Copyright (C) 2008-2017 Cyrus Shaoul
Permission is granted to copy, distribute and/or modify this document under the terms of the GNU Free Documentation License, Version 1.3 or any later version published by the Free Software Foundation; with no Invariant Sections, no Front-Cover Texts, and no Back-Cover Texts. A copy of the license is included in the section entitled "GNU Free Documentation License" at the end of this file.
HiDEx: The High Dimensional Explorer, A User Manual.
by Cyrus Shaoul
Document Version 0.092 for HiDEx v. 0.092
Updated on April 25th, 2017
Preface: This document is split into two parts: Part 1 is the Quick Start Guide. It is intended to be used by those who have extensive computer experience who want to get HiDEx working as quickly as possible. Part 2 is a detailed description of the software, and includes step-by-step instructions on how to download, build, and use HiDEx including all the settings and how to set them. If you are at all unsure about your expertise, please jump directly to Part 2.
Part 1: Quick Start Guide.
1A) Download the latest version of HiDEx from the HiDEx web site:
Install GIT and then type this command:
git clone https://github.com/cyrus/high-dimensional-explorer.git
NOTE: Make sure that you are on a supported platform and that you have all the required software packages.
1C) Compile the software using
and install it using the command: (Default location is ~/bin, so make sure to add that to your path.)
1D) Get a corpus! There are a few on the Westbury Lab web site:
or for a shortcut, use an example vector set, available at:
1E) Go ahead! Start using HiDEx!
Part 2: User Manual
Table of Contents:
2A) What is HiDEx? What can I measure? 2B) What kind of hardware and software do I need to use HiDEx? 2C) How do I obtain HiDEx? 2D) How do I compile and install HiDEx? 2E) How do I prepare my data before running HiDEx? 2F) How do I use HiDEx to process my corpus? 2G) How do I use HiDEx once I have finished processing my corpus? 2H) How do I set up the configfile config.txt? What settings are available? 2I) How do I improve the performance of HiDEx? Bib) Bibliography
2A) What is HiDEx? What can I measure?
HiDEx is a software system that implements a family word co-occurrence models. These models are similar to those that were introduced by Curt Burgess in 1996 as the HAL model and have been extensively studied since (See the bibliography for a list of papers that will help you understand these models).
If you are unfamiliar with the ideas behind these models, I recommend that you start by reading the following papers listed in the Bibliography:
Shaoul and Westbury, 2010 Shaoul and Westbury, 2006 Bullinaria and Levy, 2008 Bullinaria and Levy, 2012
HiDEx enables you to define the parameters of a general word-space model and then process a corpus of text to create word vectors. It also allows you to perform comparisons of these word vectors and other calculations. HiDEx has been designed to handle very large corpora and very large vectors, and to process them efficiently on systems with large amounts of RAM and multiple CPUs.
NOTE: HiDEx has been tested on English, French, Spanish, Serbian, Estonian, Japanese and Chinese, but it may work on other languages as well. Any language that can be encoded in UTF8 and has spaces between words should work. If there are no spaces between words, you may need to use software to insert spaces. The authors are very interested in how HiDEx works in other languages, so please let us know what you are doing!
Using HiDEx, it is possible to make the following measurements:
-Average Neighbor Similarity (ANS): [Note that in previous versions of HiDEx, this was called the ARC.] This is the average similarity between the word and its neighbors, as defined by the neighborhood threshold. In the case that a word has zero neighbords within the threshold, the ANS will be taken as the similarity between the word and the most similar word outside the threshold.
-Inverse Neighbor Count (InvNCOUNT) The inverse of the number of neighbors.
HiDEx is free software released under the GNU Public License. This means that you are free to change HiDEx to better suit your needs. If you do make improvements, please send them to me so that I can add them to my version of HiDEx and share the improvements with everyone.
2B) What kind of hardware and software do I need to use HiDEx?
HiDEx is not distributed as a pre-compiled program. It comes as many C++ files, and must be compiled by the user before it will work. Here are the hardware and software requirements for parallel and serial operation.
HiDEx does not work well or at all on computers that are underpowered. Please make sure that you have sufficient resources to run this program before continuing with the installation process.
