussed how the plan had worked. John Darren and Mrs. Brown would take down the scarecrow the following week. She thought it was too scary for the dog. Mr. Craye painted his fence. Then Mrs. Brown watered lotus, carnations, daffodils, and roses.

Pre-processing technique applied

Entries

Result

1. Create Named-Entity List.

John Darren and Mrs. Brown

Mr. Cray

He couldn't. Mrs. Brown

Prof. Darren Mrs. Brown & Mr. Cray

13-Jun

But

Mr. Cray's

Mr. Craye

Problem solved. Then Mrs. Brown

Then Mrs. Brown

12-Jun

Weeding

She

She asked Mr. Cray

Mrs. Brown

2. Create Named-Entity List and use it to build a generalization thesaurus.

Mr. Cray's/Mr. Craye Mr. Craye/Mr. Cray's

3. Add further words that belong together to the generalization thesaurus.

Prof. Darren/Prof_Darren

4. Apply generalization thesaurus (no thesaurus content only).

mr. craye brown dog ate the lotus blossom at 10 am. mrs. brown was unhappy with the dog. she yelled at it saying "you impossible dog!" but the dog kept eating the flowers and weeds. she asked mr. cray to stop the dog. he couldn't. mrs. brown planted roses and weeded the garden. the silly dog % dug up the roses looking for a vole on june 12, 1880. weeding was no longer needed.

mrs. brown & mr. cray, mrs. brown & mr. cray met the next day to concoct a plan. john darren and mrs. brown put up a scarecrow. she thought it would scare the dog. mr. cray's put up a fence. problem solved. brown planted lotus, carnations, daffodils, and roses.

3. Deletion (rhetorical adjacency).

a
an
some
many
this
that
these
those
the
all
one
every

Mr. Cray's brown dog ate xxx lotus blossom at 10 am. Mrs. Brown was unhappy with xxx dog. She yelled at it saying "You impossible dog!" But xxx dog kept eating xxx flowers and weeds. She asked Mr. Cray to stop xxx dog. He couldn't. Mrs. Brown planted roses and weeded xxx garden. xxx silly dog % dug up xxx roses looking for xxx vole on June 12, 1880. Weeding was no longer needed.

Prof. Darren, Mrs. Brown & Mr. Cray met xxx next day to concoct xxx plan. John Darren and Mrs. Brown put up xxx scarecrow. She thought it would scare xxx dog. Mr. Craye put up xxx fence. Problem solved. Then Mrs. Brown planted lotus, carnations, daffodils, and roses.

4. Create Union Concept List.

(Coincides with Union Concept List loaded into AutoMap, see cell below)

5. Load Union Concept List into AutoMap.

1880

asked

blossom

brown

but

concoct

craye

daffodils

dinner

discussed

dog

dog!"

down

dug

fence

for

his

how

looking

lotus

over

plan

problem

said

saying

scary

solved

stopped

take

watered

weeded

weeding

weeds

with

worked

yelled

Outside of AutoMap: Use a text editor to create a Thesaurus.
Build and edit a thesaurus.
Save the Thesaurus.
Open the Thesaurus in AutoMap.
You can edit the Thesaurus in AutoMap if you wish.

6.3 Edit a Generalization Thesaurus

You can add, change or drop the lines of a thesaurus on P2, (tab no. 6) Generalization Thesaurus.

The general structure of a Thesaurus follows the five points below (see also the interface of the Generalization Thesaurus index card for an example):

Every line contains Concept/ Key Concept or in other words Old Word/ New Word.
A Concept can be one or more words.
A Key Concept is one word.
Be sure to separate the words by a slash.
The Thesaurus is not case sensitive.

6.4 Apply a Generalization Thesaurus

If you wish to apply a Delete List and a Generalization Thesaurus please be sure to use the Delete List first and then the Thesaurus. Then go through the following process:

Decide if you want to use the Thesaurus content only option or not. If you do not select the Thesaurus content only option this setting will not be applied.
If you select the Thesaurus content only option you can choose an adjacency option.
Adjacency can be either direct (default) or rhetorical.
To apply your Generalization Thesaurus with the settings you have specified click the Apply Thesaurus button on the Generalization Thesaurus Index card. AutoMap uses the entries in the Thesaurus to search the text(s) for concepts. If a match is found it will be translated into a key concept. Again, the Thesaurus is not case sensitive.
See the pre-processed texts on the P1, (tab no. 4) Generalization Thesaurus.
If the Thesaurus content only option and Direct Adjacency were chosen only key concepts would be displayed and considered for analysis. If the Thesaurus content only option and Rhetorical Adjacency were chosen key concepts and their original distances, which are symbolized by place holders (xxx), are displayed and considered for analysis.

