Use geographic binary relation info/context about mordecai HOT 2 CLOSED

How many combinations of "X, etc Y" are there?: I'm guessing a dictionary-based approach would be fairly effective. Presumably the phrases follow the usual rank-size distribution. We don't really need a parse since we've got the commas, and we can automatically generate the candidate phrases with a simple regex search (maybe with some simple markup first. But not a full parse).

Alternatively, try to generate a conditional random field model or something similar to catch these. But I'd try getting the candidate phrases first and see how many we've got.

from mordecai.

Maryam here at UTD has already tried the CRF approach to get at subnational
locations. It was not a very successful exercise.

Spoke with her and Andy about this today. He is making other modifications
to Mordecai as well.

Further issue is that we need more labelled training data. Talking with C.
Fariss about this via email to see if we can employ some HR text data he
has recently published.

On Tue, Jun 28, 2016 at 4:39 PM, Philip Schrodt [email protected]
wrote:

How many combinations of "X, etc Y" are there?: I'm guessing a
dictionary-based approach would be fairly effective. Presumably the phrases
follow the usual rank-size distribution. We don't really need a parse since
we've got the commas, and we can automatically generate the candidate
phrases with a simple regex search (maybe with some simple markup first.
But not a full parse).

Alternatively, try to generate a conditional random field model or
something similar to catch these. But I'd try getting the candidate phrases
first and see how many we've got.

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Patrick T. Brandt
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School of Economic, Political and Policy Sciences
University of Texas at Dallas
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from mordecai.