Abbreviations: +Req=Required, +Rec=Recommended
CPU: (Types: PowerPC, Power5, and Intel/AMD x86 CPUs have been tested so far) -For serial operation: *Req: A fast 64-bit CPU. *Rec: The fastest CPU you can get your hands on. HiDEx has not been tested on 32-bit CPUs, but it might work. -For parallel operation: *Req: A computer with more than one core/processor in it, the faster the better. *Rec: A computer with 4 or more cores/processors in it, the faster the better.
Memory: +Req: 2 Gb RAM (the more the better) +Rec: 10 Gb or more of RAM (the more the better)
Hard Disk: +Req: 100 Gb of disk +Rec: 500 Gb or more of VERY FAST DISK (the faster the better)
OS: +Req: MacOS X 10.4.11 or greater with XCode Tools installed. +Req: Linux 2.6.x or greater
Compiler: +Req: g++ 4.2 or greater with libgomp included (For Mac OS X, you may need to download gcc using port, fink or brew)
Text Editor: +Rec: Mac OS X: TextWrangler, BBEdit or any other text editor. +Rec: Linux: emacs, vi or other text editor
Dependent Software Libraries: The GNU Unicode String Libraries On Linux and Mac, please install this package:
% sudo apt-get install libunistring-dev
2C) How do I obtain HiDEx?
Go to this web site and download the software:
Please make sure that you read and understand the restrictions that govern your use of the software. Read the file COPYING.txt, as it is written in English, not legal gobbledygook.
2D) How do I compile and install HiDEx?
Step 1: Move the TGZ file that you just downloaded to the correct directory. (I recommend the ~/src directory).
Or get the latest version from the GIT repo:
git clone https://github.com/cyrus/high-dimensional-explorer.git
Step 2: Start up a terminal application (Mac OS X: Terminal, Linux: xterm). Change to the correct directory:
Step 3: Unpack the software in a good location To unpack it, use this command:
tar xzf hidex.[version].tgz
Step 4: Change directory to the newly created directory:
Step 5: Compile the program (Make sure that your system has all the required software listed in section 2B first, in particular, the new compiler).
Step 6: Create a directory for the binary if you do not already have one. (I recommend ~/bin)
Step 7: Using a text editor, edit the file called "Makefile" to have the correct value for BINDIR (in this case, ~/bin). Then install the program using the following command:
Step 8: Add the BINDIR to the PATH environment variable of your shell, if it is not already included. Reload your PATH or start a new terminal process. If you don't much about the shell, and the PATH environment variable, please read the following web page:
That is it! Take a break! Stretch out your tired finger muscles!
WARNING: This is the first public release of HiDEx, so there may still be some bugs in the above process. If you get any error messages when doing the above steps, please post your questions to Cyrus Shaoul using his web site:
2E) How do I prepare my data before running HiDEx?
There are two choices for getting co-occurrence data when using HiDEx: use your own corpus or downloading a pre-built set of co-occurrence data. First I will describe the process of using a corpus, and then I will explain how download and install a set of vectors from the Internet.
The amount of processing power, hard disk and processing time required to process a corpus is large, but feasible on a modern workstation or server. On most computers I have tested it will take approximately three days to process a 1 billion word corpus. The raw matrix that HiDEx creates from that corpus will be 60Gb in size or larger. Once this process is done once, it will not have to be done again for that corpus.
Step 1: Obtain or download a large corpus of text. I have created a very large corpus of USENET text that is available freely under a Creative Commons license. It is available here:
Step 2: If the corpus is not the above corpus, please change the corpus so that it follows the following format :
[Text] [Document Separator] [Text] [Document Separator] [Text] [Document Separator]
Choose a very uncommon string to act as your document separator. I recommend:
Make sure that there is one of these between every document in your corpus. Do not worry about capitalization, punctuation, numbers or other symbols, as they will be removed by HiDEx. Do make sure to remove any markup (such as SGML, HTML or other markup language with tags).
If possible, remove any redundant, non-content text (such as "Courtesy of the XYZ News Wire Service") if it is in all the documents.
NEW in version 0.06:
For Unicode support to work, all your corpora and word lists must be encoded as UTF-8 in unix-type text files. Legacy encodings are not supported. There are many tools for converting files to UTF-8, and I have used iconv with some success.
