Christopher-Thornton

import hmni

# Initialize a Matcher Object
matcher = hmni.Matcher(model='latin')

# Single Pair Similarity
matcher.similarity('Alan', 'Al')
# 0.6838303319889133
matcher.similarity('Alan', 'Al', prob=False)
# 1

Christopher-Thornton

def wiki_page(page_name):
    wiki_api = wikipediaapi.Wikipedia(language='en',
            extract_format=wikipediaapi.ExtractFormat.WIKI)
    page_name = wiki_api.page(page_name)
    if not page_name.exists():
        print('Page {} does not exist.'.format(page_name))
        return

    page_data = pd.DataFrame({
        'page': page_name,

Christopher-Thornton

import itertools

def prepro(pairs, filterout=None, hard_lmt=100000):
    pairs['relation'] = 1
    G = nx.from_pandas_edgelist(pairs, 'subject', 'object',
                                create_using=nx.DiGraph())
    if filterout:
        nodes = \
            list(set(pairs[~pairs.subject_type.isin(filterout)]['subject'].tolist()
                 + pairs[~pairs.object_type.isin(filterout)]['object'].tolist()))

Christopher-Thornton

def filter_graph(pairs, node):
    k_graph = nx.from_pandas_edgelist(pairs, 'subject', 'object',
            create_using=nx.MultiDiGraph())
    edges = nx.dfs_successors(k_graph, node)
    nodes = []
    for k, v in edges.items():
        nodes.extend([k])
        nodes.extend(v)
    subgraph = k_graph.subgraph(nodes)
    layout = (nx.random_layout(k_graph))

Christopher-Thornton

import pandas as pd
import re
import spacy
import neuralcoref

nlp = spacy.load('en_core_web_lg')
neuralcoref.add_to_pipe(nlp)


def get_entity_pairs(text, coref=True):

Christopher-Thornton

import networkx as nx
import matplotlib.pyplot as plt


def draw_kg(pairs):
    k_graph = nx.from_pandas_edgelist(pairs, 'subject', 'object',
            create_using=nx.MultiDiGraph())
    node_deg = nx.degree(k_graph)
    layout = nx.spring_layout(k_graph, k=0.15, iterations=20)
    plt.figure(num=None, figsize=(120, 90), dpi=80)

Christopher-Thornton

import wikipediaapi  # pip install wikipedia-api
import pandas as pd
import concurrent.futures
from tqdm import tqdm

def wiki_scrape(topic_name, verbose=True):
    def wiki_link(link):
        try:
            page = wiki_api.page(link)
            if page.exists():

Christopher-Thornton

import urllib.request
from bs4 import BeautifulSoup
import spacy
import neuralcoref
nlp = spacy.load('en_core_web_lg')
neuralcoref.add_to_pipe(nlp)

html = urllib.request.urlopen('https://www.law.cornell.edu/supremecourt/text/418/683').read()
soup = BeautifulSoup(html, 'html.parser')
text = ''.join([t for t in soup.find_all(text=True) if t.parent.name == 'p' and len(t) >= 25])

Christopher-Thornton

venv .env
source .env/bin/activate
git clone https://github.com/huggingface/neuralcoref.git
cd neuralcoref
pip install -r requirements.txt
pip install -e .