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.

Working with span objects. [spaCy, python]

I'm not sure if this is a really dumb question, but here goes.
text_corpus = '''Insurance bosses plead guilty\n\nAnother three US insurance executives have pleaded guilty to fraud charges stemming from an ongoing investigation into industry malpractice.\n\nTwo executives from American International Group (AIG) and one from Marsh & McLennan were the latest. The investigation by New York attorney general Eliot Spitzer has now obtained nine guilty pleas. The highest ranking executive pleading guilty on Tuesday was former Marsh senior vice president Joshua Bewlay.\n\nHe admitted one felony count of scheming to defraud and faces up to four years in prison. A Marsh spokeswoman said Mr Bewlay was no longer with the company. Mr Spitzer\'s investigation of the US insurance industry looked at whether companies rigged bids and fixed prices. Last month Marsh agreed to pay $850m (£415m) to settle a lawsuit filed by Mr Spitzer, but under the settlement it "neither admits nor denies the allegations".\n'''
def get_entities(document_text, model):
analyzed_doc = model(document_text)
entities = [entity for entity in analyzed_doc.ents if entity.label_ in ["PER", "ORG", "LOC", "GPE"]]
return entities
model = spacy.load("en_core_web_sm")
entities_1 = get_entities(text_corpus, model)
entities_2 = get_entities(text_corpus, model)
but when it run the following,
entities_1[0] in entities_2
The output is False.
Why is that? The objects in both the entity lists are the same. Yet an item from one list is not in the other one. That's extremely odd. Can someone please explain why that is so to me?

This is due to the way ents's are represented in spaCy. They are classes with specific implementations so even entities_2[0] == entities_1[0] will evaluate to False. By the looks of it, the Span class does not have an implementation of __eq__ which, at first glance at least, is the simple reason why.
If you print out the value of entities_2[0] it will give you US but this is simply because the span class has a __repr__ method implemented in the same file. If you want to do a boolean comparison, one way would be to use the text property of Span and do something like:
entities_1[0].text in [e.text for e in entities_2]
edit:
As #abb pointed out, Span implements __richcmp__, however this is applicable to the same instance of Span since it checks the position of the token itself.

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.

How can I parse email text for components like <salutation><body><signature><reply text> etc?

I'm writing an application that analyzes emails and it would save me a bunch of time if I could use a python library that would parse email text down into named components like <salutation><body><signature><reply text> etc.
For example, the following text "Hi Dave,\nLets meet up this Tuesday\nCheers, Tom\n\nOn Sunday, 15 May 2011 at 5:02 PM, Dave Trindall wrote: Hey Tom,\nHow about we get together ..." would be parsed as
Salutation: "Hi Dave,\n"
Body: "Lets meet up this Tuesday\n"
Signature: "Cheers, Tom\n\n"
Reply Text: "On Sunday, 15 May 2011 at 5:02 PM, Dave Trindal wrote: ..."
I know there's no perfect solution for this kind of problem, but even a library that does good approximation would help. Where can I find one?

https://github.com/Trindaz/EFZP
This provides functionality posed in the original question, plus fair recognition of email zones as they commonly appear in email written by native English speakers from common email clients like Outlook and Gmail.

If you score each line based on the types of words it contains you may get a fairly good indication.
E.G. A line with greeting words near the start is the salutation (also salutations may have phrases that refer to the past tense e.g. it was good to see you last time)
A Body will typically contain words such as "movie, concert" etc. It will also contain verbs (go to, run, walk, etc) and questions marks and offerings (e.g. want to, can we, should we, prefer..).
Check out http://nodebox.net/code/index.php/Linguistics#verb_conjugation
http://ogden.basic-english.org/
http://osteele.com/projects/pywordnet/
the signature will contain closing words.
If you find a datasource that has messages of the structure you want you could do some frequency analysis to see how often each word occurs in each section.
Each word would get a score [salutation score, body score, signature score,..]
e.g. hello could occur 900 times in the salutation, 10 times in the body, and 3 times in the signature.
this means hello would get assigned [900, 10, 3, ..]
cheers might get assigned [10,3,100,..]
now you will have a large list of about 500,000 words.
words that don't have a large range aren't useful.
e.g. catch might have [100,101,80..] = range of 21
(it was good to catch up, wanna go catch a fish, catch you later). catch can occur anywhere.
Now you can reduce the number of words down to about 10,000
now for each line, give the line a score also of the form [salutation score, body score, signature score,..]
this score is calculated by adding the vector scores of each word.
e.g. a sentence "hello cheers for giving me your number" could be:
[900, 10, 3, ..] + [10,3,100,..] + .. + .. + = [900+10+..,10+3+..,3+100,..]
=[1023,900,500,..] say
then because the biggest number is at the start in the salutation score position, this sentence is a salutation.
then if you had to score one of your lines to see what component the line should be in, for each word you would add on its score
Good luck, there is always a trade-off between computation complexity and accuracy. If you can find a good set of words and make a good model to base you calculations it will help.

The first approach that comes to mind (not necessarily the best...) would be to start off by using split. here's a little bit of code and stuff
linearray=emailtext.split('\n')
now you have an array of strings, each one like a paragraph or whatever
so linearray[0] would contain the salutation
deciding where the reply text starts is a little more tricky, i noticed that there is a double newline just before it so maybe do a search for that from the back and hope that the last one indicates the start of the reply text.
Or store some signature words you might expect and search for those from the front, like cheers, regards, and whatever else.
Once you figure out where the signature is the rest is the rest is easy
hope this helped

I built a pretty cheap API for this actually to parse the contact data from signatures of emails and email chains. It's called SigParser. You can see the Swagger docs here for it.
Basically you send it a header 'x-api-key' with a JSON body like so and it parses all the contacts in the reply chain of an email.
{
"subject": "Thanks for meeting...",
"from_address": "bgates#example.com",
"from_name": "Bill Gates",
"htmlbody": "<div>Hi, good seeing you the other day.</div><div>--</div><div>Bill Gates</div><div>Cell 777-444-8888</div>LinkedInTwitter",
"plainbody": "Hi, good seeing you the other day. \r\n--\r\nBill Gates\r\nCell 777-444-8888",
"date": "Mon, 28 May 2018 23:33:40 +0000 (UTC)"
}