Proceedings

Semantic text and metadata extraction from conference proceedings for constructing knowledge bases and semantic search.

Dataset

The dataset covers major conferences in machine learning, natural language and speech processing. The dataset directory contains the directory structure for a preview. A sample processed json object is available here.

Download the dataset here. Download size: 508MB, Extracted size: 6GB

Data

The current release of the dataset has documents processed from the following conference proceedings.

NeurIPS	EMNLP	ACL	InterSpeech
-	-	-	-
2019	2019	2019	2019
2018	-	2018	2018
2017	-	2017	2017
2016	-	2016	-
2015	-	2015	-

Note: Few files from certain proceedings are dropped from the dataset due to parsing errors. More proceedings volumes to be added soon.

Features

The dataset contains the following fields extracted from each document.

• Semantically extracted fields using a nltk and spacy pipeline.
  • entities: Named Entity Recognition is performed on the document text to extract text span as entites and tag them with entity_type.
  • tags: Part of Speech tagged tokens extracted from document text.
  • parser: Dependency Parsing between text spans of document.
  • noun_chunks: Base noun phrases that have a noun as their head.
• Metadata fields extracted using PyPDF2

  filename	metadata	numPages
  title	author	subject
  creator	producer	keywords
  creationdate	moddate	trapped
  ptexfullbanner	raw_text	-

Local Development

An instance of the extraction pipeline can be run locally to produce processed json in the same format as from the dataset.

Installation

Clone the repository and cd into proceedings/.

git clone https://github.com/enigmaeth/proceedings
cd proceedings/

The repository requires python3 to be installed. Check the python version and initialise a virtualenv to install requirements.

$ python --version
Python 3.6.9

$ python3 -m venv .
$ source .env/bin/activate
$ pip install -r requirements.txt

Download the required spacy and nltk modules.

$ pip install -U spacy
$ pip install -U spacy-lookups-data
$ python -m spacy download en_core_web_sm

# Install the nltk punkt module
$ python3
>>> import nltk
>>> nltk.download()
NLTK Downloader
---------------------------------------------------------------------------
    d) Download   l) List    u) Update   c) Config   h) Help   q) Quit
---------------------------------------------------------------------------
Downloader> d

Download which package (l=list; x=cancel)?
  Identifier> punkt

# punkt module should be downloaded now.

Using extract.py

The entry point for the pipeline is src/extract.py. It defines the following three arguments.

  --directory DIRECTORY **REQUIRED**
                        Root dir with documents to process.
                        eg: ~/docs/acl/acl_2015
  --accepted_formats ACCEPTED_FORMATS
                        File extensions to process. Default: ["pdf"]
  --max_size MAX_SIZE   Max size of a single file to process. Default: 5MB

To process a documents directory with extract.py, run the following command. The path to directory should be an absolute path.

python3 extract.py --directory <path/to/directory>

Contributing

Adding a new proceeding

Contributions for adding processed proceedings are welcome and would help grow the dataset quickly.

• If you are interested in adding a new proceeding, please follow the steps in Local Development and send a Pull Request with the processed json (in .zip/.tar.gz) that would be added in the next release!

• If you want a new proceeding to be added, please open a new issue using the template here!

Adding a new semantic extraction component

All the semantic extraction components live inside src/repository/semantic_extract.py file.

• If you are interested in adding a new semantic extraction component, please follow the steps in Local Development to run a local instance. Verify if your component works by running it on the test directory and send a Pull Request!

• If you want a new component to be added, please open a new issue using the template here!

LICENSE

The project is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.