Develop Reference

Simplify song and artist names

Introduction
Hello. I am currently building a web application that takes a random song and put it into a spotify playlist. (The user can't choose which songs he wants)
So I search the input with the spotify api and get a list of results.
Problem
Since spotify is returning not always the best result, I wanted to loop through the results and find the best matching one. How would you achieve the best result?
My attempt
The first thing I tried, was matching the strings with the fuzzywuzzy library.
This looked something like this:
song_ratio = ratio(real_song_name,result_song_name)
This was good and it helped a lot but what is with songs that just have a different punctuation?
So what I did is removing the punctuation with:
song_name = song_name.translate(str.maketrans('', '', punctuation))
I want also want to avoid Karaoke, Remastered or Live Versions, etc. e.g.:
Stay with Me Till Dawn - Live in the UK, 1982 / 2010 Remaster from Judie Tzuke
Just filtering by this names would make no sense because they appear not in the same shape.
Another problem:
Searching for the song "Fascination" from "Jane Morgan And The Troubadors"
What I get is:
Best found song: Its Been A Long Long Time to 22 % match<br>
Best found artist: Jane Morgan 54 %
Would I just have queried for the song "Fascination" from "Jane Morgan" i would get:
Best found song: Fascination 100 % <br>
Best found artist: Jane Morgan 100 %
Question
What is a good way to solve this issue? Is it possible to train a neural network to process my strings into the right format and then find the best matching?

Something you could try is to use the advanced query syntax offered by Spotify search, and only search for part of the song title/artist name. For example your query for "Fascination" from "Jane Morgan And The Troubadors" could become:
artist:"Jane Mo" track:"Fascin"
and still return the correct result.
This query looks for the exact string 'Jane M' appearing in the artist name and 'Fascin' in the track title.

Python package to extract sentence from a textfile based on keyword

I need a python package that could get the related sentence from a text, based on the keywords provided.
For example, below is the Wikipedia page of J.J Oppenheimer -
Early life
Childhood and education
J. Robert Oppenheimer was born in New York City on April 22, 1904,[note 1][7] to Julius Oppenheimer, a wealthy Jewish textile importer who had immigrated to the United States from Germany in 1888, and Ella Friedman, a painter.
Julius came to the United States with no money, no baccalaureate studies, and no knowledge of the English language. He got a job in a textile company and within a decade was an executive with the company. Ella was from Baltimore.[8] The Oppenheimer were non-observant Ashkenazi Jews.[9]
The first atomic bomb was successfully detonated on July 16, 1945, in the Trinity test in New Mexico.
Oppenheimer later remarked that it brought to mind words from the Bhagavad Gita: "Now I am become Death, the destroyer of worlds.
If my passed string is - "JJ Oppenheimer birth date", it should return "J. Robert Oppenheimer was born in New York City on April 22, 1904"
If my passed string is - "JJ Openheimer Trinity test", it should return "The first atomic bomb was successfully detonated on July 16, 1945, in the Trinity test in New Mexico"
I tried searching a lot but nothing comes closer to what I want and I don't know much about NLP vectorization techniques. It would be great if someone please suggest some package if they know(or exist).

You could use fuzzywuzzy.
fuzz.ratio(search_text, sentence).
This gives you a score of how similar two strings are.
https://github.com/seatgeek/fuzzywuzzy

I am pretty sure a Module exists that could do this for you, you could try and make it yourself by parsing through the text and creating words like: ["date of birth", "born", "birth date", etc] and you do this for multiple fields. This would thus allow you to find information that would be available.
The idea is:
you grab your text or whatever u have,
you grab what you are looking for (example date of birth)
You then assign a date of birth to a list of similar words,
you look through ur file to see if you find a sentence that has that in it.
I am pretty sure there is no module, maybe I am wrong but smth like this should work.

The task you describe looks like Information Retrieval. Given a query (the keywords) the model should return a list of document (the sentences) that best matches the query.
This is essentially what the response using fuzzywuzzy is suggesting. But maybe just counting the number of occurences of the query words in each sentence is enough (and more efficient).
The next step would be to use Tf-Idf. It is a weighting scheme, that gives high scores to words that are specific to a document with respect to a set of document (the corpus).
This results in every document having a vector associated, you will then be able to sort the documents according to their similarity to the query vector. SO Answer to do that

