Implementing Pure Language Processing and Graph Idea to match and advocate several types of paperwork
Most of the initiatives individuals develop at this time usually start with the primary essential step: Lively Analysis. Investing in what different individuals have executed and constructing on their work is vital on your mission’s means so as to add worth. Not solely must you study from the sturdy conclusions of what different individuals have executed, however you additionally will wish to determine what you shouldn’t do in your mission to make sure its success.
As I labored by way of my thesis, I began amassing a number of several types of analysis recordsdata. For instance, I had collections of various tutorial publications I learn by way of in addition to excel sheets with info containing the outcomes of various experiments. As I accomplished the analysis for my thesis, I questioned: Is there a approach to create a advice system that may examine all of the analysis I’ve in my archive and assist information me in my subsequent mission?
In truth, there’s!
Observe: Not solely would this be for a repository of the entire analysis you might be amassing from varied search engines like google and yahoo, however it should additionally work for any listing you’ve got containing varied varieties of completely different paperwork.
I developed this advice with my workforce utilizing Python 3.
There are many APIs that assist this advice system and researching what every particular API can carry out could also be useful on your personal studying.
import string
import csv
from io import StringIO
from pptx import Presentation
import docx2txt
import PyPDF2
import spacy
import pandas as pd
import numpy as np
import nltk
import re
import openpyxl
from nltk.stem import WordNetLemmatizer
from sklearn.feature_extraction.textual content import TfidfVectorizer
from sklearn.metrics.pairwise import cosine_similarity
from gensim.parsing.preprocessing import STOPWORDS as SW
nltk.obtain('stopwords')
nltk.obtain('wordnet')
nltk.obtain('omw-1.4')
nltk.obtain('averaged_perceptron_tagger')
from nltk.corpus import wordnet
import networkx as nx
from networkx.algorithms.shortest_paths import weighted
import glob
The Hurdle
One massive hurdle I needed to overcome was the necessity for the advice machine’s means to match several types of recordsdata. For instance, I wished to see if an excel spreadsheet has info related or is linked to the knowledge inside a PowerPoint and tutorial PDF journal. The trick to doing this was studying each file sort into Python and remodeling every object right into a single string of phrases. This normalizes all the info and permits for the calculation of a similarity metric.
PDF Studying Class
The primary class we are going to have a look at for this mission is the pdfReader class which is ready to format a PDF to be readable in Python. Of all of the file codecs, I’d argue that PDFs are some of the vital since lots of the journal articles downloaded from analysis repositories akin to Google Scholar are in PDF format.
class pdfReader:def __init__(self, file_path: str) -> str:
self.file_path = file_path
def PDF_one_pager(self) -> str:
"""A perform which returns a one line string of the
pdf.
Returns:
one_page_pdf (str): A one line string of the pdf.
"""
content material = ""
p = open(self.file_path, "rb")
pdf = PyPDF2.PdfReader(p)
num_pages = len(pdf.pages)
for i in vary(0, num_pages):
content material += pdf.pages[i].extract_text() + "n"
content material = " ".be part of(content material.exchange(u"xa0", " ").strip().cut up())
page_number_removal = r"d{1,3} of d{1,3}"
page_number_removal_pattern = re.compile(page_number_removal, re.IGNORECASE)
content material = re.sub(page_number_removal_pattern, '',content material)
return content material
def pdf_reader(self) -> str:
"""A perform which may learn .pdf formatted recordsdata
and returns a python readable pdf.
Returns:
read_pdf: A python readable .pdf file.
"""
opener = open(self.file_path,'rb')
read_pdf = PyPDF2.PdfFileReader(opener)
return read_pdf
def pdf_info(self) -> dict:
"""A perform which returns an info dictionary of a
pdf.
Returns:
dict(pdf_info_dict): A dictionary containing the meta
knowledge of the thing.
"""
opener = open(self.file_path,'rb')
read_pdf = PyPDF2.PdfFileReader(opener)
pdf_info_dict = {}
for key,worth in read_pdf.documentInfo.gadgets():
pdf_info_dict[re.sub('/',"",key)] = worth
return pdf_info_dict
def pdf_dictionary(self) -> dict:
"""A perform which returns a dictionary of
the thing the place the keys are the pages
and the textual content throughout the pages are the
values.
Returns:
dict(pdf_dict): A dictionary pages and textual content.
