A system for connecting language to space and time.
Scala Python Shell Java
Fetching latest commit…
Cannot retrieve the latest commit at this time.
Permalink
Failed to load latest commit information.
bin
data
lib
project
src
.gitignore
CHANGES
LICENSE.txt
README.geolocate
README.poligrounder
README.preprocess
README.txt
THIS.IS.TEXTGROUNDER
build.sbt
pom.xml

README.geolocate

See `README.textgrounder` for an introduction to TextGrounder and to the
Geolocate subproject.  This file describes how exactly to run the
applications in Geolocate.

========================================
Specifying where the corpora are located
========================================

The corpora are located using the environment variable TG_CORPUS_DIR.
If this is unset, and TG_GROUPS_DIR is set, it will be initialized to
the 'corpora' subdirectory of this variable.  If you are running
directly on Maverick, you can set TG_ON_MAVERICK to 'yes', which will
set things up appropriately to use the corpora located in
/work/01683/benwing/maverick/corpora.

====================
Running under Hadoop
====================

To run Geolocate under Hadoop with data in HDFS rather then locally, you
will need to copy the data into HDFS, which you can do using
'tg-copy-data-to-hadoop'.  You need to copy two things, the corpus or corpora
you want to run on and some ancillary data needed for TextGrounder (basically
the stop lists).  You run the command as follows:

$ tg-copy-data-to-hadoop CORPUS ...

where CORPUS is the name of a corpus, similar to what is specified when
running 'tg-geolocate'.  Additionally, the pseudo-corpus 'textgrounder'
will copy the ancillary TextGrounder data.  For example, to copy
the Portuguese Wikipedia for March 15, 2012, as well as the ancillary data,
run the following:

$ tg-copy-data-to-hadoop textgrounder ptwiki-20120315

Other possibilities for CORPUS are 'geotext' (the Twitter GeoText corpus),
any other corpus listed in the corpus directory (TG_CORPUS_DIR), any
tree containing corpora (all corpora underneath will be copied and the
directory structure preserved), or any absolute path to a corpus or
tree of corpora.

Then, run as follows:

$ TG_USE_HDFS=yes tg-geolocate --hadoop geotext output

If you use '--verbose' as follows, you can see exactly which options are
being passed to the underlying 'textgrounder' script:

$ TG_USE_HDFS=yes tg-geolocate --hadoop --verbose geotext output

By default, the data copied using 'tg-copy-data-to-hadoop' and referenced
by 'tg-geolocate' or 'textgrounder' is placed in the directory
'textgrounder-data' under your home directory on HDFS.  You can change this
by setting TG_HADOOP_DIR before running 'tg-copy-data-to-hadoop'.

==================
Obtaining the Data
==================

If you don't have the data already (you do if you have access to the Comp
Ling machines), download and unzip the processed Wikipedia/Twitter data
from http://web.corral.tacc.utexas.edu/utcompling/wing-baldridge-2011/.

There are two sets of data to download:
  * The processed Wikipedia data, in `enwiki-20100905/`. You need at least the
    files ending in '.data.txt.bz2' and '.schema.txt'.
  * The processed Twitter data, in `twitter-geotext/'. You need at least the
    subdirectories 'docthresh-*'.

Download the data files. It is generally recommended that you create
a directory and set `TG_GROUPS_DIR` to point to it; then put the Wikipedia
and Twitter data underneath the `$TG_GROUPS_DIR/corpora` subdirectory.
Alternatively, `TG_CORPUS_DIR` can be used to directly point to where the
corpora are stored. In both cases, the directory storing the corpora should
have subdirectories 'enwiki-20100905' and 'twitter-geotext'; underneath the
former should be the downloaded '*.data.txt.bz2' and '*.schema.txt' files,
and underneath the latter should be the 'docthresh-*' subdirectories.

(Alternatively, if you are running on a UTexas Comp Ling machine, or a
machine with a copy of the relevant portions of /groups/corpora and
/groups/projects in the same places, set `TG_ON_COMP_LING_MACHINES` to a
non-empty value and it will initialize those three for you.  If you are
running on the UTexas Maverick cluster, set `TG_ON_MAVERICK` to a non-empty
value and the variables will be initialized appropriately for Maverick.)

