ericwburden/py-nltk-dev

Python NLTK library utilities

###About###

This code is a research/academic project conducted at Kaunas University of Technology (Lithuania) in 2013-05 by two Informatics faculty MSc students: Aiste Ivonyte & Tomas Uktveris.

Project analyses & applies natural language processing(NLP) algorithms to texts extracted from certain year Wikipedia archived news articles.

The created text analyzer does the following (for a given article):

1. Extracts named entities (people) - the named entity recognition (NER) problem [ner.py]

• uses default Python NLTK ne_chunker + extra logic to detect sex/city/country & remove false positives

2. Creates a summary from article text [summarize.py, ph_reduction.py]. Two methods used:

• Sentences with most frequent words - Summary I
• Phrase reduction method - Summary II

3. Classifies the article into 5 most frequent (top) categories from all analyzed Wikipedia articles [training.py, training_binary.py]

• Uses three NLTK library built-in classifiers - Bayes, MaxEnt (regression) and DecisionTree.
  Two approaches are used for classifier training:
  • Multiclass - classifier is trained to detect 1 class from multiple (7 classes in total)
  • Binary - trains 3x6 binary classifiers to detect if article represents a given category

4. Finds people actions [action.py]

• Custom token & sentence analysis - reuses NER data to find & assign references.

5. Resolves references/anaphoras (named entity normalization - NEN) [references.py]

• Custom token & sentence analysis - reuses NER data to find required verbs.

6. Finds people interactions [interactions.py]

• Custom token & sentence analysis - reuses NER & reference data for finding multiple people in sentence and their actions.

###License### Code & project provided under MIT license (http://opensource.org/licenses/mit-license.php). Use at your own risk, no guaranties or warranty included.

Female/male names dictionary used from NLTK project: https://code.google.com/p/nltk/

English words dictionary used from: http://www-01.sil.org/linguistics/wordlists/english/

World cities database used from: http://www.maxmind.com/en/worldcities

###Requirements###

• Python 2.7 (http://www.python.org/download/releases/)
• Python NTLK library (http://nltk.org/) + installed all available corporas (>>> import nltk; nltk.download())

###Directory structure###

  . - (root directory) contains all source code for the analyzer
  ./archives - contains EN generic word, people names & country dictionaries, SQL city names DB files & scripts
  ./db - contains extracted Wikipedia articles by month
  ./FtpDownloader - Java utility to download articles DB from FTP site
  ./other - misc & example scripts

###Usage###

Running the analyzer

• Run article parser & data generation utility to generate the required data files for the next step: python data.py

• Run multiclass trainer to generate three types of classifier files:

python training.py -b
python training.py -m
python training.py -d

• Run binary trainer to generate other classifier files:

python training_binary.py -b
python training_binary.py -m
python training_binary.py -d

• Run the main analyzer script to analyze a given article:

python main.py -f db/klementavicius-rimvydas/2011-12-03-1.txt

###Running other tests### Some analyzer functionality can be tested separately by running the test_xxxx.py files.