"""
opener = open(self.file_path,'rb')
read_pdf = PyPDF2.PdfReader(opener)
size = read_pdf.pages
pdf_dict = {}
for i in vary(size):
web page = read_pdf.getPage(i)
textual content = web page.extract_text()
pdf_dict[i] = textual content
return pdf_dict
Microsoft Powerpoint Reader
The pptReader class is able to studying Microsoft Powerpoint recordsdata into Python.
class pptReader:def __init__(self, file_path: str) -> None:
self.file_path = file_path
def ppt_text(self) -> str:
"""A perform that returns a string of textual content from all
of the slides in a pptReader object.
Returns:
textual content (str): A single string containing the textual content
inside every slide of the pptReader object.
"""
prs = Presentation(self.file_path)
textual content = str()
for slide in prs.slides:
for form in slide.shapes:
if not form.has_text_frame:
proceed
for paragraph in form.text_frame.paragraphs:
for run in paragraph.runs:
textual content += ' ' + run.textual content
return textual content
Microsoft Phrase Doc Reader
The wordDocReader class can be utilized for studying Microsoft Phrase Paperwork in Python. It makes use of the doc2txt API and returns a string of the textual content/info situated inside a given phrase doc.
class wordDocReader:
def __init__(self, file_path: str) -> str:
self.file_path = file_pathdef word_reader(self):
"""A perform that transforms a wordDocReader object right into a Python readable
phrase doc."""
textual content = docx2txt.course of(self.file_path)
textual content = textual content.exchange('n', ' ')
textual content = textual content.exchange('xa0', ' ')
textual content = textual content.exchange('t', ' ')
return textual content
Microsft Excel Reader
Typically researchers will embody excel sheets of their outcomes with their publications. Having the ability to learn the column names, and even the values, might assist with recommending outcomes which are like what you might be looking for. For instance, what should you had been researching info on the previous efficiency of a sure inventory? Possibly you seek for the identify and image which is annotated in a historic efficiency excel sheet. This advice system would advocate the excel sheet to you to assist together with your analysis.
class xlsxReader:def __init__(self, file_path: str) -> str:
self.file_path = file_path
def xlsx_text(self):
"""A perform which returns the string of an
excel doc.
Returns:
textual content(str): String of textual content of a doc.
"""
inputExcelFile = self.file_path
textual content = str()
wb = openpyxl.load_workbook(inputExcelFile)
#This may save the excel sheet as a CSV file
for sn in wb.sheetnames:
excelFile = pd.read_excel(inputExcelFile, engine = 'openpyxl', sheet_name = sn)
excelFile.to_csv("ResultCsvFile.csv", index = None, header=True)
with open("ResultCsvFile.csv", "r") as csvFile:
traces = csvFile.learn().cut up(",") # "rn" if wanted
for val in traces:
if val != '':
textual content += val + ' '
textual content = textual content.exchange('ufeff', '')
textual content = textual content.exchange('n', ' ')
return textCSV File Reader
The csvReader class will enable for CSV recordsdata to be included in your database and for use within the system’s suggestions.
class csvReader:def __init__(self, file_path: str) -> str:
self.file_path = file_path
def csv_text(self):
"""A perform which returns the string of a
csv doc.
Returns:
textual content(str): String of textual content of a doc.
"""
textual content = str()
with open(self.file_path, "r") as csvFile:
traces = csvFile.learn().cut up(",") # "rn" if wanted
for val in traces:
textual content += val + ' '
textual content = textual content.exchange('ufeff', '')
textual content = textual content.exchange('n', ' ')
return textMicrosoft PowerPoint Reader
Right here’s a useful class. Not many individuals take into consideration how there’s priceless info saved throughout the our bodies of PowerPoint displays. These displays are by and enormous created to visualise key concepts and data to the viewers. The next class will assist relate any PowerPoints you’ve got in your database to different our bodies of knowledge in hopes of steering you in the direction of linked items of labor.
class pptReader:def __init__(self, file_path: str) -> str:
self.file_path = file_path
def ppt_text(self):
"""A perform which returns the string of a
Mirocsoft PowerPoint doc.
Returns:
textual content(str): String of textual content of a doc.