Step 3: If you corpus is contained in many separate files, concatenate them into one large file. For example the command:
cat *.txt > corpus.txt
will concatenate all the files with the extension "txt" in a directory into a new file.
Step 4: Put the corpus file in your working directory. I recommend creating a new one for using with HiDEx. These commands will do it for you.
mkdir ~/work mv corpus.txt ~/work
Step 5: Choose a lexicon to use with HiDEx. This is a file that contains all the words that will be analyzed for co-occurrence. If you do not have one, and need one for the English language, I recommend using one available here under a Creative Commons License:
Make sure to remove the frequency data from this file before using the above file.
I also recommend reducing the size of the lexicon to 30,000 to 70,000 words for performance reasons. You can do this by removing the words with the n lowest frequencies. Please see section 2I for information on how lexicon size impacts HiDEx's performance.
Once your lexicon is ready, put it in your work directory like so:
mv lexicon.txt ~/work
Now you are ready to process your corpus.
2F) How do I use HiDEx to process my corpus?
Intro: HiDEx is a program that has many different modes of operation. From this point onwards you will be specifying the mode that you would like HiDEx to use by typing "-m" and then the name of the mode.
Also, HiDEx has many parameters that change how it operates. These parameters are set inside a text file called the configfile. A sample configfile is provided with the software. Before doing any work, I recommend copying the sample configfile to your work directory:
cp ~/src/hidex/sample.config.txt ~/work/config.txt
If no configfile is specified on the command line, a file in the current directory called config.txt is used. You can specify an alternate name and location of the configfile by using the "-f" option. For example
hidex -f special.config.txt
Step 1: Edit the configfile to make the correct settings. Edit the "config.txt" file in your favorite text editor. The list of possible settings and their implications are described in the documentation in the "config.txt" file.
Step 2: Create an empty data-store using this command:
cd ~/work hidex -m create
Step 3: Process the corpus. The "update" command will create a sparse matrix representation of all the lexical co-occurrences in the corpus. WARNING: depending on the size of your corpus and your parameter settings, this process can take a long time. Please see section 2I for information on this topic.
hidex -m update
ALTERNATIVE METHOD: Download a vector set.
If are unable to obtain and process a corpus, please download a vector set from the following URL:
To install this vector set, please read the instructions that are included with the download.
2G) How do I use HiDEx once I have finished processing my corpus?
Once the corpus co-occurrence data has been collected, you can begin analyzing it using several of HiDEx's other modes.
There are two types of calculations: vector aggregation and vector-based metrics. Vector aggregation must take place before metric calculation, and can be quite time-consuming. For this reason, HiDEx can save the results of aggregation (called the Global Co-occurrence Matrix, or GCM) in the work directory.
Each time a GCM is needed, HiDEx will seek and reuse a GCM saved in the work directory. This feature is enabled by using the saveGCM setting in the config file. Please see the config file documentation for more info.
Warning: On some computers it may be faster to NOT save the GCM and recalculate the GCM each time. Please try both modes to compare.
NOTE: The output of the following commands will depend on the settings you have made in the configuration file. Please see section 2H for an explanation of all the settings possible.
To get neighborhoods in a word space, use the "getneighbors" mode, as in the following example:
hidex -m getneighbors
NOTE: There are two numbers reported for each word of interest: 1) ANS: Average Neighbour Similarity (The average similarity between the word and its neighbors that are defined by the threshold. 2) InvNCOUNT: The inverse of the number of neighbors (+1).
To find the similarity between pairs of words, use the "getsimilarity":
hidex -m getsimilarity
To output specific vectors from the GCM in a dense matrix format text file, use the "getvectors" mode:
hidex -m getvectors
To run a large batch of models and find neighborhoods, use the "batchneighbors" mode: [Currently under development. Please do not use!]
hidex -m batchneighbors
To run a large batch of models and find inter-word distances, use the "batchsimilarity" mode: [Currently under development. Please do not use!]
hidex -m batchsimilarity
For all of these commands, the output will be saved in a new folder called "output.date.time". This folder will be created in the current directory, or if you have specified a different output directory in the configfile, in that directory.
