neuralcoref | ✨Fast Coreference Resolution in spaCy with Neural Networks | Natural Language Processing library
kandi X-RAY | neuralcoref Summary
kandi X-RAY | neuralcoref Summary
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. 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, a web interface powered by a REST server that can be tried online. NeuralCoref is released under the MIT license. Version 4.0 out now! Available on pip and compatible with SpaCy 2.1+.
Support
Quality
Security
License
Reuse
Top functions reviewed by kandi - BETA
- Train model
- Load embeddings from file
- Build a test file
- Merge the coreference clusters
- Reads data_path into parallel_conll files
- Creates a list of Mention objects
- Given a list of tokens and spacy_tokens and spacy_tokens and spacy_tokens
- Add a conll utterance
- Setup the package
- Context manager to change working directory
- Generate cythonize
- List all detected mentions
- Reads files into parallel processes
- Return the path to a local file or a local path
- Download a file from the local cache
- Save the tunable vocabulary
- Return the stat for a word
- Check if words in embedding vocabulary are in embedding vocabulary
- Normalize a word
- Load utterance from a file
- Clean up a token
- Build a key file from a corpus
- Get the resolved utterances
- Use spacy to detect mentions
- Check all files in root directory
- Build and gather two arrays
- Run one - shot coref on the given corpus
neuralcoref Key Features
neuralcoref Examples and Code Snippets
Lines of Code : 10License : Strong Copyleft (CC BY-SA 4.0)
Copyimport string
sents = doc1._.coref_resolved.split(". ")
sents_wo_punct = []
for sent in sents:
sents_wo_punct.append(sent.translate(str.maketrans("", "", string.punctuation)))
print(sents_wo_punct)
['My sisterimport spacy
import neuralcoref
nlp = spacy.load('en_core_web_sm')
neuralcoref.add_to_pipe(nlp)
texts = ['My sister has a dog. She loves him.','Angela lives in Boston. She is quite happy in that city.']
docs = nlp.pipe(texts)
inp = []
ou[My sister: [My sister, She], a dog: [a dog, him]]
Angela: [Angela, She]
Boston: [Boston, that city]
dict(Counter(map(tuple, token))).most_common())
pip install msgpack==0.5.6 spacy==2.0.13 https://github.com/huggingface/neuralcoref-models/releases/download/en_coref_md-3.0.0/en_coref_md-3.0.0.tar.gz
mkdir temp
cd temp
!git clone https://github.com/huggingface/neuralcoref.git
!pip install -U spacy
!python -m spacy download en
cd neuralcoref
!pip install -r requirements.txt
!pip install -e .
import neuralcoref
import spacy
nlp = def remove_unserializable_results(doc):
doc.user_data = {}
for x in dir(doc._):
if x in ['get', 'set', 'has']: continue
setattr(doc._, x, None)
for token in doc:
for x in dir(import spacy
import neuralcoref
nlp = spacy.load('en_core_web_sm')
neuralcoref.add_to_pipe(nlp)
doc = nlp('London is the capital of and largest city in England and the United Kingdom. It was founded by the Romans.')
print(doc._.coref_clCommunity Discussions
Trending Discussions on neuralcoref
QUESTION
I am facing an issue when trying to call spaCy into my Jupyter notebook. When I run
import spacy I get the below:
I have used spaCy before many times with no issue, but I noticed this problem began after I was trying to also install from neuralcoref import Coref and am not sure if that has caused this.
When I go into the terminal and run conda list spacy it looks like spaCy is available:
I do not really understand what the errors are suggesting, but I tried to reinstall murmurhash using conda install -c anaconda murmurhash after which I got this. This is just a screenshot of the first few but there are MANY packages that are allegedly causing the inconsistency:
Following the list of packages causing inconsistencies, I get this:
For reference, I am using MacOS and python 3.7. How can I fix this?
...ANSWER
Answered 2021-Apr-15 at 15:33spacy>=3.0 and neuralcoref are currently not compatible - the Cython API of spaCy's v3 has changed too much. This might be causing conflicts in your environment?
QUESTION
I am using the neuralcoref library for coreference resolution. It works on top of the Spacy library. I get it working as by the documentation.
...ANSWER
Answered 2021-Feb-03 at 21:36doc1._.coref_resolved is of str type so you may wish to process it towards your desired output as:
QUESTION
I would like to replace pronouns to nouns like below using Python 3.6 and spaCy neuralcoref.
...ANSWER
Answered 2020-Oct-19 at 07:17neuralcoref has a specially dedicated doc._.coref_resolved method for tasks like this:
QUESTION
I would like to run the sample code for spaCy neuralcoref on jupyter notebook.