"""
prs = Presentation(self.file_path)
textual content = str()
for slide in prs.slides:
for form in slide.shapes:
if not form.has_text_frame:
proceed
for paragraph in form.text_frame.paragraphs:
for run in paragraph.runs:
textual content += ' ' + run.textual content
return textMicrosoft Phrase Doc Reader
The ultimate class for this technique is a Microsoft Phrase doc reader. Phrase paperwork are one other priceless supply of knowledge. Many individuals will write stories, indicating their findings and concepts in phrase doc format.
class wordDocReader:
def __init__(self, file_path: str) -> str:
self.file_path = file_pathdef word_reader(self):
"""A perform which returns the string of a
Microsoft Phrase doc.
Returns:
textual content(str): String of textual content of a doc.
"""
textual content = docx2txt.course of(self.file_path)
textual content = textual content.exchange('n', ' ')
textual content = textual content.exchange('xa0', ' ')
textual content = textual content.exchange('t', ' ')
return textual content
That’s a wrap for the lessons utilized in at this time’s mission. Please be aware: there are tons of different file varieties you need to use to reinforce your advice system. A present model of the code being developed will settle for pictures and attempt to relate them to different paperwork inside a database!
Preprocessing
Let’s have a look at how one can preprocess this knowledge. This advice system was constructed for a repository of educational analysis, due to this fact the necessity to break the textual content down utilizing the preprocessing steps guided by Pure Language Processing (NLP) was vital.
The info processing class is just referred to as datapreprocessor and the primary perform throughout the class is a phrase elements of speech tagger.
class dataprocessor:
def __init__(self):
return@staticmethod
def get_wordnet_pos(textual content: str) -> str:
"""Map POS tag to first character lemmatize() accepts
Inputs:
textual content(str): A string of textual content
Returns:
tag_dict(dict): A dictionary of tags
"""
tag = nltk.pos_tag([text])[0][1][0].higher()
tag_dict = {"J": wordnet.ADJ,
"N": wordnet.NOUN,
"V": wordnet.VERB,
"R": wordnet.ADV}
return tag_dict.get(tag, wordnet.NOUN)
This perform tags the elements of speech in a phrase and can come in useful later within the mission.
Second, there’s a perform that conducts the traditional NLP steps many people have seen earlier than. These steps are:
- Lowercase every phrase
- Take away the punctuation
- Take away digits (I solely wished to take a look at non-numeric info. This step might be taken out if desired)
- Stopword elimination.
- Lemmanitizaion. That is the place the get_wordnet_pos() perform turns out to be useful for together with elements of speech!
@staticmethod
def preprocess(textual content: str):
"""A perform that prepoccesses textual content by way of the
steps of Pure Language Processing (NLP).
Inputs:
textual content(str): A string of textual contentReturns:
textual content(str): A processed string of textual content
"""
#lowercase
textual content = textual content.decrease()
#punctuation elimination
textual content = "".be part of([i for i in text if i not in string.punctuation])
#Digit elimination (Just for ALL numeric numbers)
textual content = [x for x in text.split(' ') if x.isnumeric() == False]
#Cease elimination
stopwords = nltk.corpus.stopwords.phrases('english')
custom_stopwords = ['n','nn', '&', ' ', '.', '-', '$', '@']
stopwords.prolong(custom_stopwords)
textual content = [i for i in text if i not in stopwords]
textual content = ' '.be part of(phrase for phrase in textual content)
#lemmanization
lm = WordNetLemmatizer()
textual content = [lm.lemmatize(word, dataprocessor.get_wordnet_pos(word)) for word in text.split(' ')]
textual content = ' '.be part of(phrase for phrase in textual content)
textual content = re.sub(' +', ' ',textual content)
return textual content
Subsequent, there’s a perform to learn the entire recordsdata into the system.
@staticmethod
def data_reader(list_file_names):
"""A perform that reads within the knowledge from a listing of recordsdata.Inputs:
list_file_names(checklist): Checklist of the filepaths in a listing.