6.4.1 Thesaurus content only
If the Thesaurus content only option is chosen AutoMap performs the following steps:

Search the text(s) for concepts specified in the thesaurus.
Translate matches into key concepts.
Maintain only key concepts in the pre-processed texts. The rest of the input text is dropped and will not be considered for further pre-processing or analysis. The original distances of the key concepts will not be maintained. However, punctuation marks like the end of sentences and paragraphs are maintained and considered for analysis.
As a result, all key concepts in the resulting text appear directly adjacent to each other.

To select the Thesaurus content only option check the Thesaurus content only item on P2, (tab no. 6.) Generalization Thesaurus index card. You can now choose to either use direct or rhetorical adjacency for the application of the Generalization Thesaurus. Then apply the Generalization Thesaurus. To switch from not using the Thesaurus content only option uncheck the Thesaurus content only item on P2, (tab no. 6.) Generalization Thesaurus and apply the Generalization Thesaurus again.

If the Thesaurus content only option is NOT chosen AutoMap performs the following steps:

Search the text(s) for concepts specified in the thesaurus.
Translate matches into key concepts.
Keep the rest of the text as it is. This means, all other concepts in the text that did not match concepts specified in the thesaurus will not be affected in any way. Original distances of both unaffected concepts and key concepts will be maintained. This rule does not apply if a concept consisting of more than one word was translated into a key concept.

The Thesaurus content only item on P2, (tab no. 6) Generalization Thesaurus index card by default is not checked because AutoMap does not apply the Thesaurus content only option. To switch to using the Thesaurus content only option check the Thesaurus content only item on P2, (tab no. 6) Generalization Thesaurus and then apply the Generalization Thesaurus again.

6.4.1.1 Direct Adjacency

Direct adjacency means that original distances of concepts that represent the key concepts will neither be visualized nor considered for analysis.

To choose the direct adjacency click the Direct button in the Adjacency field on P2, (tab no. 6) Generalization Thesaurus index card. Then apply the Generalization Thesaurus. If the user does not change the adjacency option, AutoMap uses direct adjacency for generalization and analysis.

6.4.1.2 Rhetorical Adjacency

Rhetorical adjacency means that the original distance of key concepts will be considered for the analysis. Original distances of concepts that represent the key concepts will be visually symbolized by placeholders (xxx) and considered for analysis. Rhetorical adjacency can only be applied if the Thesaurus content only option was not chosen.

To choose the rhetorical adjacency click the Rhetorical button in the Adjacency field on P2, Generalization Thesaurus (tab no. 6). Then apply the Generalization Thesaurus.
If the user does not change the adjacency option, AutoMap uses direct adjacency for analysis.

6.5 Un-Apply a Generalization Thesaurus

To un-apply a Generalization Thesaurus that was applied to the data, go to P2, Generalization Thesaurus (tab no. 6) and hit the Un-Apply button. The Generalization Thesaurus (Tab no. 4) on P1 will be cleared.

6.6 Save an applied Generalization Thesaurus

To save a Generalization Thesaurus that you have applied to the data, click the File menu, select Save Generalization Thesaurus As ( a file chooser will pop up).

6.7 Save text(s) after application of Generalization Thesaurus

To save the text(s) after the application of the Generalization Thesaurus, click the File menu, select Save Text(s) after Generalization Thesaurus applied. All texts are automatically saved in a folder called "preprocessed" in the root directory of AutoMap. The filename will be after_general_thes_NameOfYourText.txt".

