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This presentation will cover a handful of the NLP building blocks provided by NLTK (and a few additional libraries), including extracting text from HTML, stemming lemmatization, frequency analysis, and named entity recognition. Several of these components will then be assembled to build a very basic document summarization program.

Obviously, you'll need Python installed on your system to run the code examples used in this presentation. We enthusiatically recommend using Anaconda , a Python distribution provided by Continuum Analytics . Anaconda is free to use, it includes nearly 200 of the most commonly used Python packages for data analysis (including NLTK), and it works on Mac, Linux, and yes, even Windows.

We'll make use of the following Python packages in the example code:

Please note that the readability package is not distributed with Anaconda, so you'll need to download install it separately using something like or .

readability

If you don't use Anaconda, you'll also need to download install the other packages separately using similar methods. Refer to the homepage of each package for instructions.

You'll want to run one time to get all of the NLTK packages, corpora, etc. (see below). Select the "all" option. Depending on your network speed, this could take a while, but you'll only need to do it once.

One of the examples will use NLTK's interface to the Stanford Named Entity Recognizer , which is distributed as a Java library. In particular, you'll want the following files handy in order to run this particular example:

The first thing we'll need to do is :

The first time you run anything using NLTK, you'll want to go ahead and download the additional resources that aren't distributed directly with the NLTK package. Upon running the command below, the the NLTK Downloader window will pop-up. In the Collections tab, select "all" and click on Download. As mentioned earlier, this may take several minutes depending on your network connection speed, but you'll only ever need to run it a single time.

Now the fun begins. We'll start with a pretty basic and commonly-faced task: extracting text content from an HTML page. Python's urllib package gives us the tools we need to fetch a web page from a given URL, but we see that the output is full of HTML markup that we don't want to deal with.

(N.B.: Throughout the examples in this presentation, we'll use Python (e.g., below) to only display a small portion of a string or list. Otherwise, if we displayed the entire item, sometimes it would take up the entire screen.)

Fortunately, NTLK provides a method called to get the raw text out of an HTML-formatted string. It's still not perfect, though, since the output will contain page navigation and all kinds of other junk that we don't want, especially if our goal is to focus on the body content from a news article, for example.

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We are now doing video interviews with some of our undergraduate students! Check out our first interview with Saloni Lad, former Director of Content, where she talks about her research experience.

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by discussionsjournal

The following is the record of an interview with 4th year CWRU student Kevin Kwock. He’s currently working on his B.S.E. Engineering Physics and music performance degree, B.A. Music – Piano and Harpsichord

1. Can you take me through what research you’ve done/ are currently working on?

I am currently working with Dr. Kash and Dr. Chottiner on a research project regarding crystal ordering in ternary semiconductors. Specifically, ZnSnSb2 has interesting crystal structure near its phase transition temperature;understanding the anion-cation ordering of semiconductors may provide new insights into other families of ternarysemiconductors in the same class. In addition to my university research, some of my previous research has been on studying self-assembling ionic liquids and how we can adapt these unique assembled structures to photonic applications.

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by discussionsjournal

The following is the record of an interview with 4th year CWRU student Tori Hamilton. She’s getting ready to graduate with her degree in Sociology:

Can you take me through what research you’ve done/ are currently working on?

Last semester I completed my capstone project titled “Restaurants, Re-Entry, and Recidivism: A Qualitative Analysis of Cleveland’s Re-Entry Programs,” which was a series of interviews I conducted with individuals working on re-entry programs as well as hiring formerly incarcerated people. In total, I interviewed two individuals working in Cleveland for culinary training programs that work with formerly/currently incarcerated individuals, and two employers in the area–one of which runs a restaurant. I looked to better understand the roles that these programs took on, and how they functioned within Cleveland’s community. Continue reading “Discussions with an UndergraduateResearcher”

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