Keyword extraction from scientific documents

Spring Semester 2017, University of Trento.

The task and the results are discussed in the report file.

Repository Structure

This repository contains:

• two folders, data, embeddings
• three main files, main_crfbs, main_crfrs, main_crfgen;
• one requirement file requirements
• one .pdf file, report; and
• two .md files, README and LICENCE.

In the data folder is contained the dataset used for the evaluation.

In the embedding folder is contained a model to try the algorithm.

In the main_crf* are contained the three algorithms presented in the report.

In the requirements is contained the pip freeze of the used python environment.

In the report is contained an overview of the approach and the experimentation with the experimental results.

Initialize the working environment

Download and install the crfpp framework used.

% git clone https://github.com/taku910/crfpp.git
% cd crfpp/
% ./configure 
% make
% su
% make install

If you do not want the installation of crfpp to occupy common places on your machine please consider setting the prefix flag. Keep in mind that doing so you will need to either set an alias in your command line for the crf_learn and and crf_test custom location or modify the crfpp_gen code.

% ./configure --prefix=/somewhere/else/than/usr/local

Install the additional python requirements.

% pip2.7 install -r requirements.txt

Finally, please make sure to download the word_embedding model and replace the given placeholder in embeddings/lay_512/epo_15/vectors.txt.

Running the algorithm

Running the baseline.

% python2.7 main_crfbs.py

Running the first optimisation with RandomSearch.

% python2.7 main_crfrs.py

Running the second optimisation with Genetic optimiser..

% python2.7 main_crfgen.py

Please note that the default configuration will NOT clean up the sandbox when done.

For any further information about the working of the algorithm do not hesitate to contact me or read the report.pdf.