Returns:
text_list (checklist): An inventory the place every worth is a string of textual content
for every file within the listing
file_dict(dict): Dictionary the place the keys are the filename and the values
are the knowledge discovered inside every given file
"""
text_list = []
reader = dataprocessor()
for file in list_file_names:
temp = file.cut up('.')
filetype = temp[-1]
if filetype == "pdf":
file_pdf = pdfReader(file)
textual content = file_pdf.PDF_one_pager()
elif filetype == "docx":
word_doc_reader = wordDocReader(file)
textual content = word_doc_reader.word_reader()
elif filetype == "pptx" or filetype == 'ppt':
ppt_reader = pptReader(file)
textual content = ppt_reader.ppt_text()
elif filetype == "csv":
csv_reader = csvReader(file)
textual content = csv_reader.csv_text()
elif filetype == 'xlsx':
xl_reader = xlsxReader(file)
textual content = xl_reader.xlsx_text()
else:
print('File sort {} not supported!'.format(filetype))
proceed
textual content = reader.preprocess(textual content)
text_list.append(textual content)
file_dict = dict()
for i,file in enumerate(list_file_names):
file_dict[i] = (file, file.cut up('/')[-1])
return text_list, file_dict
As that is the primary model of this technique, I wish to foot stomp that the code might be tailored to incorporate many different file varieties!
The following perform is known as the database_preprocess() which is used to course of the entire recordsdata inside your given database. The enter is an inventory of the recordsdata, every with its related string of textual content (processed already). The strings of textual content are then vectorized utilizing sklearn’s tfidVectorizer. What’s that precisely? Mainly, it should rework all of the textual content into completely different function vectors primarily based on the frequency of every given phrase. We do that so we are able to have a look at how intently associated paperwork are utilizing similarity formulation regarding vector arithmetic.
@staticmethod
@staticmethod
def database_processor(file_dict,text_list: checklist):
"""A perform that transforms the textual content of every file throughout the
database right into a vector.Inputs:
file_dixt(dict): Dictionary the place the keys are the filename and the values
are the knowledge discovered inside every given file
text_list (checklist): An inventory the place every worth is a string of the textual content
for every file within the listing
Returns:
list_dense(checklist): An inventory of the recordsdata' textual content become vectors.
vectorizer: The vectorizor used to rework the strings of textual content
file_vector_dict(dict): A dictionary the place the file names are the keys
and the vectors of every recordsdata' textual content are the values.
"""
file_vector_dict = dict()
vectorizer = TfidfVectorizer()
vectors = vectorizer.fit_transform(text_list)
feature_names = vectorizer.get_feature_names_out()
matrix = vectors.todense()
list_dense = matrix.tolist()
for i in vary(len(list_dense)):
file_vector_dict[file_dict[i][1]] = list_dense[i]
return list_dense, vectorizer, file_vector_dict
The explanation a vectorizer is created off of the database is that when a person provides an inventory of phrases to seek for within the database, these phrases will probably be vectorized primarily based on their frequency in stated database. That is the most important weak point of the present system. As we enhance the dimensions of the database, the time and computational allocation wanted for calculating similarities will enhance and decelerate the system. One advice given throughout a high quality management assembly was to make use of Reinforcement Studying for recommending completely different articles of knowledge.
Subsequent, we are able to use an enter processor that processes any phrase supplied right into a vector. That is synonymous to once you sort a request right into a search engine.
@staticmethod
def input_processor(textual content, TDIF_vectorizor):
"""A perform which accepts a string of textual content and vectorizes the textual content utilizing a
TDIF vectorizoer.Inputs:
textual content(str): A string of textual content
TDIF_vectorizor: A pretrained vectorizor
Returns:
phrases(checklist): An inventory of the enter textual content in vectored type.
"""
phrases = ''
total_words = len(textual content.cut up(' '))
for phrase in textual content.cut up(' '):
phrases += (phrase + ' ') * total_words
total_words -= 1
phrases = [words[:-1]]
phrases = TDIF_vectorizor.rework(phrases)
phrases = phrases.todense()
phrases = phrases.tolist()
return phrases
Since the entire info inside and given to the database will probably be vectors, we are able to use cosine similarity to compute the angle between the vectors. The nearer the angle is to 0, the much less related the 2 stated vectors will probably be.
@staticmethod
def similarity_checker(vector_1, vector_2):
"""A perform which accepts two vectors and computes their cosine similarity.Inputs:
vector_1(int): A numerical vector
vector_2(int): A numerical vector
Returns:
cosine_similarity([vector_1], vector_2) (int): Cosine similarity rating
"""
vectors = [vector_1, vector_2]
for vec in vectors:
if np.ndim(vec) == 1:
vec = np.expand_dims(vec, axis=0)
return cosine_similarity([vector_1], vector_2)
As soon as the potential of discovering the similarity rating between two vectors is completed, rankings can now be created between the phrases being searched and the paperwork situated throughout the database.
