NCSR Demokritos submission to Pan 2016.

This work is based on last year's submission for PAN15..

Installation:

Dataset:

In order to run the examples you will need to download the corpus for the author profiling task from the PAN website:

http://pan.webis.de/clef16/pan16-web/author-profiling.html

Requirements:

Install the requirements

pip install -r requirements.txt

Module:

You can also install the module if you would like to check it out from ipython. git clone this project cd projectfolder pip install --user .

Package consists of a python module and scripts for:

• crossvalidating
• training
• testing models on the PAN 2016 dataset.

Example usage:

• python tesst.py -i pan16-author-profiling-training-dataset/pan16-author-profiling-training-dataset-english/ -s 0.2 # for train/test splitting
• python cross.py -i pan16-author-profiling-training-dataset/pan16-author-profiling-training-dataset-english/

Configuration:

Configuration follows the same conventions used for PAN15 submission. In the config folder is a toy setup of the configuration for pangram. It is based on the YAML format.

Settings currently configurable are:

• Pan dataset settings for each language
• Feature groupings, preprocessing for each feature group, and classifier settings

In config/languages there is a file for each language which specifies where each attribute to be predicted is in the truth file that contains the label for the training set. For each of these attributes, you can set a file that contains the feature grouping and preprocessing settings. In the example provided the mapping is the same for each language, but this need not be the case.

In config/features the settings for each feature group can be found. The format is in the form label of:

label of feature group

• feature extractor 1
• feature extractor 2
• .. preprocessing : label: label this so that it doesn't get computed twice if it has been defined elsewhere pipe: - method 1 - method 2 - ... In the above snippet, feature extractor names are expected to be defined in pan/features.py. Similarly, the above methods are expected to be defined in pan/preprocess.py and process a mutable iterable in place. (in our case a list of texts)

License

Copyright 2016 NCSR Demokritos submission for Pan 2016, Konstantinos Bougiatiotis

Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at

http://www.apache.org/licenses/LICENSE-2.0

Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License.