when I try to import nltk though the python jupyter notebook, it does not seem to work. According to previous posts here I should simply use the command Alteryx.installPackages("nltk"), but this gives the error
Two approaches have been suggested to me by @danilang -- both utilize Alteryx's Python tool (POTENTIAL SOLUTIONS). Option 1 utilizes the NLTK library to parse sentences; Option 2 utilizes Regex. There is only one small problem, I do NOT know Python (yet - Alteryx is my side hobby). My original question
Download Nltk Library
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I was able to successfully install the NLTK package... but from there I do not know how to tell Python to parse a particular field (vs for example a file) using NLTK or Regex and then output to Alteryx...
nltk.load('punkt') is used to download a pre-trained english language sentence parsing library that handles most of the edge cases, i.e. trailing periods don't always mark sentence boundaries (Ms.), etc. The next line builds a tokenizer using the rules in the Punkt library.
I assume the [0,1] is a row, column index. I can add columns and change the column index from 0,1 to 0,2 etc. to target the specific column I want to parse. Adding rows and changing the 0,1 to 1,1 to target a specific row did not work. So, is the 0 in 0,1 a row reference or something else?
The .iloc[r,c] syntax is a way to reference any cell in a dataframe using r(ow) and c(olumn) indices, both 0 based. So .iloc[0,0] works in your example to access the RecordID column in the first row, but there is no second row so .iloc[1,x] fails.
As for next steps, give SpaCy a try, specifically pySBD. It's built based on the "Golden Rules" and should be able handle most cases. Case 16 in this list actually deals with U.S. followed by a capitalized letter, which was one of the breaking cases in your example.
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A great example of this is some work we did in the telco space where they wished to be able to work on support tickets offshore. To be able to diagnose problems with radio towers, networking equipment and the associated software it was important to be able to follow an IP address or an MSISDN through various pieces of equipment and their individual log files to track down issues and gather enough knowledge to get to a resolution. This obviously can present some problems with privacy regulations such as GDPR because an IP or subscriber number can be linked to an individual and the additional information known about the issue for the troubleshooting such as cell tower id and location could rightly be seen as a breach of privacy.
Voltage is well placed to tackle this with our data classification tool, Fusion, which brings together market leading capabilities in data discovery and classification across structured and unstructured data sets. Fusion uses many techniques to achieve this from regular expressions, through dictionaries and term lists through context awareness and extensions to calculate things such as check digits to minimise false positives.
Then combining this with our data protection tool, SecureData, to bring Format-Preserving Encryption to the solution we were able to discover and protect (while preserving referential integrity) the data so that it was able to be shared more widely. Essentially the data had been defanged.
Not that what I present below represents a like for like comparison but I wanted to explore NLP and see how close it was able to get, and how quickly. As a bad python programmer, I choose the nltk library as it seemed to be well documented and looked like it had the functions needed for entity identification. I suspect that any NLP library will offer similar capabilities.
We start by passing the input string into the word tokenizer function and the output of this into the pos_tag function, assigning the results to a variable called bar. This breaks down the string and assigns each word with a tag, such as 'Adjective' or 'Proper Noun'. There are many tags and a full list can be found on the nltk web page. Some are shown here :-
Next we need to process the JSON that NLTK creates so we can work with it in an easier fashion in python. In the below code block I define some array variables and then loop through the parsed sentences keeping the values we are interested in based on the tags. I have chosen to keep numeral, cardinal, noun, proper, singular, and pronoun, personal.
Now we have two steps remaining, apply a treatment to the values the NLP model has tagged as having the type of values we want to protect, I will do this with Format-Preserving Encryption using the SecureData RESTful JSON interface and then finally replacing the original data.
Leaving us three arrays all the same length, one containing the words from our input text, one containing the tag applied by NLTK and the final one containing the protected version. To carry out the replacement python makes this very simple using the string.replace function.
I appreciate this is a very simple and quick example and is not a replacement for the full discovery and classification solutions that Voltage bring to the market but for such a small amount of work the output is impressive and you will note that Daniel Smith and Mr Smith are protected consistently.
From just over 80 lines of code, 20 are converting JSON to arrays and lists and doing python things, another 20 are used to apply FPE protection, 6 for the text replacement and only 6 for the entity extraction and NLP tagging.
Well, do you get a picture for a very short sentence like "Cats eat dogs.? If not, there would be an infinite loop somewhere (post your code in this case). If yes, gradually add sentences and see where you can get (I am new to nltk so I have no idea of an order of magnitude here).
I tried to make simple web app to test the interaction of NLTK in PythonAnywhere but received a"500 internal server error". What I tried to do was to get a text query from the user and return nltk.word_tokenize(). My init.py funcion contains:
This word_tokenizer is such a frequent feature that it's lack of functioning inPythonAnywhere should be considered a bug in the PythonAnywhere installation of theNLTK library. At least that's my opinion and suggestion.
Thanks for details on getting the nltk download. For anyone else who may need theparticular file required by nltk.word_tokenize , the download code is 'punkt',so nltk.download('punkt') does the download.Incidentally, the download puts the file in a place that the nltk calling method knows about,which is a nice detail.
If we want to download all packages from the NLTk library, then by using the above command, we can download the packages that will unzip all the packages from NLTK Corpus, for example, Stemmer, lemmatizer, and many more.
Python 2 and 3 live in different worlds, they have their own environments and packages. In this case, if you just need a globally installed package available from the system Python 3 environment, you can use apt to install python3-nltk:
Developing things against the system Python environment is a little risky though. As You update to newer releases of Ubuntu, these packages will update too. That can cause breakages. It can also mean you're held back on an older package of something.
The official installation instructions would have you install the package with pip (or pip3) into the system environment. This will likely work but could have serious ramifications on the system you're doing it to. Ubuntu itself needs a Python environment so it's best not to mess around with it outside the things that a properly packaged.
Additionally, it passes the -U flag which will upgrade the package and anything it depends on to the latest PyPI-available version. Great for getting the latest and greatest, but what happens when you inadvertantly upgrade somehthing Ubuntu requires to an incompatible version?
Text-based communication has become one of the most common forms of expression. We email, text message, tweet, and update our statuses on a daily basis. As a result, unstructured text data has become extremely common, and analyzing large quantities of text data is now a key way to understand what people are thinking.
Tweets on Twitter help us find trending news topics in the world. Reviews on Amazon help users purchase the best-rated products. These examples of organizing and structuring knowledge represent Natural Language Processing (NLP) tasks.
NLP is a field of computer science that focuses on the interaction between computers and humans. NLP techniques are used to analyze text, providing a way for computers to understand human language. A few examples of NLP applications include automatic summarization, topic segmentation, and sentiment analysis.
For this tutorial, you should have Python 3 installed, as well as a local programming environment set up on your computer. If this is not the case, you can get set up by following the appropriate installation and set up guide for your operating system.
A noun, in its most basic definition, is usually defined as a person, place, or thing. For example, a movie, a book, and a burger are all nouns. Counting nouns can help determine how many different topics are being discussed.
An adjective is a word that modifies a noun (or pronoun), for example: a horrible movie, a funny book, or a delicious burger. Counting adjectives can determine what type of language is being used, i.e. opinions tend to include more adjectives than facts.
You could later extend this script to count positive adjectives (great, awesome, happy, etc.) versus negative adjectives (boring, lame, sad, etc.), which could be used to analyze the sentiment of tweets or reviews about a product or movie, for example. This script provides data that can in turn inform decisions related to that product or movie. 152ee80cbc
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