To add more text to your co-occurrence database, you can change the corpus filename to the new corpus that you would like to add, and then run this command again:
hidex -m update
To remove a database and all the auxiliary files that are related to it use the following command:
hidex -m remove
WARNING: The above command is NOT reversible. Once you have removed the database, it is gone forever. You have been warned! :-)
2H) How do I set up the configfile config.txt? What settings are available?
The section explains all the settings available in the configfile and what options are possible for each setting. The default name of the config file is "config.txt" and the default location is the current directory. To specify the name and location of the configfile, use the following command:
hidex -f alternate.config.file.txt -m getneighbors
[General Options] dbname: This is the name to use for the database and all of its auxiliary files. You can store multiple databases in one directory and pick which one to by changing this parameter.
(dbpath): (Optional setting). Used to specify an absolute path for the database. Avoid using this setting unless you need to.
dictFilename: Name of the lexicon file, assumed to be in the Working Directory. Format is a text file with one word per line.
[Corpus Processing Options] maxWindowBehind: Maximum size of word window behind target word. maxWindowAhead: Maximum size of word window ahead of target word. These two parameters change the size of the database, as they limit how much context information is saved. The actual size of the window used during vector aggregation is set using the windowLenBehind setting below.
corpusFilename: The filename of the corpus. Assumed to be in the Working Directory. The format is described in section 2E of this manual.
stepsize: The number of vectors to collect per pass through the corpus. If you run out of memory during an "update", reduce this value. If you find that you have more RAM to spare when running update, increase this value. A rough guide to help you set this: a stepsize of 5000 will use 4Gb of RAM, and RAM usage increases linearly as stepsize increases.
eod: End of Document marker. Used when processing the corpus. HiDEx does not count co-occurrence accross document boundaries. See section 2E for details.
(outputpath): (Optional!) An absolute path for program output. HiDEx defaults to the Working Directory is this is not set.
metric: Choose your similarity metric here. If it is blank, HiDEx will default to using (inverse) Euclidean distance. Other possible values are Cosine, CityBlock and Correlation. For more information on these metrics, please see the references listed in the Bibliography.
normalization: Choose your method for vector normalization here. Default is co-occurrence divided by target word frequency. Other possibilities are PPMI (Positive Pointwise Mutual Information) and Correlation. For more information on these normalization methods, please see the references listed in the Bibliography.
weightingScheme: Code number to choose a weighting scheme. The names are fairly self explanatory! The original HAL model used #1, Ramped Linear Weighting.
FLAT = 0 RAMPED LINEAR = 1 RAMPED QUADRATIC = 2 FORWARD RAMP = 3 BACKWARD RAMP = 4 INVERSE RAMP = 5 INVERSE QUADRATIC = 6 SECOND WORD = 7 THIRD WORD = 8 FOURTH WORD = 9
contextSize: The number of dimensions to use in the co-occurrence vector. If contextSize is set to N, only the N most frequent word's vectors are included in the final GCM.
windowLenBehind: Size of the window behind the target word. Can be any number from 1 to MaxWindowLenBehind.
windowLenAhead: Size of the window behind the target word. Can be any number from 1 to MaxWindowLenAhead.
useThreshold: Setting to turn on z-score based neighborhood membership thresholds (words more that 1 standard deviation similar than the average similarity. 1 means it is ON, 0 means it is OFF.
percenttosample: When using thresholds, the percent of pairs to sample during the calculation of the z-scores. 0.1 means 10%.
neighbourhoodSize: When not using neighborhood thresholds, the number of neighbors to find.
separate: Enables separate forward and backward vectors when set to 1. When set to 0, Forward and Backward vectors are combined, halving the context size.
wordlistfilename: List of words to analyze (it will usually be a list of words, one per line. Only when using getsimilarity mode will you need to proved a list of word pairs, two words per line, separated by tabs.)
wordlistsize: Used for picking random sub-samples from a word list. If this number is smaller than the number of words in the wordlist, HiDEx will pick a random subset of the words in the wordlistfile to use. If this number is larger than the number of words in the wordlistfile, or if the setting is left blank, HiDEx will use all the words in the file set by wordlistfilename.
saveGCM: If this is set to "1", HiDEx will create a large text file called "dbname.gcm" in the working directory. This file will be automatically detected the next time a GCM is required, and loaded. Depending on the speed of your computer, this may be faster or slower than building the GCM on the fly.