@staticmethod
def recommender(vector_file_list,query_vector, file_dict):
"""A perform which accepts an inventory of vectors, question vectors, and a dictionary
pertaining to the checklist of vectors with their unique values and file names.Inputs:
vector_file_list(checklist): An inventory of vectors
query_vector(int): A numerical vector
file_dict(dict): A dictionary of filenames and textual content regarding the checklist
of vectors
Returns:
final_recommendation (checklist): An inventory of the ultimate beneficial recordsdata
similarity_list[:len(final_recommendation)] (checklist): An inventory of the similarity
scores of the ultimate suggestions.
"""
similarity_list = []
score_dict = dict()
for i,file_vector in enumerate(vector_file_list):
x = dataprocessor.similarity_checker(file_vector, query_vector)
score_dict[file_dict[i][1]] = (x[0][0])
similarity_list.append(x)
similarity_list = sorted(similarity_list, reverse = True)
#Recommends the highest 20%
beneficial = sorted(score_dict.gadgets(),
key=lambda x:-x[1])[:int(np.round(.5*len(similarity_list)))]
final_recommendation = []
for i in vary(len(beneficial)):
final_recommendation.append(beneficial[i][0])
#add in graph for better than 3 recommendationa
return final_recommendation, similarity_list[:len(final_recommendation)]
The vector file checklist is the checklist of vectors we created from the recordsdata earlier than. The question vector is a vector of the phrases being searched. The file dictionary was created earlier which makes use of file names for the keys and the recordsdata’ textual content as values. Similarities are computed, after which a rating is created favoring probably the most related items of knowledge to the queried phrases being beneficial first. Observe, what if there are better than 3 suggestions? Incorporating components of Networks and Graph Idea will add an additional stage of computational profit to this technique and create extra assured suggestions.
Web page Rank Idea
Let’s take a fast detour and go over the idea of web page rank. Don’t get me unsuitable, cosine similarity is a strong computation for measuring the similarity between vectors, put incorporating web page rank into your advice algorithm permits for similarity comparisons throughout a number of vectors (knowledge inside your database).
Web page rank was first designed by Larry Web page to rank web sites and measure their significance [1]. The fundamental concept is {that a} web site might be deemed “extra vital” if extra web sites are linked to it. Drawing from this concept, a node on a graph might be ranked as extra vital if there’s a lower within the distance of its edge to different nodes. The shorter the collective distance a node has in comparison with different nodes in a graph, the extra vital stated node is.
As we speak we are going to use one variation of PageRank referred to as eigenvector centrality. Eigenvector centrality is like PageRank in that it measures the connections between nodes of a graph, assigning greater scores for stronger connections. Largest distinction? Eigenvector centrality will account for the significance of nodes linked to a given node to estimate how vital that node is. That is synonymous with saying, an individual who is aware of a number of vital individuals could also be crucial themselves by way of these sturdy relationships. All-in-all, these two algorithms are very shut in the best way they’re applied.
For this database, after the vectors are computed, they are often positioned right into a graph the place their edge distance is decided by their similarity to different vectors.
@staticmethod
def ranker(recommendation_val, file_vec_dict):
"""A perform which accepts an inventory of recommendaton values and a dictionary
recordsdata wihin the databse and their vectors.Inputs:
reccomendation_val(checklist): An inventory of suggestions discovered by way of cosine
similarity
file_vec_dic(dict): A dictionary of the filenames as keys and their
textual content in vectors because the values.
Returns:
ec_recommended(checklist): An inventory of the highest 20% suggestions discovered utilizing the
eigenvector centrality algorithm.
"""
my_graph = nx.Graph()
for i in vary(len(recommendation_val)):
file_1 = recommendation_val[i]
for j in vary(len(recommendation_val)):
file_2 = recommendation_val[j]
if i != j:
#Calculate sim_score between two values (weight)
edge_dist = cosine_similarity([file_vec_dict[recommendation_val[i]]],[file_vec_dict[recommendation_val[j]]])
#add an edge from file 1 to file 2 with the burden
my_graph.add_edge(file_1, file_2, weight=edge_dist)
#Pagerank the graph ]
rec = nx.eigenvector_centrality(my_graph)
#Takes 20% of the values
ec_recommended = sorted(rec.gadgets(), key=lambda x:-x[1])[:int(np.round(len(rec)))]
return ec_recommended
Okay, now what? Now we have the suggestions created through the use of the cosine similarity between every knowledge level within the database, and suggestions computed by the eigenvector centrality algorithm. Which suggestions ought to we output? Each!