Want to extract text from a text or pdf file as different paragraphs

Check the following text piece
IN THE HIGH COURT OF GUJARAT AT AHMEDABAD
R/CRIMINAL APPEAL NO. 251 of 2009
FOR APPROVAL AND SIGNATURE:
HONOURABLE MR.JUSTICE R.P.DHOLARIA
==========================================================
1 Whether Reporters of Local Papers may be allowed to see the judgment ?
2 To be referred to the Reporter or not ?
3 Whether their Lordships wish to see the fair copy of the judgment ?
4 Whether this case involves a substantial question of law as to the interpretation of the Constitution of India or any order made thereunder ?
========================================================== STATE OF GUJARAT,S M RAO,FOOD INSPECTOR,OFFICE OF THE Versus DHARMESHBHAI NARHARIBHAI GANDHI ========================================================== Appearance: MS HB PUNANI, APP (2) for the Appellant(s) No. 1 MR DK MODI(1317) for the Opponent(s)/Respondent(s) No. 1 ==========================================================
CORAM: HONOURABLE MR.JUSTICE R.P.DHOLARIA
Date : 12/03/2019
ORAL JUDGMENT
1. The appellant State of Gujarat has
preferred the present appeal under section 378(1)
(3) of the Code of Criminal Procedure, 1973
against the judgment and order of acquittal dated
Page 1 of 12
R/CR.A/251/2009 JUDGMENT
17.11.2008 rendered by learned 2nd Additional
Civil Judge and Judicial Magistrate, First Class,
Nadiad in Food Case No.1 of 2007.
The short facts giving rise to the
present appeal are that on 10.11.2006 at about
18.00 hours, the complainant visited the place of
the respondent accused situated at Juna
Makhanpura, Rabarivad, Nadiad along with panch
witness and the respondent was found dealing in
provisional items. The complainant identified
himself as a Food Inspector and after giving
intimation in Form No.6 has purchased muddamal
sample of mustard seeds in the presence of the
panchas for the purpose of analysis. Thereafter,
the complainant Food Inspector has divided the
said sample in equal three parts and after
completing formalities of packing and sealing
obtained signatures of the vendor and panchas and
out of the said three parts, one part was sent to
the Public Analyst, Vadodara for analysis and
remaining two parts were sent to the Local Health
Authority, Gandhinagar. Thereafter, the Public
Analyst forwarded his report. In the said report,
it is stated that the muddamal sample of mustard
seeds is misbranded which is in breach of the
provisions of the Food Adulteration Act, 1954
(for short “the Act”) and the Rules framed
thereunder. It is alleged that, therefore, the
sample of mustard seeds was misbranded and,
thereby, the accused has committed the offence.
**Page 2 of 12
R/CR.A/251/2009* JUDGMENT*
Hence, the complaint came to be lodged against
the respondent accused.
I want to be able to write a program such that it follows the given constraints. Be wary of the fact that this is only a single file i have like 40k files and it should run on all the files. All the files have some difference but the basic format for every file is the same.
Constraints.
It should start the text extraction process from after the "metadata" . Metadata is the data about the file from the starting of the file i.e " In the high court of gujarat" till Oral Judgment. In all the files i have , there are various POINTS after the string ends. So i need all these points as a separate paragraph ( see the text has 2 points , i need it in different paragraphs ).
Check the lines in italics, these are the panes in the text/pdf file. I need to remove these as these donot have any meaning to the text content i want.
These files are both available in TEXT or PDF format so i can use either. But i am new to python so i dont know how and where to start. I just have basic knowledge in python.
This data is going to be made into a "corpus" for further processes in building a huge expert system so you know what needs to be done i hope.

Read the official python docs!
Start with python's basic str type and its methods. One of its methods, find, will find substrings in your text.
Use the python slicing notation to extract the portion of text you need, e.g.
text = """YOUR TEXT HERE..."""
meta_start = 'In the high court of gujarat'
meta_end = 'ORAL JUDGMENT'
pos1 = text.find(meta_start)
pos2 = text.find(meta_end)
if pos2 > pos1 and pos1 > -1:
# text is found, extract it
text1 = text[meta_start + len(meta_start):meta_end - 1]
After that, you can go ahead and save your extracted text to a database.
Of course, a better and more complicated solution would be to use regular expressions, but that's another story -- try finding the right way for yourself!
As to italics and other text formatting, you won't ever be able to mark it out in plain text (unless you have some 'meta' markers, like e.g. [i] tags).

Matching company names in the news data using Python

I have news dataset which contains almost 10,000 news over the last 3 years.
I also have a list of companies (names of companies) which are registered in NYSE. Now I want to check whether list of company names in the list have appeared in the news dataset or not.
Example:
company Name: 'E.I. du Pont de Nemours and Company'
News: 'Monsanto and DuPont settle major disputes with broad patent-licensing deal, with DuPont agreeing to pay at least $1.75 billion over 10 years for rights to technology for herbicide-resistant soybeans.'
Now, I can find the news contains company name if the exact company name is in the news but you can see from the above example it is not the case.
I also tried another way i.e. I took the integral name in the company's full name i.e. in the above example 'Pont' is a word which should be definitely a part of the text when this company name is called. So it worked for majority of the times but then problem occurs in the following example:
Company Name: Ennis, Inc.
News: L D`ennis` Kozlowski, former chief executive convicted of looting nearly $100 million from Tyco International, has emerged into far more modest life after serving six-and-a-half year sentence and probation; Kozlowski, who became ultimate symbol of corporate greed in era that included scandals at Enron and WorldCom, describes his personal transformation and more humble pleasures that have replaced his once high-flying lifestyle.
Now you can see Ennis is matching with Dennis in the text so it giving irrelevant news results.
Can someone help in telling the right way of doing this ? Thanks.

