Archive for the ‘MALLET’ Category

Use Cases for Taming Text, 2nd ed.

Saturday, January 25th, 2014

Use Cases for Taming Text, 2nd ed. by Grant Ingersoll.

From the post:

Drew Farris, Tom Morton and I are currently working on the 2nd Edition of Taming Text (http://www.manning.com/ingersoll for first ed.) and are soliciting interested parties who would be willing to contribute to a chapter on practical use cases (i.e. you have something in production and are willing to write about it) for search with Solr, NLP using OpenNLP or Stanford NLP and machine learning using Mahout, OpenNLP or MALLET — ideally you are using combinations of 2 or more of these to solve your problems. We are especially interested in large scale use cases in eCommerce, Advertising, social media analytics, fraud, etc.

The writing process is fairly straightforward. A section roughly equates to somewhere between 3 – 10 pages, including diagrams/pictures. After writing, there will be some feedback from editors and us, but otherwise the process is fairly simple.

In order to participate, you must have permission from your company to write on the topic. You would not need to divulge any proprietary information, but we would want enough information for our readers to gain a high-level understanding of your use case. In exchange for your participation, you will have your name and company published on that section of the book as well as in the acknowledgments section. If you have a copy of Lucene in Action or Mahout In Action, it would be similar to the use case sections in those books.

Cool!

I am guessing the second edition isn’t going to take as long as the first. 😉

Couldn’t be in better company as far as co-authors.

See the post for the contact details.

Lincoln Logarithms: Finding Meaning in Sermons

Thursday, February 28th, 2013

Lincoln Logarithms: Finding Meaning in Sermons

From the webpage:

Just after his death, Abraham Lincoln was hailed as a luminary, martyr, and divine messenger. We wondered if using digital tools to analyze a digitized collection of elegiac sermons might uncover patterns or new insights about his memorialization.

We explored the power and possibility of four digital tools—MALLET, Voyant, Paper Machines, and Viewshare. MALLET, Paper Machines, and Voyant all examine text. They show how words are arranged in texts, their frequency, and their proximity. Voyant and Paper Machines also allow users to make visualizations of word patterns. Viewshare allows users to create timelines, maps, and charts of bodies of material. In this project, we wanted to experiment with understanding what these tools, which are in part created to reveal, could and could not show us in a small, but rich corpus. What we have produced is an exploration of the possibilities and the constraints of these tools as applied to this collection.

The resulting digital collection: The Martyred President: Sermons Given on the Assassination of President Lincoln.

Let’s say this is not an “ahistorical” view. 😉

Good example of exploring “unstructured” data.

A first step before authoring a topic map.

Topic Discovery With Apache Pig and Mallet

Friday, February 1st, 2013

Topic Discovery With Apache Pig and Mallet

Only one of two posts from this blog in 2012 but it is a useful one.

From the post:

A common desire when working with natural language is topic discovery. That is, given a set of documents (eg. tweets, blog posts, emails) you would like to discover the topics inherent in those documents. Often this method is used to summarize a large corpus of text so it can be quickly understood what that text is ‘about’. You can go further and use topic discovery as a way to classify new documents or to group and organize the documents you’ve done topic discovery on.

Walks through the use of Pig and Mallet on a newsgroup data set.

I have been thinking about getting one of those unlimited download newsgroup accounts.

Maybe I need to go ahead and start building some newsgroup data sets.

Getting Started with MALLET and Topic Modeling

Thursday, September 1st, 2011

Getting Started with MALLET and Topic Modeling

If you don’t remember MALLET, take a look at: MALLET: MAchine Learning for LanguagE Toolkit Topic Map Competition (TMC) Contender?

Shawn is very interested in applying topic modeling to a variety of historical texts.

His blog, Electric Archaeology: Digital Media for Learning and Research looks very interesting. Covers: “Agent based modeling, games, virtual worlds, and online education for archaeology and history.”

This is the sort of person who might be interested in topic maps and related technologies.

As far as I know, there is still a real lack of example driven texts that would introduce most humanists to modern software.

PyBrain: The Python Machine Learning Library

Thursday, February 3rd, 2011

PyBrain: The Python Machine Learning Library

From the website:

PyBrain is a modular Machine Learning Library for Python. Its goal is to offer flexible, easy-to-use yet still powerful algorithms for Machine Learning Tasks and a variety of predefined environments to test and compare your algorithms.

PyBrain is short for Python-Based Reinforcement Learning, Artificial Intelligence and Neural Network Library. In fact, we came up with the name first and later reverse-engineered this quite descriptive “Backronym”.

How is PyBrain different?

While there are a few machine learning libraries out there, PyBrain aims to be a very easy-to-use modular library that can be used by entry-level students but still offers the flexibility and algorithms for state-of-the-art research. We are constantly working on more and faster algorithms, developing new environments and improving usability.

What PyBrain can do

PyBrain, as its written-out name already suggests, contains algorithms for neural networks, for reinforcement learning (and the combination of the two), for unsupervised learning, and evolution. Since most of the current problems deal with continuous state and action spaces, function approximators (like neural networks) must be used to cope with the large dimensionality. Our library is built around neural networks in the kernel and all of the training methods accept a neural network as the to-be-trained instance. This makes PyBrain a powerful tool for real-life tasks.

Another tool kit to assist in the construction of topic maps.

And another likely contender for the Topic Map Competition!