Awesome

Phrase-At-Scale

Phrase-At-Scale provides a fast and easy way to discover phrases from large text corpora using PySpark. Here's an example of phrases extracted from a review dataset:

Features

Discover most common phrases in your text
Size of discovered phrases can be arbitrary (typically: bigrams and trigrams)
Adjust configuration to control quality of phrases
Can be used in languages other than English
Can be run locally using multiple threads, or in parallel on multiple machines
Annotate your corpora with the phrases discovered

Quick Start

Run locally

To re-run phrase discovery using the default dataset:

Install Spark
Clone this repo and move into its top-level directory.
```
git clone git@github.com:kavgan/phrase-at-scale.git
```

Run the spark job:

<your_path_to_spark>/bin/spark-submit --master local[200] --driver-memory 4G phrase_generator.py

This will use settings (including input data files) as specified in config.py.

You should be able to monitor the progress of your job at http://localhost:4040/

Notes:

The above command runs the job on master and uses the specified number of threads within local[num_of_threads].
This job outputs 2 files:
1. the list of phrases under top-opinrank-phrases.txt
2. the annotated corpora under data/tagged-data/

Configuration

To change configuration, just edit the config.py file.

Config	Description
`input_file`	Path to your input data files. This can be a file or folder with files. The default assumption is one text document (of any size) per line. This can be one sentence per line, one paragraph per line, etc.
`output-folder`	Path to output your annotated corpora. Can be local path or on HDFS
`phrase-file`	Path to file that should hold the list of discovered phrases.
`stop-file`	Stop-words file to use to indicate phrase boundary.
`min-phrase-count`	Minimum number of occurrence for phrases. Guidelines: use 50 for < 300 MB of text, 100 for < 2GB and larger values for a much larger dataset.

Dataset

The default configuration uses a subset of the OpinRank dataset, consisting of about 255,000 hotel reviews. You can use the following to cite the dataset:

@article{ganesan2012opinion,
  title={Opinion-based entity ranking},
  author={Ganesan, Kavita and Zhai, ChengXiang},
  journal={Information retrieval},
  volume={15},
  number={2},
  pages={116--150},
  year={2012},
  publisher={Springer} 
}

Contact

This repository is maintained by Kavita Ganesan. Please send me an e-mail or open a GitHub issue if you have questions.