Getting names and dates from the email headers is relatively straightforward, because that is structured information.  Getting geographical locations from the email content itself is not.  Here is how I would approach it.

First, some background.  What we are talking about here is ‘Information Extraction‘, and ’Entity Detection‘.  Since we know we are looking for geographical locations, we can attempt to convert the unstructured data in the body of an email into structured data.  Basically we parse into a database.

On to the show:  The basic architecture of such a system could look like this:
Raw Text->:Sentence Segmentation:->Sentences->:Tokenization:->Tokenized Sentences->:Part of Speech Tagging:->POS-Tagged Sentences->:Entity Detection:->chunked sentences->:Relation Detection:->Relations (list of tuples)

NLTK would be my tool of choice.  To do the first three steps, I would use the default sentence segmenter, word tokenizer, and pos tagger.

def steps_one_to_three(document):
sentences = nltk.sent_tokenize(document)
sentences = [nltk.word_tokenize(sent) for sent in sentences]
sentences = [nltk.pos_tag(sent) for sent in sentences]

This will give you POS tagged sentences.
The next step would be to segment and lable the entities that might have interesting relationships with each other, such as a proper name (Houston, Texas).  Then we can search for specific patterns between pairs of entities that occur near each other in the text, and use that to build our tuples to document the relationship between the entitites.

Enter Chunking!  Chunking is a basic technique for entity detection.  It segments and labels multi-token sequences, such as “the smart engineer” into something called a noun phrase.  Thank heavens, because geographical information just to happens to be a noun phrase (Houston, Texas).  Sometimes a human can look at a noun phrase and say “hey, that is a single noun phrase but my chunker broke it up into 2 noun phrases”.  Thats ok.  The algorithm tries to make our noun phrases small enough such that they dont contain any other ‘nested’ noun phrase, so it errs on the side of caution.  Make sense?  Here is an example:

“The national association for the advancement of colored peoples” is clearly a single noun phrase, but it contains nested noun phrases.  However, it will be captured as a series of noun phrase chunks.

Enough about chunking.  What’s next?  In order to use our chunker effectively, we need to define a chunk grammar.  This will be the ruleset that will dictate how sentences should be chunked.  This is pretty straight forward in the form of a regex:

grammar = “NP: (<DT>?<JJ><NN>)” <-”Tirrell, what the fu’schnick is that?”.  Lemme break it down.  The rule to our grammar says, “Find my the Noun Phrases (NP) that has an optional determiner(DT) followed by any number of adjectives (JJ) and then a noun (NN)”.  So this grammar chunker would find something like “The little wayward boy”, and the grammar is defined as a “Tag Pattern”

Are you still with me?  Good.  Now I can circle back to the original question of finding a geography in unstructured data.  Earlier, I talked about Named Entities, which is what we want to extract.  The goal of a “named entity recognition” system is to identify all textual mentions of the named entities, and this amounts to identifying the boundaries of the named entity, then identifying its type.

Examples:
ORGANIZATION:  Quora, Inc
PERSON:  TIRRELL PAYTON
LOCATION, HOUSTON, TX

The cool thing about named entity recognition is that it can do other things too, like help answer questions.  You did a google search of “Who was the first black man killed in the revolutionary war?”.

Google returns a wikipedia entry that looks something like this,”Crispus Attucks (c. 1723 – March 5, 1770) was a dockworker of Wampanoag and African descent. He was the first person shot to death by British redcoats during the Boston Massacre, inBoston, Massachusetts.[2] He has been called the first martyr of the revolution.[3]“.  If we were to run this passage through our Entity Recognition System, it could return something like “Crispus Attucks was the first black man killed in the revolutionary war”.  Cool huh?

Ok, I’m getting off track again, lets get back to geographies.  In the case of locations, we could use a gazetteer (geographical dictionary), but dictionary lookups can be pretty dumb because it does blind pattern matches.  ”Reading is fundamental”<-thinks this passage is about Reading, UK.  Well, this is about reading… as the gerund form of the verb “to read”, not the PLACE “Reading, UK”.  AHA.  Now you see why all the razzle dazzle on POS tagging will be important!