With the addition of Unicode support, there are two new options
normCase: Apply case normalization globally. This option will take any Unicode character than can have a lower-case glyph, and replace it with that glyph. The default mode is 1 (true). To stop case normalization, set this to 0 (false). There is one more important factor to correct case normalization: your shell's environment variable settings for the LANG variable. HiDEx will check this setting and appy the correct normalization only if this is set to the correct language. For example, if you are processing German, your LANG setting should be set to de_DE.UTF-8
englishContractions: In English, the string "'s" is a contraction that can make it difficult for the model to recognize words that are possessives. This defaults to 1 (true), which means that words like "ball's" will be split into two words, "ball" and "'s" and "can't" will become "can" and "'nt". englishContractions=1
NEW OPTIONS in vesion 0.07:
useVariance: In previous versions of HiDEx, the context was created from the N most frequent words in the corpus. In the original HAL paper (Lund & Burgess, 1996), the vectors for the N most variant words were used instead. This feature implements the method from Lund & Burgess (1996).
The default value for this is "0", meaning that variance will not be used. If it is set to "1", it must be enabled at the time of creation of the co-occurrence database. It will not work with databases created with versions of HiDEx before version 0.06, or with new databases that were not created with useVariance = 1.
2I) How do I improve the performance of HiDEx?
The simplest way to improve performance is to run HiDEx on a computer with more CPUs and faster CPUs, more memory and faster disk. Make sure to use OpenMP, and set the number of threads equal to the number of cores available.
Certain configuration options have a very strong influence on the performance of HiDEx. Here is a summary of the most important ones:
Lexicon Size: The larger the lexicon, the larger the matrix (Each extra word in the lexicon adds an extra dimension to the representation). A lexicon with double the number of words in it can cause a 400% increase in storage space needed for the raw co-occurrence data. Also, the lexicon size will increase the size of the GCM, which will slow down all metric calculations. I normally use a lexicon of around 50,000 words in size.
maxWindowBehind and maxWindowAhead: Large values for these variable will greatly slow down the processing of the corpus and the aggregation of the GCM.
stepsize: This is a memory-usage tuning setting, and must be set carefully. During corpus processing, large amounts of data must be kept in RAM. There is no way that I know to predict the sparseness of the database, and that means there is no way to predict the memory usage of HiDEx when processing a corpus. The default setting is very conservative, and should work for computers that have 4Gb of RAM. If you have more RAM than 4Gb, please try larger values, but be careful to monitor the memory usage of the HiDEx process. If HiDEx tries to use more memory than your computer has, it will begin using virtual memory (and cause enormous amounts of swapping) and your corpus processing will take months instead of hours.
contextSize: The larger the context size, the greater the memory usage, and the greater the computation required. I recommend using context sizes of 10,000 or greater.
++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++ Thanks for reading the manual. If you any comments about it, please send them to firstname.lastname@example.org ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
Buchanan, L., Westbury, C., & Burgess, C. (2001). Characterizing semantic space: Neighborhood effects in word recognition. Psychonomic Bulletin & Review, 8, 531-544.
Bullinaria, J., & Levy, J. (2007). Extracting semantic representations from word co-occurrence statistics: A computational study. Behavior Research Methods, 39, 510-526.
Bullinaria, J.A. & Levy, J.P. (2012). Extracting Semantic Representations from Word Co-occurrence Statistics: Stop-lists, Stemming and SVD. Behavior Research Methods, 44, 890-907.
Lund, K., & Burgess, C. (1996). Producing high-dimensional semantic spaces from lexical co-occurrence. Behavior Research Methods, Instrumentation, and Computers, 28, 203-208.
Rohde, D. L. T., Gonnerman, L., and Plaut, D. C. (submitted). An improved model of semantic similarity based on lexical co-Occurrence. Cognitive Science. http://www.cnbc.cmu.edu/~plaut/papers/abstracts/RohdeGonnermanPlautSUB-CogSci.COALS.html
Shaoul, C., & Westbury, C. (2006). Word frequency effects in high-dimensional co-occurrence models: A new approach. Behavior Research Methods, 38, 190-195.