@staticmethod
def weighted_final_rank(sim_list,ec_recommended,final_recommendation):
"""A perform which accepts an inventory of similiarity values discovered by way of
cosine similairty, suggestions discovered by way of eigenvector centrality,
and the ultimate suggestions produced by cosine similarity.Inputs:
sim_list(checklist): An inventory of the entire similarity values for the recordsdata
throughout the database.
ec_recommended(checklist): An inventory of the highest 20% suggestions discovered utilizing the
eigenvector centrality algorithm.
final_recommendation (checklist): An inventory of the ultimate suggestions discovered
through the use of cosine similarity.
Returns:
weighted_final_recommend(checklist): An inventory of the ultimate suggestions for
the recordsdata within the database.
"""
final_dict = dict()
for i in vary(len(sim_list)):
val = (.8*sim_list[final_recommendation.index(ec_recommendation[i][0])].squeeze()) + (.2 * ec_recommendation[i][1])
final_dict[ec_recommendation[i][0]] = val
weighted_final_recommend = sorted(final_dict.gadgets(), key=lambda x:-x[1])[:int(np.round(len(final_dict)))]
return weighted_final_recommend
The ultimate perform of this script will weigh the completely different suggestions produced by cosine similarity and eigenvector centrality. Presently, 80% of the burden will probably be given to the suggestions produced by the cosine similarity suggestions, and 20% of the burden will probably be given to eigenvector centrality suggestions. The ultimate suggestions might be computed primarily based on these weights and aggregated collectively to supply suggestions which are consultant of all of the similarity computations within the system. The weights can simply be modified by the developer to mirror which batch of suggestions they really feel are extra vital.
Let’s do a fast instance with this code. The paperwork inside my database are all within the codecs beforehand mentioned and pertain to completely different areas of machine studying. Extra paperwork within the database are associated to Generative Adversarial Networks (GANS), so I’d suspect these to be beneficial first when “Generative Adversarial Community” is the question time period.
path = '/content material/drive/MyDrive/database/'
db = [f for f in glob.glob(path + '*')]research_documents, file_dictionary = dataprocessor.data_reader(db)
list_files, vectorizer, file_vec_dict = dataprocessor.database_processor(file_dictionary,research_documents)
question = 'Generative Adversarial Networks'
question = dataprocessor.preprocess(question)
question = dataprocessor.input_processor(question, vectorizer)
advice, sim_list = dataprocessor.recommender(list_files,question, file_dictionary)
ec_recommendation = dataprocessor.ranker(advice, file_vec_dict)
final_weighted_recommended = dataprocessor.weighted_final_rank(sim_list,ec_recommendation, advice)
print(final_weighted_recommended)
Working this block of code produces the next suggestions, together with the burden worth for every advice.
[(‘GAN_presentation.pptx’, 0.3411272882084124), (‘Using GANs to Augment UAV Data_V2.docx’, 0.16293615818015078), (‘GANS_DAY_1.docx’, 0.12546058188955278), (‘ml_pdf.pdf’, 0.10864164490536887)]
Let’s attempt yet one more. What if I question “Machine Studying” ?
[(‘ml_pdf.pdf’, 0.31244922151487337), (‘GAN_presentation.pptx’, 0.18170070184645432), (‘GANS_DAY_1.docx’, 0.14825501243059303), (‘Using GANs to Augment UAV Data_V2.docx’, 0.1309153863914564)]
Aha! As anticipated, the primary doc beneficial is an introductory transient to machine studying! I solely used 7 paperwork for this instance, and the extra paperwork added, the extra suggestions one will obtain!
As we speak we checked out how one can create a advice system for recordsdata you acquire (particularly in case you are amassing analysis for a mission). The primary function of this technique is that it goes one step additional in computing the cosine similarity of vectors by adopting the eigenvector centrality algorithm for extra concise, and higher suggestions. Do this out at this time, and I hope it helps you get a greater understanding of how associated the items of knowledge you possess are.
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