NLTK is cool because it has a classifier that can already recognized named entities<-Damn Tirrell, why didn’t you just say that before.  Because I had to build suspense and an appreciation of the magnitude and messiness of the problem space!  NLTK tags named entities according to category labels.  As a side note, the new version of NLTK includes an interface to the Stanford Named Entity Recognizer:  http://nltk.github.com/api/nltk….

Stanford people are pretty smart, so this may work better than the default.  BUT for the sake of this discussion, lets assume the vanilla case that we are using the default nltk.ne_chunk, and if you use ne_chunk, you can get a location out of your unstructured data.

So now, you know how to get locations out of unstructured data such as email.  If you wanted to take it a step further, you could extract relationships from the named location entities to answer questions such as “Where was this person when this email was sent?” or “Where is this company located?”

Going into that is beyond the scope of your question, and I write too much anyway.  I hope this was valuable.

When I am looking at a dataset, the first thing I ask myself is “What is the question I am trying to get an answer to?”  The answer may very well be in the data, but the question is of utmost importance.

The next question I ask myself is, “Is this data ready for me to start asking it the question”.  Most likely it is not, because *real* data is dirty data, and you need to clean it up before you can ask it questions.

After I have clean data, the next questions is “What is the class of question I am asking this data”.

-If I have a set of example features, then I can model according to those features in a supervised learning algorithm.

“Do I have combination-type features?”

-If so, a Naive Bayesian classifier most likely wont work, because the combinations of features and classifications will confuse my classifier.

“Maybe I can use a decision tree?”

-Maybe, but if I need to create a model that requires incremental training, a decision tree wont work well.  I will need to retrain and rebuild the tree for every new feature/combination

“What about a Neural Network?”
- Maybe, but I will need to run a LOT of experiments to get the parameters right.

“What about a Support Vector Machine?”
- Maybe, but if we are dealing with a high number of dimensions I might not understand how the SVM is even *doing* the classification.  That may not be an issue, but it may be an issue if I need to mentally walk through it.

So on and so on.

The point being, as you get more familiar with the algorithms and applications, you get a better sense of which ones to use either alone or in combination.

1. Whats the question?
2. Is the data ready?
3. Experiment Experiment Experiment.  Test Test Test.

This question was asked on Quora:

Lets say we have 3 types of entities, A, !A, and U
A are entities known to be of type A
!A are entities known to NOT be of type A
U are entities that are unknown.  Could be A, could be !A, could be some other entity class (B), but for now they are U”

How is !A different from U?
Logically, !A has a more defined feature than U.  That feature is that we know that it is NOT an instance of type A.  Based on the definition above, we don’t know anything about U, other than it has no defined features for us to classify against.  Could be A, could be !A, could be some other entity class (B), but for now they are U

Let me move away from symbolism for a moment and talk about this in terms of a metaphor.  We are classifying animals.
A is an antelope.
!A could be anything BUT an antelope, such as a tiger or a dog.
U is unknown.  Could be an antelope, could NOT be an antelope.  We don’t know.  For now we call it U.  As the algorithm gets better at classification, this could turn out to be a different species of antelope(A), or a different animal altogether(!A)

How additional knowledge of NOT can be used in 2 (the training set in which some classes are classified as objects of class A, some are unknown, and some are classified as objects which are known NOT to belong to class A?

The additional knowledge of NOT can be used to set up a different class, so instead of A and !A, you can now have A, !A, and U.

Are different methods of classification must be used for 1 and 2?
Like everything else, it depends:
- Are you looking to classify binary A vs !A?  If so, and you know U = !A, then the U entities and the !A entities can be given the same binary classification(0) and the entities that are known to be A can be given(1)

- Are you looking for a richer classification set with more numerous classifications?  If so, U can be given its own classification, with A, and !A given their own classifications.  Potentially U can be decomposed into other classifications of its own.  Given the definition in the question, I would call U unknown… which doesnt mean anything in terms of features.  It may turn out to be A, it may turn out to be !A, and it may turn out to be something totally different.

How do you learn with negative examples?
Make U = !A and train the model with that assumption

Someone asked this question on Quora, so I tried to answer it:  http://www.quora.com/Is-it-possible-to-use-a-machine-learning-algorithm-to-determine-whether-or-not-a-voice-comes-from-person-1-or-person-2

Yes.