Shaoul, C., & Westbury, C. (2007). A usenet corpus (2005-2008) (Tech. Rep.). Edmonton, AB: University of Alberta. Downloaded from http://www.psych.ualberta.ca/~westburylab/downloads/usenetcorpus.download.html
Shaoul, C. & Westbury, C. (2010). Exploring lexical co-occurrence space using HiDEx. Behavior Research Methods, 42:2, 393-413.
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A. Use in the Title Page (and on the covers, if any) a title distinct from that of the Document, and from those of previous versions (which should, if there were any, be listed in the History section of the Document). You may use the same title as a previous version if the original publisher of that version gives permission. B. List on the Title Page, as authors, one or more persons or entities responsible for authorship of the modifications in the Modified Version, together with at least five of the principal authors of the Document (all of its principal authors, if it has fewer than five), unless they release you from this requirement. C. State on the Title page the name of the publisher of the Modified Version, as the publisher. D. Preserve all the copyright notices of the Document. E. Add an appropriate copyright notice for your modifications adjacent to the other copyright notices. F. Include, immediately after the copyright notices, a license notice giving the public permission to use the Modified Version under the terms of this License, in the form shown in the Addendum below. G. Preserve in that license notice the full lists of Invariant Sections and required Cover Texts given in the Document's license notice. H. Include an unaltered copy of this License. I. Preserve the section Entitled "History", Preserve its Title, and add to it an item stating at least the title, year, new authors, and publisher of the Modified Version as given on the Title Page. If there is no section Entitled "History" in the Document, create one stating the title, year, authors, and publisher of the Document as given on its Title Page, then add an item describing the Modified Version as stated in the previous sentence. J. Preserve the network location, if any, given in the Document for public access to a Transparent copy of the Document, and likewise the network locations given in the Document for previous versions it was based on. These may be placed in the "History" section. You may omit a network location for a work that was published at least four years before the Document itself, or if the original publisher of the version it refers to gives permission. K. For any section Entitled "Acknowledgements" or "Dedications", Preserve the Title of the section, and preserve in the section all the substance and tone of each of the contributor acknowledgements and/or dedications given therein. L. Preserve all the Invariant Sections of the Document, unaltered in their text and in their titles. Section numbers or the equivalent are not considered part of the section titles. M. Delete any section Entitled "Endorsements". Such a section may not be included in the Modified Version. N. Do not retitle any existing section to be Entitled "Endorsements" or to conflict in title with any Invariant Section. O. Preserve any Warranty Disclaimers.
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- COMBINING DOCUMENTS
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In the combination, you must combine any sections Entitled "History" in the various original documents, forming one section Entitled "History"; likewise combine any sections Entitled "Acknowledgements", and any sections Entitled "Dedications". You must delete all sections Entitled "Endorsements".
- COLLECTIONS OF DOCUMENTS
You may make a collection consisting of the Document and other documents released under this License, and replace the individual copies of this License in the various documents with a single copy that is included in the collection, provided that you follow the rules of this License for verbatim copying of each of the documents in all other respects.
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- AGGREGATION WITH INDEPENDENT WORKS
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If the Cover Text requirement of section 3 is applicable to these copies of the Document, then if the Document is less than one half of the entire aggregate, the Document's Cover Texts may be placed on covers that bracket the Document within the aggregate, or the electronic equivalent of covers if the Document is in electronic form. Otherwise they must appear on printed covers that bracket the whole aggregate.
Translation is considered a kind of modification, so you may distribute translations of the Document under the terms of section 4. Replacing Invariant Sections with translations requires special permission from their copyright holders, but you may include translations of some or all Invariant Sections in addition to the original versions of these Invariant Sections. You may include a translation of this License, and all the license notices in the Document, and any Warranty Disclaimers, provided that you also include the original English version of this License and the original versions of those notices and disclaimers. In case of a disagreement between the translation and the original version of this License or a notice or disclaimer, the original version will prevail.
If a section in the Document is Entitled "Acknowledgements", "Dedications", or "History", the requirement (section 4) to Preserve its Title (section 1) will typically require changing the actual title.
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- FUTURE REVISIONS OF THIS LICENSE
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