The Wikipedia data was generated from [http://download.wikimedia.org/enwiki/20100904/enwiki-20100904-pages-articles.xml.bz2 the original English-language Wikipedia dump of September 4, 2010].

The Twitter data was generated from [http://www.ark.cs.cmu.edu/GeoText/ The Geo-tagged Microblog corpus] created by [http://aclweb.org/anthology-new/D/D10/D10-1124.pdf Eisenstein et al (2010)].

===========================
Replicating the experiments
===========================

The code in Geolocate.scala does the actual geolocating.  Although these
are written in Java and can conceivably be run directly using `java`,
in practice it's much more convenient using either the `textgrounder`
driver script or some other even higher-level front-end script.
`textgrounder` sets up the paths correctly so that all libraries, etc.
will be found, and takes an application to run, knowing how to map that
application to the actual class that implements the application.  Each
application typically takes various command-line arguments, and
`textgrounder` itself also takes various command-line options (given
*before* the application name), which mostly control operation of the
JVM.

In this case, document geotagging can be invoked directly with `textgrounder`
using `textgrounder geolocate-document`, but the normal route is to
go through a front-end script.  The following is a list of the front-end
scripts available:

  * `tg-geolocate` is the script you probably want to use.  It takes a
    CORPUS parameter to specify which corpus you want to act on (currently
    recognized: `geotext`; a Wikipedia corpus, e.g. `enwiki-20120307` or
    `ptwiki-20120315`; `wikipedia`, which picks some "default" Wikipedia
    corpus, specifically `enwiki-20100905` (the same one used for the
    original Wing+Baldridge paper, and quite old by now); and
    `geotext-wiki`, which is a combination of both the `wikipedia` and
    `geotext` corpora).  This sets up additional arguments to
    specify the data files for the corpus/corpora to be loaded/evaluated,
    and the language of the data, e.g. to select the correct stopwords list.
    The application to run is specified by the `--app` option; if omitted,
    it defaults to `geolocate-document` (other possibilities are
    `generate-kml`).  For the Twitter corpora, an additional option
    `--doc-thresh NUM` can be used to specify the threshold, i.e. minimum
    number of documents that a vocabulary item must be seen in; uncommon
    vocabulary before that is ignored (or rather, converted to an OOV token).
    Additional arguments to both the app and `textgrounder` itself can be
    given.  Configuration values (e.g. indicating where to find Wikipedia
    and Twitter, given the above environment variables) are read from
    `config-geolocate` in the TextGrounder `bin` directory; additional
    site-specific configuration will be read from `local-config-geolocate`,
    if you create that file in the `bin` directory.  There's a
    `sample.local-config-geolocate` file in the directory giving a sample
    local config file.

  * `tg-generate-kml` is exactly the same as `tg-geolocate --app generate-kml`
    but easier to type.

  * `run-nohup` is a script for wrapping other scripts.  The other script
    is run using `nohup`, so that a long-running experiment will not get
    terminated if your shell session ends.  In addition, starting times
    and arguments, along with all output, are logged to a file with a
    unique, not-currently existing name, where the name incorporates the
    name of the underlying script run, the current time and date, an
    optional ID string (specified using the `-i` or `--id` argument),
    and possibly an additional number needed to ensure that the file is
    unique -- it will refuse to overwrite an existing file.  This ID is
    useful for identifying different experiments using the same script.
    The experiment runner `run-geolocate-exper.py`, which allows iterating
    over different parameter settings, generates an ID based on the
    current parameter settings.

  * `python/run-geolocate-exper.py` is a framework for running a series of
    experiments on similar arguments.  It was used extensively in running
    the experiments for the paper.

You can invoke `tg-geolocate wikipedia` with no options, and it will do
something reasonable: It will attempt to geolocate the entire dev set of
the old English Wikipedia corpus, using KL divergence as a strategy, with
a grid size of 1 degrees.  Options you may find useful (which also apply to
`textgrounder geolocate` and all front ends):

`--degrees-per-cell NUM`
`--dpc NUM`

Set the size of a cell in degrees, which can be a fractional value.

`--eval-set SET`

Set the split to evaluate on, either "dev" or "test".

