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https://github.com/huggingface/neuralcoref

✨Fast Coreference Resolution in spaCy with Neural Networks
https://github.com/huggingface/neuralcoref

coreference coreference-resolution machine-learning neural-networks nlp python pytorch spacy spacy-extension spacy-pipeline

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✨Fast Coreference Resolution in spaCy with Neural Networks

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# ✨NeuralCoref 4.0: Coreference Resolution in spaCy with Neural Networks.



NeuralCoref is a pipeline extension for spaCy 2.1+ which annotates and resolves coreference clusters using a neural network. NeuralCoref is production-ready, integrated in spaCy's NLP pipeline and extensible to new training datasets.



For a brief introduction to coreference resolution and NeuralCoref, please refer to our [blog post](https://medium.com/huggingface/state-of-the-art-neural-coreference-resolution-for-chatbots-3302365dcf30).

NeuralCoref is written in Python/Cython and comes with a pre-trained statistical model for **English only**.



NeuralCoref is accompanied by a visualization client [NeuralCoref-Viz](https://github.com/huggingface/neuralcoref-viz), a web interface  powered by a REST server that can be [tried online](https://huggingface.co/coref/). NeuralCoref is released under the MIT license.



✨ Version 4.0 out now! Available on pip and compatible with SpaCy 2.1+.



[![Current Release Version](https://img.shields.io/github/release/huggingface/neuralcoref.svg?style=flat-square)](https://github.com/huggingface/neuralcoref/releases)

[![spaCy](https://img.shields.io/badge/made%20with%20❤%20and-spaCy-09a3d5.svg)](https://spacy.io)

[![Travis-CI](https://travis-ci.org/huggingface/neuralcoref.svg?branch=master)](https://travis-ci.org/huggingface/neuralcoref)

[![NeuralCoref online Demo](https://huggingface.co/coref/assets/thumbnail-large.png)](https://huggingface.co/coref/)



- **Operating system**: macOS / OS X · Linux · Windows (Cygwin, MinGW, Visual Studio)

- **Python version**: Python 3.6+ (only 64 bit)

- **Package managers**: [pip]



## Install NeuralCoref



### Install NeuralCoref with pip



This is the easiest way to install NeuralCoref.



```bash

pip install neuralcoref

```



#### `spacy.strings.StringStore size changed` error



If you have an error mentioning `spacy.strings.StringStore size changed, may indicate binary incompatibility` when loading NeuralCoref with `import neuralcoref`, it means you'll have to install NeuralCoref from the distribution's sources instead of the wheels to get NeuralCoref to build against the most recent version of SpaCy for your system.



In this case, simply re-install neuralcoref as follows:



```bash

pip uninstall neuralcoref

pip install neuralcoref --no-binary neuralcoref

```



#### Installing SpaCy's model



To be able to use NeuralCoref you will also need to have an English model for SpaCy.



You can use whatever english model works fine for your application but note that the performances of NeuralCoref are strongly dependent on the performances of the SpaCy model and in particular on the performances of SpaCy model's tagger, parser and NER components. A larger SpaCy English model will thus improve the quality of the coreference resolution as well (see some details in the [Internals and Model](#-Internals-and-Model) section below).



Here is an example of how you can install SpaCy and a (small) English model for SpaCy, more information can be found on [spacy's website](https://spacy.io/usage/models):



```bash

pip install -U spacy

python -m spacy download en

```



### Install NeuralCoref from source



You can also install NeuralCoref from sources. You will need to install the dependencies first which includes Cython and SpaCy.



Here is the process:



```bash

venv .env

source .env/bin/activate

git clone https://github.com/huggingface/neuralcoref.git

cd neuralcoref

pip install -r requirements.txt

pip install -e .

```



## Internals and Model



NeuralCoref is made of two sub-modules:



- a rule-based mentions-detection module which uses SpaCy's tagger, parser and NER annotations to identify a set of potential coreference mentions, and

- a feed-forward neural-network which compute a coreference score for each pair of potential mentions.