6.8 Example for the building and applying a Generalization Thesaurus

Applying a thesaurus to text that was not pre-processed

Input text	Tool used	Setting	Resulting text
Mr. Cray's brown dog ate the lotus blossom at 10 am. Mrs. Brown was unhappy with the dog. She yelled at it saying "You impossible dog!" But the dog kept eating the flowers and weeds. She asked Mr. Cray to stop the dog. He couldn't. Mrs. Brown planted roses and weeded the garden. The silly dog % dug up the roses looking for a vole on June 12, 1880. Weeding was no longer needed. Prof. Darren, Mrs. Brown & Mr. Cray met the next day to concoct a plan. John Darren and Mrs. Brown put up a scarecrow. She thought it would scare the dog. Mr. Craye put up a fence. Problem solved. Then Mrs. Brown planted lotus, carnations, daffodils, and roses.	Mr Craye/Mr_Cray Mrs Browns/Mrs_Brown John Darren/Prof_John_Darren Prof Darren/Prof_John_Darren yelled/yelling ate/eating	Not Selected - Thesaurus content only (default)	mr. cray's brown dog eating the lotus blossom at 10 am. mrs. brown was unhappy with the dog. she yelling at it saying "you impossible dog!" but the dog kept eating the flowers and weeds. she asked mr. cray to stop the dog. he couldn't. mrs. brown planted roses and weeded the garden. the silly dog % dug up the roses looking for a vole on june 12, 1880. weeding was no longer needed. prof. darren, mrs. brown & mr. cray met the next day to concoct a plan. prof_john_darren and mrs. brown put up a scarecrow. she thought it would scare the dog. mr. craye put up a fence. problem solved. then mrs. brown planted lotus, carnations, daffodils, and roses.
		Thesaurus content only, Direct Adjacency (default if Thesaurus content only is chosen)	. eating... yelling......,.. .,... prof_john_darren.......,,,.
		Selected - Thesaurus content only, Rhetorical Adjacency	xxx. xxx xxx xxx eating xxx xxx xxx xxx xxx xxx. xxx. xxx xxx xxx xxx xxx xxx. xxx yelling xxx xxx xxx xxx xxx xxx xxx xxx xxx xxx xxx xxx xxx xxx xxx. xxx xxx xxx. xxx xxx xxx xxx xxx. xxx xxx. xxx. xxx xxx xxx xxx xxx xxx xxx. xxx xxx xxx xxx xxx xxx xxx xxx xxx xxx xxx xxx xxx, xxx. xxx xxx xxx xxx xxx. xxx. xxx, xxx. xxx xxx. xxx xxx xxx xxx xxx xxx xxx xxx. prof_john_darren xxx xxx. xxx xxx xxx xxx. xxx xxx xxx xxx xxx xxx xxx. xxx. xxx xxx xxx. xxx xxx. xxx xxx. xxx xxx xxx, xxx, xxx, xxx xxx.

Applying a thesaurus to text that was pre-processed with a Delete List, direct adjacency:

Input text	Tool used	Setting	Resulting text
Text after application of customized extensive delete list, direct adjacency: Mr. Cray's brown dog ate lotus blossom 10 am. Mrs. Brown was unhappy with dog. yelled saying "You impossible dog!" dog kept eating flowers weeds. asked Mr. Cray stop dog. couldn't. Mrs. Brown planted roses weeded garden. silly dog % dug up roses looking vole on June 12, 1880. Weeding was no longer needed. Prof. Darren, Mrs. Brown & Mr. Cray met next day concoct plan. John Darren Mrs. Brown put up scarecrow. thought scare dog. Mr. Craye put up fence. Problem solved. Then Mrs. Brown planted lotus, carnations, daffodils, roses.	Thesaurus (same as above)	Not selected - Thesaurus content only (default)	mr. cray's brown dog eating lotus blossom 10 am. mrs. brown was unhappy with dog. yelling saying "you impossible dog!" dog kept eating flowers weeds. asked mr. cray stop dog. couldn't. mrs. brown planted roses weeded garden. silly dog % dug up roses looking vole on june 12, 1880. weeding was no longer needed. prof. darren, mrs. brown & mr. cray met next day concoct plan. prof_john_darren mrs. brown put up scarecrow. thought scare dog. mr. craye put up fence. problem solved. then mrs. brown planted lotus, carnations, daffodils, roses.
		Selected - Thesaurus content only, Direct Adjacency (default if Thesaurus content only is chosen)	. eating... yelling......,.. .,... prof_john_darren.......,,,.
		Thesaurus content only, Rhetorical Adjacency	xxx. xxx xxx xxx eating xxx xxx xxx xxx. xxx. xxx xxx xxx xxx xxx. yelling xxx xxx xxx xxx xxx xxx xxx xxx xxx. xxx xxx. xxx xxx xxx. xxx. xxx. xxx xxx xxx xxx xxx. xxx xxx xxx xxx xxx xxx xxx xxx xxx xxx, xxx. xxx xxx xxx xxx xxx. xxx. xxx, xxx. xxx xxx. xxx xxx xxx xxx xxx xxx. prof_john_darren xxx. xxx xxx xxx xxx. xxx xxx xxx. xxx. xxx xxx xxx. xxx xxx. xxx xxx. xxx xxx xxx, xxx, xxx, xxx.