`--strategy STRAT ...`

Set the strategy to use for geolocating.  Sample strategies are
`partial-kl-divergence` ("KL Divergence" in the paper),
`average-cell-probability` ("ACP" in the paper),
`naive-bayes-with-baseline` ("Naive Bayes" in the paper), and `baseline`
(any of the baselines).  You can specify multiple `--strategy` options
on the command line, and the specified strategies will be tried one after
the other.

`--baseline-strategy STRAT ...`

Set the baseline strategy to use for geolocating. (It's a separate
argument because some strategies use a baseline strategy as a fallback,
and in those cases, both the strategy and baseline strategy need to be
given.) Sample strategies are `link-most-common-toponym` ("??" in the
paper), `num-documents`  ("??" in the paper), and `random` ("Random" in
the paper).  You can specify multiple `--baseline-strategy` options,
just like for `--strategy`.

`--num-training-docs, --num-test-docs`

One way of controlling how much work is done.  These specify the maximum
number of documents (training and testing, respectively) to load/evaluate.

`--max-time-per-stage SECS`
`--mts SECS`

Another way of controlling how much work is done.  Set the maximum amount
of time to spend in each "stage" of processing.  A value of 300 will
load enough to give you fairly reasonable results but not take too much
time running.

`--skip-initial N, --every-nth N`

A final way of controlling how much work is done.  `--skip-initial`
specifies a number of test documents to skip at the beginning before
stating to evaluate.  `--every-nth` processes only every Nth document
rather than all, if N > 1.  Used judiciously, they can be used to split
up a long run.

An additional argument specific to the Twitter front ends is
`--doc-thresh`, which specifies the threshold (in number of documents)
below which vocabulary is ignored.  See the paper for more details.

==================
Extracting results
==================

A few scripts are provided to extract the results (i.e. mean and median
errors) from a series of runs with different parameters, and output the
results either directly or sorted by error distance:

  * `extract-raw-results.sh` extracts results from a number of runs of
    any of the above front end programs.  It extracts the mean and median
    errors from each specified file, computes the avg mean/median error,
    and outputs a line giving the errors along with relevant parameters
    for that particular run.

  * `extract-results.sh` is similar but also sorts by distance (both
    median and mean, as well as avg mean/median), to see which parameter
    combinations gave the best results.

===============
Specifying data
===============

Data is specified using the `--input-corpus` argument, which takes a
directory.  The corpus generally contains one or more "views" on the raw
data comprising the corpus, with different views corresponding to differing
ways of representing the original text of the documents -- as raw,
word-split text (i.e. a list, in order, of the "tokens" that occur in the
text, where punctuation marks count as their own tokens and hyphenated words
may be split into separate tokens); as unigram word counts (for each unique
word -- or more properly, token -- the number of times that word occurred);
as bigram word counts (similar to unigram counts but pairs of adjacent tokens
rather than single tokens are counted); etc.  Each such view has a schema
file and one or more document files.  The schema file is a short file
describing the structure of the document files.  The document files
contain all the data for describing each document, including title, split
(training, dev or test) and other metadata, as well as the text or word
counts that are used to create the textual distribution of the document.

The document files are laid out in a very simple database format,
consisting of one document per line, where each line is composed of a
fixed number of fields, separated by TAB characters. (E.g. one field
would list the title, another the split, another all the word counts,
etc.) A separate schema file lists the name of each expected field.  Some
of these names (e.g. "title", "split", "text", "coord") have pre-defined
meanings, but arbitrary names are allowed, so that additional
corpus-specific information can be provided (e.g. retweet info for tweets
that were retweeted from some other tweet, redirect info when a Wikipedia
article is a redirect to another article, etc.).

Additional data files (which are automatically handled by the
`tg-geolocate` script) can be specified using `--stopwords-file` and
`--whitelist-file`.  The stopwords file is a list of stopwords (one per
line), i.e. words to be ignored when generating a distribution from the
word counts in the counts file.  The whitelist file is the logical opposite
of the stopwords file; if given, only words in the file will be used when
generating a distribution from the word counts in the counts file.
You don't normally have to specify these args at all, as the whitelist
file is optional in any case and a default stopwords file will be retrieved
from inside the TextGrounder distribution if necessary.