ProblemAfter I asked my former questeion, Error to import spaCy neuralcoref module even follwoing the sample code, I have tried to install libraries following another answer to this issue on stackoverflow.
What should I do to run the sample code of spaCy neuralcoref?
Problem 1This part is executable, but notice and output are shown.
...ANSWER
Answered 2020-Jul-11 at 17:11Downgrade to python 3.7. neuralcoref works only for python 3.7 and spaCy 2.1.0.
The best way to fix this in opinion would be alter the requirements.txt of neuralcoref and change spacy>=2.1.0,<2.2.0 to spacy==2.1.0
Hope that helps.
QUESTION
I have been trying to use the library neuralcoref: State-of-the-art coreference resolution based on neural nets and spaCy. I am using Ubuntu 16.04, Python 3.7.3 in conda 1.9.7 and Spacy 2.2.4.
My code (from the https://spacy.io/universe/project/neuralcoref):
...ANSWER
Answered 2020-Jul-11 at 01:32For neuralcoref to work, you need to use spaCy version 2.1.0 and python version 3.7. That is the only combination that neuralcored works for on Ubuntu 16.04 and on Mac.
- Install python 3.7 on your machine, see here
- Make sure the selected version of python is 3.7
- Create your project folder
- Create a python virtual environment in your given project folder like so,
python -m venv ./venv, - Install spaCy 2.1.0 like so
python -m pip install spacy==2.1.0. - Install neuralcoref
python -m pip install neuralcoref
Hope this helps.
After running your code above, I get the following output:
QUESTION
I need to implement a solution which can recognize pronouns associated with the noun in a sentence. Say I have an paragraph about a person, I wanna count how many times the person has been referenced (name or any other pronoun). I want to implement this is Python.
After some research I came across neuralcoref and though it could be useful. After several attempts I'm still getting stuck because the kernel keeps dying.
It would be great if someone can help with this problem. I am also open to suggestions about other libraries/resources I could use to implement this.
Thanks!
This is the code I used:
...ANSWER
Answered 2020-Jul-09 at 06:30You need to use spaCy version 2.1.0 and python version 3.7 for neuralcoref to work. See here for reference
QUESTION
I would like to use Space to extract word relation information in the form of "agent, action, and patient." For example, "Autonomous cars shift insurance liability toward manufacturers" -> ("autonomous cars", "shift", "liability") or ("autonomous cars", "shift", "liability towards manufacturers"). In other words, "who did what to whom" and "what applied the action to something else." I don't know much about my input data, so I can't make many assumptions.
I also want to extract logical relationships. For example, "Whenever/if the sun is in the sky, the bird flies" or cause/effect cases like "Heat makes ice cream melt."
For dependencies, Space recommends iterating through sentences word by word and finding the root that way, but I'm not sure what clear pattern in traversal to use in order to get the information in a reliable way I can organize. My use case involves structuring these sentences into a form that I can use for queries and logical conclusions. This might be comparable to my own mini Prolog data store.
For cause/effect, I could hard-code some rules, but then I still need to find a way of reliably traversing the dependency tree and extracting information. (I will probably combine this with core resolution using neuralcoref and also word vectors and concept net to resolve ambiguities, but this is a little tangential.)
In short, the question is really about how to extract that information / how best to traverse.
On a tangential note, I am wondering if I really need a constituency tree as well for phrase-level parsing to achieve this. I think that Stanford provides that, but Spacy might not.
...ANSWER
Answered 2020-Jun-30 at 16:30To the first part of your question, it's pretty easy to use token.dep_ to identify nsubj, ROOT, and dobj tags.
QUESTION
I'm trying to use the following spacy module in colab:
https://spacy.io/universe/project/neuralcoref
I install the following packages:
...ANSWER
Answered 2020-Apr-17 at 12:50Update:
Since the previous helped solving the first problem but created another problem, I have updated the answer.
According to neuralcoref page, for our version of Spacy, we need to manually install it from the source.
Also, try each of the following blocks in new cell in Colab, and Restart Runtime after installation.
Community Discussions, Code Snippets contain sources that include Stack Exchange Network
Vulnerabilities
No vulnerabilities reported
Install neuralcoref
You can also install NeuralCoref from sources. You will need to install the dependencies first which includes Cython and SpaCy.
Support
Reuse Trending Solutions
Find, review, and download reusable Libraries, Code Snippets, Cloud APIs from over 650 million Knowledge Items
Find more librariesStay Updated
Subscribe to our newsletter for trending solutions and developer bootcamps
Share this Page