Applying a thesaurus to text that was pre-processed with a Delete List, rhetorical adjacency:

Input text	Tool used	Setting	Resulting text
Text after application of Customized extensive delete list, rhetorical adjacency: Mr. Cray's brown dog ate xxx lotus blossom xxx 10 am. Mrs. Brown was unhappy with xxx dog. xxx yelled xxx xxx saying "You impossible dog!" xxx xxx dog kept eating xxx flowers xxx weeds. xxx asked Mr. Cray xxx stop xxx dog. xxx couldn't. Mrs. Brown planted roses xxx weeded xxx garden. xxx silly dog % dug up xxx roses looking xxx xxx vole on June 12, 1880. Weeding was no longer needed. Prof. Darren, Mrs. Brown & Mr. Cray met xxx next day xxx concoct xxx plan. John Darren xxx Mrs. Brown put up xxx scarecrow. xxx thought xxx xxx scare xxx dog. Mr. Craye put up xxx fence. Problem solved. Then Mrs. Brown planted lotus, carnations, daffodils, xxx roses..	Thesaurus (same as above)	Not Selected - Thesaurus content only (default)	mr. cray's brown dog eating xxx lotus blossom xxx 10 am. mrs. brown was unhappy with xxx dog. xxx yelling xxx xxx saying "you impossible dog!" xxx xxx dog kept eating xxx flowers xxx weeds. xxx asked mr. cray xxx stop xxx dog. xxx couldn't. mrs. brown planted roses xxx weeded xxx garden. xxx silly dog % dug up xxx roses looking xxx xxx vole on june 12, 1880. weeding was no longer needed. prof. darren, mrs. brown & mr. cray met xxx next day xxx concoct xxx plan. prof_john_darren xxx mrs. brown put up xxx scarecrow. xxx thought xxx xxx scare xxx dog. mr. craye put up xxx fence. problem solved. then mrs. brown planted lotus, carnations, daffodils, xxx roses.
		Thesaurus content only, Direct Adjacency (default if Thesaurus content only is chosen)	. eating... yelling......,.. .,... prof_john_darren.......,,,.
		Thesaurus content only, Rhetorical Adjacency	xxx. xxx xxx xxx eating xxx xxx xxx xxx xxx xxx. xxx. xxx xxx xxx xxx xxx xxx. xxx yelling xxx xxx xxx xxx xxx xxx xxx xxx xxx xxx xxx xxx xxx xxx xxx. xxx xxx xxx. xxx xxx xxx xxx xxx. xxx xxx. xxx. xxx xxx xxx xxx xxx xxx xxx. xxx xxx xxx xxx xxx xxx xxx xxx xxx xxx xxx xxx xxx xxx, xxx. xxx xxx xxx xxx xxx. xxx. xxx, xxx. xxx xxx. xxx xxx xxx xxx xxx xxx xxx xxx xxx. prof_john_darren xxx xxx. xxx xxx xxx xxx xxx. xxx xxx xxx xxx xxx xxx xxx. xxx. xxx xxx xxx xxx. xxx xxx. xxx xxx. xxx xxx xxx, xxx, xxx, xxx xxx.

7. Meta-Matrix Thesaurus
More information about Meta-Matrix Thesaurus.
A Meta-Matrix Thesaurus has to be applied if Meta-Matrix Analysis should be performed.
A Meta-Matrix Thesaurus associates concepts with meta-matrix categories:

Agent
Knowledge
Resource
Task/Event
Organization
Location
Action
Role
Attribute
Any user-defined category (as many as the user defines)

When applying a Meta-Matrix Thesaurus, AutoMap searches the text(s) for the entries specified in the Meta-Matrix Thesaurus and translates matches into related Meta-Matrix categories. If you also want to apply a Delete List or/ and a Generalization Thesaurus you will need to apply these pre-processing tools before the Meta-Matrix Thesaurus.The Meta-Matrix Thesaurus is not case sensitive.

You might also see the meta matrix model as implemented in AutoMap to better understand the meta-matrix.

7.1 Open a Meta-Matrix Thesaurus

Click the File menu, select Open Meta-Matrix Thesaurus and choose Open from highest level of pre-processing.

The union of concepts after the highest level of pre-processing applied so far will be displayed in alphabetical order on P2, Meta-Matrix Thesaurus (tab no. 7) index card.

Note: The Meta-Matrix Thesaurus can be edited.

If you have a pre-defined Meta-Matrix Thesaurus available that matches (some of) the concepts contained in the loaded Meta-Matrix Thesaurus you can open this file.

To do so, click the File menu, select Open Meta-Matrix Thesaurus and choose Open from file.
If a concept contained in the pre-defined file matches a concept in the currently opened Meta-Matrix Thesaurus the meta-matrix categories assigned to this concept in the pre-defined file will be automatically assigned to the concept in the currently opened Meta-Matrix Thesaurus.

Note: The pre-assigned Meta-Matrix Thesaurus can be edited.

7.2 Edit a Meta-Matrix Thesaurus

To each concept that appears in the Concept column of the Meta-Matrix Thesaurus you can assign special categories:

One or multiple meta-matrix categorie