The first time you import NeuralCoref in python, it will download the weights of the neural network model in a cache folder.



The cache folder is set by defaults to `~/.neuralcoref_cache` (see [file_utils.py](./neuralcoref/file_utils.py)) but this behavior can be overided by setting the environment variable `NEURALCOREF_CACHE` to point to another location.



The cache folder can be safely deleted at any time and the module will download again the model the next time it is loaded.



You can have more information on the location, downloading and caching process of the internal model by activating python's `logging` module before loading NeuralCoref as follows:



```python

import logging;

logging.basicConfig(level=logging.INFO)

import neuralcoref

>>> INFO:neuralcoref:Getting model from https://s3.amazonaws.com/models.huggingface.co/neuralcoref/neuralcoref.tar.gz or cache

>>> INFO:neuralcoref.file_utils:https://s3.amazonaws.com/models.huggingface.co/neuralcoref/neuralcoref.tar.gz not found in cache, downloading to /var/folders/yx/cw8n_njx3js5jksyw_qlp8p00000gn/T/tmp_8y5_52m

100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 40155833/40155833 [00:06<00:00, 6679263.76B/s]

>>> INFO:neuralcoref.file_utils:copying /var/folders/yx/cw8n_njx3js5jksyw_qlp8p00000gn/T/tmp_8y5_52m to cache at /Users/thomaswolf/.neuralcoref_cache/f46bc05a4bfba2ae0d11ffd41c4777683fa78ed357dc04a23c67137abf675e14.7d6f9a6fecf5cf09e74b65f85c7d6896b21decadb2554d486474f63b95ec4633

>>> INFO:neuralcoref.file_utils:creating metadata file for /Users/thomaswolf/.neuralcoref_cache/f46bc05a4bfba2ae0d11ffd41c4777683fa78ed357dc04a23c67137abf675e14.7d6f9a6fecf5cf09e74b65f85c7d6896b21decadb2554d486474f63b95ec4633

>>> INFO:neuralcoref.file_utils:removing temp file /var/folders/yx/cw8n_njx3js5jksyw_qlp8p00000gn/T/tmp_8y5_52m

>>> INFO:neuralcoref:extracting archive file /Users/thomaswolf/.neuralcoref_cache/f46bc05a4bfba2ae0d11ffd41c4777683fa78ed357dc04a23c67137abf675e14.7d6f9a6fecf5cf09e74b65f85c7d6896b21decadb2554d486474f63b95ec4633 to dir /Users/thomaswolf/.neuralcoref_cache/neuralcoref

```



## Loading NeuralCoref



### Adding NeuralCoref to the pipe of an English SpaCy Language



Here is the recommended way to instantiate NeuralCoref and add it to SpaCY's pipeline of annotations:



```python



# Load your usual SpaCy model (one of SpaCy English models)

import spacy

nlp = spacy.load('en')



# Add neural coref to SpaCy's pipe

import neuralcoref

neuralcoref.add_to_pipe(nlp)



# You're done. You can now use NeuralCoref as you usually manipulate a SpaCy document annotations.

doc = nlp(u'My sister has a dog. She loves him.')



doc._.has_coref

doc._.coref_clusters

```



### Loading NeuralCoref and adding it manually to the pipe of an English SpaCy Language



An equivalent way of adding NeuralCoref to a SpaCy model pipe is to instantiate the NeuralCoref class first and then add it manually to the pipe of the SpaCy Language model.



```python



# Load your usual SpaCy model (one of SpaCy English models)

import spacy

nlp = spacy.load('en')



# load NeuralCoref and add it to the pipe of SpaCy's model

import neuralcoref

coref = neuralcoref.NeuralCoref(nlp.vocab)

nlp.add_pipe(coref, name='neuralcoref')



# You're done. You can now use NeuralCoref the same way you usually manipulate a SpaCy document and it's annotations.

doc = nlp(u'My sister has a dog. She loves him.')



doc._.has_coref

doc._.coref_clusters

```



## Using NeuralCoref



NeuralCoref will resolve the coreferences and annotate them as [extension attributes](https://spacy.io/usage/processing-pipelines#custom-components-extensions) in the spaCy `Doc`,  `Span` and `Token` objects under the `._.` dictionary.



