NED system for the paper Effective Use of Context in Noisy Entity Linking, build for the Wikilinks-NED dataset.

Unfortunately, this code is really old and requires Python 2.7.17 or similar, which I'm terribly sorry about :(

Running our system requires:

1. The Wikilinks-NED dataset
2. Word and Entity embeddings
3. A page-title json of wikipedia page ID to title strings (for title CNNs and feature construction)

The page-title json was built using a CategoryParser (in src/data_readers/CategoryParser.py), which parses a variety of information from a Wikipedia pages-articles dump and saves it into one large object. Then the script src/data_readers/pull_out_title_db.py simply saves the specific page ID to title string json that exists in that CategoryParser object to a separate json file.

There is certainly a much easier way to do this, it's relatively straightforward to do with a variety of Wiki-dump parsers. My method is a by-product of the time when I was exploring category strings and embeddings as additional features for out system.

There are a couple of ways to get the dataset and embeddings:

• The original repo for creating the dataset and embeddings is here. I found that I had to make some modifications to it to get it to work, which can be found here, but note that my version only contains scripts necessary for downloading and creating the dataset and embeddings, and not their actual NED system.
• Alternatively, you can email me at dam@jhu.edu and I can give you my dropbox link for both dataset and embeddings.

The word embedding size can be quite huge, so I suggest relativizing it. I have a script that does that (but it has hard-coded paths) at src/data_readers/relativize.py.

Our system runs off of a configuration file which contains pointers to data files, training parameters, and model parameters.

An example configuration file can be seen below (these are all available options). This configuration file builds a model with character-level CNN encoders, no hand-built features. It runs for 5 epochs with a batch size of 100 and 4 negative entity samples per example.

{
	"files": {
	    "base-data-dir": "/exp/dmueller/wikilinks/data/",
	    "wikilink-stats": "wikilinksNED-train-stats",
	    "word-embeds": "dim300vecs-relativized",
	    "ent-embeds": "dim300context-vecs-relativized",
	    "wiki-page-titles": "page_title.json"
	},
	"training": {
	    "neg_samples": 4,
	    "neg_sample_from_cands": false,
	    "batch_size": 100,
	    "epochs": 5
	},
	"model": {
	    "features": false,
	    "character_cnn": true,
	    "ccnn_window_size": 5,
	    "lr": 0.001
	}
}

The entry point of the system is src/main.py. It requires arguments --config_file, which should point to a configuration file, and --experiment_dir, which is the directory to save models to.

Running the system then looks like:

python src/main.py --config_file $CONFIG --experiment_dir $OUTPUT_DIRECTORY

As a small disclaimer, a lot of this code was written as an undergrad and was my first experience with deep learning libraries in general. A lot of my techniques for data loading, batching, etc. are very sub-optimal, and some hyper-parameters are hard-coded to match the original system described in Named Entity Disambiguation for Noisy Text.

The paper describing this system is Effective Use of Context in Noisy Entity Linking, published at EMNLP 2018.

To cite this work, please use

@InProceedings{D18-1126,
  author = 	"Mueller, David and Durrett, Greg",
  title = 	"Effective Use of Context in Noisy Entity Linking",
  booktitle = 	"Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
  year = 	"2018",
  publisher = 	"Association for Computational Linguistics",
  pages = 	"1024--1029",
  location = 	"Brussels, Belgium",
  url = 	"http://aclweb.org/anthology/D18-1126"
}