The following is a list of the generally-applicable defined fields:

  * `title`: Title of the document.  Must be unique within a given corpus,
  and must be present.  If no title exists (e.g. for a unique tweet), but
  an ID exists, use the ID.  If neither exists, make up a unique number
  or unique identifying string of some sort.

  * `id`: The (usually) numeric ID of the document, if such a thing exists.
  Currently used only when printing out documents.  For Wikipedia articles,
  this corresponds to the internally-assigned ID.

  * `split`: One of the strings "training", "dev", "test".  Must be present.

  * `corpus`: Name of the corpus (e.g. "enwiki-20111007" for the English
  Wikipedia of October 7, 2011.  Must be present.  The combination of
  title and corpus uniquely identifies a document in the presence of
  documents from multiple corpora.

  * `coord`: Coordinates of a document, or blank.  If specified, the
  format is two floats separated by a comma, giving latitude and longitude,
  respectively (positive for north and east, negative for south and
  west).

The following is a list of fields specific to Wikipedia:

  * `redir`: If this document is a Wikipedia redirect article, this
  specifies the title of the document redirected to; otherwise, blank.
  Its main use in document geotagging is in computing the incoming link
  count of a document (see below).

  * `incoming_links`: Number of incoming links, or blank if unknown.
  This specifies the number of links pointing to the document from anywhere
  else.  This is primarily used as part of certain baselines (`internal-link`
  and `link-most-common-toponym`).  Note that the actual incoming link count
  of a Wikipedia article includes the incoming link counts of any redirects
  to that article.

  * `namespace`: For Wikipedia articles, the namespace of the article.
  Articles not in the `Main` namespace have the namespace attached to
  the beginning of the article name, followed by a colon (but not all
  articles with a colon in them have a namespace prefix).  The main
  significance of this field is that articles not in the `Main` namespace
  are ignored.  For documents not from Wikipedia, this field should be
  blank.

  * `is_list_of`, `is_disambig`, `is_list`: These fields should either
  have  the value of "yes" or "no".  These are Wikipedia-specific fields
  (identifying, respectively, whether the article title is "List of ...";
  whether the article is a Wikipedia "disambiguation" page; and whether
  the article is a list of any type, which includes the previous two
  categories as well as some others).  None of these fields are currently
  used.

====================
Generating KML files
====================

It is possible to generate KML files showing the distribution of particular
words over the Earth's surface, using `tg-generate-kml` (e.g.
`tg-generate-kml wikipedia --kml-words mountain,beach,war`).  The resulting
KML files can be viewed using [http://earth.google.com Google Earth].
The only necessary arg is `--kml-words`, a comma-separated list
of the words to generate distributions for.  Each word is saved in a
file named by appending the word to whatever is specified using
`--kml-prefix`.  Another argument is `--kml-transform`, which is used
to specify a function to apply to transform the probabilities in order
to make the distinctions among them more visible.  It can be one of
`none`, `log` and `logsquared` (actually computes the negative of the
squared log).  The argument `--kml-max-height` can be used to specify
the heights of the bars in the graph.  It is also possible to specify
the colors of the bars in the graph by modifying constants given in
`Geolocate.scala`, near the beginning (`class KMLParameters`).

For example: For the Twitter corpus, running on different levels of the
document threshold for discarding words, and for the four words "cool",
"coo", "kool" and "kewl", the following code plots the distribution of
each of the words across a cell of degree size 1x1. `--mts=300` is
more for debugging and stops loading further data for generating the
distribution after 300 seconds (5 minutes) has passed.  It's unnecessary
here but may be useful if you have an enormous amount of data (e.g. all
of Wikipedia).

{{{
for x in 0 5 40; do tg-geolocate geotext --doc-thresh $x --mts=300 --degrees-per-cell=1 --mode=generate-kml --kml-words='cool,coo,kool,kewl' --kml-prefix=kml-dist.$x.none. --kml-transform=none; done 
}}}

Another example, just for the words "cool" and "coo", but with different
kinds of transformation of the probabilities.

{{{
for x in none log logsquared; do tg-geolocate geotext --doc-thresh 5 --mts=300 --degrees-per-cell=1 --mode=generate-kml --kml-words='cool,coo' --kml-prefix=kml-dist.5.$x. --kml-transform=$x; done 
}}}