Here is the list of the annotations:



|  Attribute                |  Type              |  Description

|---------------------------|--------------------|-----------------------------------------------------

|`doc._.has_coref`          |boolean             |Has any coreference has been resolved in the Doc

|`doc._.coref_clusters`     |list of `Cluster`   |All the clusters of corefering mentions in the doc

|`doc._.coref_resolved`     |unicode             |Unicode representation of the doc where each corefering mention is replaced by the main mention in the associated cluster.

|`doc._.coref_scores`       |Dict of Dict        |Scores of the coreference resolution between mentions.

|`span._.is_coref`          |boolean             |Whether the span has at least one corefering mention

|`span._.coref_cluster`     |`Cluster`           |Cluster of mentions that corefer with the span

|`span._.coref_scores`      |Dict                |Scores of the coreference resolution of & span with other mentions (if applicable).

|`token._.in_coref`         |boolean             |Whether the token is inside at least one corefering mention

|`token._.coref_clusters`   |list of `Cluster`   |All the clusters of corefering mentions that contains the token



A `Cluster` is a cluster of coreferring mentions which has 3 attributes and a few methods to simplify the navigation inside a cluster:



|  Attribute or method   |  Type / Return type |  Description

|------------------------|---------------------|-----------------------------------------------------

|`i`                     |int                  |Index of the cluster in the Doc

|`main`                  |`Span`               |Span of the most representative mention in the cluster

|`mentions`              |list of `Span`       |List of all the mentions in the cluster

|`__getitem__`           |return `Span`        |Access a mention in the cluster

|`__iter__`              |yields `Span`        |Iterate over mentions in the cluster

|`__len__`               |return int           |Number of mentions in the cluster



### Navigating the coreference cluster chains



You can also easily navigate the coreference cluster chains and display clusters and mentions.



Here are some examples, try them out to test it for yourself.



```python

import spacy

import neuralcoref

nlp = spacy.load('en')

neuralcoref.add_to_pipe(nlp)



doc = nlp(u'My sister has a dog. She loves him')



doc._.coref_clusters

doc._.coref_clusters[1].mentions

doc._.coref_clusters[1].mentions[-1]

doc._.coref_clusters[1].mentions[-1]._.coref_cluster.main



token = doc[-1]

token._.in_coref

token._.coref_clusters



span = doc[-1:]

span._.is_coref

span._.coref_cluster.main

span._.coref_cluster.main._.coref_cluster

```



**Important**: NeuralCoref mentions are spaCy [Span objects](https://spacy.io/api/span) which means you can access all the usual [Span attributes](https://spacy.io/api/span#attributes) like `span.start` (index of the first token of the span in the document), `span.end` (index of the first token after the span in the document), etc...



Ex: `doc._.coref_clusters[1].mentions[-1].start` will give you the index of the first token of the last mention of the second coreference cluster in the document.



## Parameters



You can pass several additional parameters to `neuralcoref.add_to_pipe` or `NeuralCoref()` to control the behavior of NeuralCoref.