Use a regex with boundaries for exact matches whether you choose the full name or some partial part you think is unique is up to you but using word boundaries D'ennis' won't match Ennis :
companies = ["name1", "name2",...]
companies_re = re.compile(r"|".join([r"\b{}\b".format(name) for name in companies]))
Depending on how many matches per news item, you may want to use companies_re.search(artice) or companies_re.find_all(article).
Also for case insensitive matches pass re.I to compile.
If the only line you want to check is also always the one starting with company company Name: you can narrow down the search:
for line in all_lines:
if line.startswith("company Name:"):
name = companies_re.search(line)
if name:
...
break

It sounds like you need the Aho-Corasick algorithm. There is a nice and fast implementation for python here: https://pypi.python.org/pypi/pyahocorasick/
It will only do exact matching, so you would need to index both "Du pont" and "Dupont", for example. But that's not too hard, you can use the Wikidata to help you find aliases: for example, look at the aliases of Dupont's entry: it includes both "Dupont" and "Du pont".
Ok so let's assume you have the list of company names with their aliases:
import ahocorasick
A = ahocorasick.Automaton()
companies = ["google", "apple", "tesla", "dupont", "du pont"]
for idx, key in enumerate(companies):
A.add_word(key, idx)
Next, make the automaton (see the link above for details on the algorithm):
A.make_automaton()
Great! Now you can simply search for all companies in some text:
your_text = """
I love my Apple iPhone. Do you know what a Googleplex is?
I ate some apples this morning.
"""
for end_index, idx in A.iter(your_text.lower()):
print(end_index, companies[idx])
This is the output:
15 apple
49 google
74 apple
The numbers correspond to the index of the last character of the company name in the text.
Easy, right? And super fast, this algorithm is used by some variants of GNU grep.
Saving/loading the automaton
If there are a lot of company names, creating the automaton may take some time, so you may want to create it just once, save it to disk (using pickle), then load it every time you need it:
# create_company_automaton.py
# ... create the automaton (see above)
import pickle
pickle.dump(A, open('company_automaton.pickle', 'wb'))
In the program that will use this automaton, you start by loading the automaton:
# use_company_automaton.py
import ahocorasick
import pickle
A = pickle.load(open("company_automaton.pickle", "rb"))
# ... use the automaton
Hope this helps! :)
Bonus details
If you want to match "Apple" in "Apple releases a new iPhone" but not in "I ate an apple this morning", you are going to have a hard time. But it is doable: for example, you could gather a set of articles containing the word "apple" and about the company, and a set of articles not about the company, then identify words (or n-grams) that are more likely when it's about the company (e.g. "iPhone"). Unfortunately you would need to do this for every company whose name is ambiguous.

You can try
difflib.get_close_matches
with the full company name.

Python: libphonenumber - cannot get extract out obvious phone numbers

I am using Python version of Google's libphonenumbers, but when I tried this library on different texts, sometimes it will the python function will not return me anything while it is very obvious that there a phone number there and sometimes they do return the phone numbers. Please see below:
print(x2)
for match in pnum.PhoneNumberMatcher(x2, "US"):
print(match) #for the text above, it did not get the number
output:
I just read your profile and thought it was really great. I also thought you were cute and loved the fact that you go hiking with your brothers every summer. If you want to know anything more about me, just ask. My num 555-121-5468.
With this text above, it does not return me any phone number.
But in other situation like the following, this function gives me correct input:
x9 = "hay I hate to cut you short, its been fun chatting, but unfortuantely I gotta run. I am gald we became friends though. my number is (323) 2387890"
for match in pnum.PhoneNumberMatcher(x9, "US"):
print(match)
output:
PhoneNumberMatch [132,145) (323) 2387890
I don't know what is the issue causing this problem, I am new to Python and this library and would sincerely appreciate insight.

555-121-5468 looks like a valid US phone number, but actually it isn't.
The PhoneNumberMatcher class constructor accepts a leniency argument which defines how strictly the class matches candidate phone numbers (code). The possible values of this argument The default value of leniency is 1, which will only match valid phone numbers. Changing this to 0 will match possible phone numbers like 555-121-5468.
>>> for match in pnum.PhoneNumberMatcher(x2, 'US', leniency=0):
print(match)
...
PhoneNumberMatch [220,232) 555-121-5468
The 555 prefix is not a real prefix, but is used for fictional phone numbers in US TV and cinema. From Wikipedia:
Telephone numbers with the prefix 555 are widely used for fictitious
telephone numbers in North American television shows, films, video
games, and other media in order to prevent practical jokers and
curious callers from bothering real people and organisations by
telephoning numbers they see in works of fiction; generally, in North
America, a number with 555 as a prefix will not connect to a real
person.