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Semanticizer, standalone

Semanticizest is a package for doing entity linking, also known as semantic linking or semanticizing: you feed it text, and it outputs links to pertinent Wikipedia concepts. You can use these links as a "semantic representation" of the text for NLP or machine learning, or just to provide some links to background info on the Wikipedia.

Installing

Make sure you have a Go compiler (1.4 or newer) and Git. On new versions of Debian/Ubuntu/Mint, that's::

sudo apt-get install git golang-go

On CentOS::

sudo yum -y install git golang

In case your distro has older versions of go, you might consider installing a newer version via gvm <https://github.com/moovweb/gvm>_.

Set up a Go workspace, if you haven't already. For example::

mkdir /some/where/go
cd /some/where/go
export GOPATH=$(pwd)

Fetch and compile::

go get -u github.com/semanticize/st/...

You now have a working parser at ${GOPATH}/bin/semanticizest-dumpparser. Issue::

${GOPATH}/bin/semanticizest-dumpparser --help

to figure out how to generate a semanticizer model, then use this model from the REST API::

${GOPATH}/bin/semanticizest --http=:5002 your_model
curl http://localhost:5002/all -d 'Does the entity linking work?'

You can also use semanticizest as a command-line tool by omitting --http. In that case, it will read paragraphs (double newline-separated) from standard input and emit a JSON representation of the candidate entities in each paragraph.

Python binding

Once you have built a semanticizer model, you can use semanticizest from Python using the Python wrapper. Install the Python wrapper with::

python setup.py install

You can use the python wrapper as follows:

The wrapper can operate in two modes: either you start the semanticizest REST server, or you have the wrapper start it for you.

If you already have a server running on port 5002, as described above::

from semanticizest import Client

sentence = 'Antwerpen'
url = 'http://localhost:5002/'
client = Client(url)
candidates = client.all_candidates(sentence)

To start the server automatically, you need to construct a Semanticizer from the path to the model file::

from semanticizest import Semanticizer

sentence = 'Antwerpen'
semanticizer = Semanticizer('/path/to/model.db')
candidates = client.all_candidates(sentence)