Here is the full list of these parameters and their descriptions:



| Parameter       | Type                    | Description

|-----------------|-------------------------|----------------

|`greedyness`     |float                    |A number between 0 and 1 determining how greedy the model is about making coreference decisions (more greedy means more coreference links). The default value is 0.5.

|`max_dist`       |int                      |How many mentions back to look when considering possible antecedents of the current mention. Decreasing the value will cause the system to run faster but less accurately. The default value is 50.

|`max_dist_match` |int                      |The system will consider linking the current mention to a preceding one further than `max_dist` away if they share a noun or proper noun. In this case, it looks `max_dist_match` away instead. The default value is 500.

|`blacklist`      |boolean                  |Should the system resolve coreferences for pronouns in the following list: `["i", "me", "my", "you", "your"]`. The default value is True (coreference resolved).

|`store_scores`   |boolean                  |Should the system store the scores for the coreferences in annotations. The default value is True.

|`conv_dict`      |dict(str, list(str))     |A conversion dictionary that you can use to replace the embeddings of *rare words* (keys) by an average of the embeddings of a list of *common words* (values). Ex: `conv_dict={"Angela": ["woman", "girl"]}` will help resolving coreferences for `Angela` by using the embeddings for the more common `woman` and `girl` instead of the embedding of `Angela`. This currently only works for single words (not for words groups).



### How to change a parameter



```python

import spacy

import neuralcoref



# Let's load a SpaCy model

nlp = spacy.load('en')



# First way we can control a parameter

neuralcoref.add_to_pipe(nlp, greedyness=0.75)



# Another way we can control a parameter

nlp.remove_pipe("neuralcoref")  # This remove the current neuralcoref instance from SpaCy pipe

coref = neuralcoref.NeuralCoref(nlp.vocab, greedyness=0.75)

nlp.add_pipe(coref, name='neuralcoref')

```



### Using the conversion dictionary parameter to help resolve rare words



Here is an example on how we can use the parameter `conv_dict` to help resolving coreferences of a rare word like a name:



```python

import spacy

import neuralcoref



nlp = spacy.load('en')



# Let's try before using the conversion dictionary:

neuralcoref.add_to_pipe(nlp)

doc = nlp(u'Deepika has a dog. She loves him. The movie star has always been fond of animals')

doc._.coref_clusters

doc._.coref_resolved

# >>> [Deepika: [Deepika, She, him, The movie star]]

# >>> 'Deepika has a dog. Deepika loves Deepika. Deepika has always been fond of animals'

# >>> Not very good...



# Here are three ways we can add the conversion dictionary

nlp.remove_pipe("neuralcoref")

neuralcoref.add_to_pipe(nlp, conv_dict={'Deepika': ['woman', 'actress']})

# or

nlp.remove_pipe("neuralcoref")

coref = neuralcoref.NeuralCoref(nlp.vocab, conv_dict={'Deepika': ['woman', 'actress']})

nlp.add_pipe(coref, name='neuralcoref')

# or after NeuralCoref is already in SpaCy's pipe, by modifying NeuralCoref in the pipeline

nlp.get_pipe('neuralcoref').set_conv_dict({'Deepika': ['woman', 'actress']})



# Let's try agin with the conversion dictionary:

doc = nlp(u'Deepika has a dog. She loves him. The movie star has always been fond of animals')

doc._.coref_clusters

# >>> [Deepika: [Deepika, She, The movie star], a dog: [a dog, him]]

# >>> 'Deepika has a dog. Deepika loves a dog. Deepika has always been fond of animals'

# >>> A lot better!

```



## Using NeuralCoref as a server



A simple example of server script for integrating NeuralCoref in a REST API is provided as an example in [`examples/server.py`](examples/server.py).



To use it you need to install falcon first:



```bash

pip install falcon

```



You can then start the server as follows:



```bash

cd examples

python ./server.py

```



And query the server like this:



```bash

curl --data-urlencode "text=My sister has a dog. She loves him." -G localhost:8000

```



There are many other ways you can manage and deploy NeuralCoref. Some examples can be found in [spaCy Universe](https://spacy.io/universe/).



## Re-train the model / Extend to another language



If you want to retrain the model or train it on another language, see our [training instructions](./neuralcoref/train/training.md) as well as our [blog post](https://medium.com/huggingface/how-to-train-a-neural-coreference-model-neuralcoref-2-7bb30c1abdfe)