Lists of English Words

When I was a kid, I went through an 80s music phase…well, some things never change. “People just love to play with words…” Know that song? Anyway…

One of the biggest pains of text mining and NLP is colloquialism — language that is only appropriate in casual language and not in formal speech or writing. Words such as informal contractions (“gonna”, “wanna”, “whatcha”, “ain’t”, “y’all”) are colloquialisms and are everywhere on the Web. There is also a great deal of slang common on the Web including acronyms/emoticons (“LOL”, “WTF”) and smilies that add sentiment to text. There is also a less used slang called leetspeak that replaces letters with numbers (“n00b” rather than “noob”, “pwned” instead of “owned” and “pr0n” instead of “porn”).

There are also regionalisms which are a pain for semantic analysis but not so much for probabilistic analysis. Some examples are pancakes (“flapjacks”, “griddlecakes”) or carbonated beverages (“soda”, “pop”, “Coke”). Or, little did I know, “maple bars” vs. “Long Johns”. Now I am hungry. There are also words that have a formal and informal meeting such as “kid” (a young goat, or a child…same thing).

Source: http://popvssoda.com/

Linguists consider colloquialisms different than slang. Slang is informal language used by a specific [...]

Opening Statements on Markov Chain Monte Carlo

This quarter I am TAing UCLA’s Statistics 102C. Introduction to Monte Carlo Methods for Professor Qing Zhou. This course did not exist when I was an undergraduate, and I think it is pretty rare to teach Monte Carlo (minus the bootstrap if you count that) or MCMC to undergrads. I am excited about this class because to me, MCMC turns Statistics on its head. It felt like a totally different paradigm compared to the regression and data analysis paradigm that I was used to at the time. It also exposes students to the connection between Statistics/MCMC and other fields such as Computer Science, Genetics/Biology, etc.

I usually do not have much to talk about during week 1, especially if my class is the second day of the quarter. Today was an exception because I wanted to excite the class about this topic.

Some examples I discussed:

the general recipe for Monte Carlo methods
the bootstrap as an example of resampling, and R loops
computing and mention of Buffon’s Needle
scheduling/timetabling and occupancy/matching problems using stochastic search (simulated annealing, Tabu search etc.)
mention of genetic algorithms and swarm intelligence
PageRank as a Markov process
drawing a random sample of web pages using Random Walk Metropolis-Hastings
short inventory of fields [...]

My Experience at ACM Data Mining Camp #DMcamp

My parents and I made plans to visit San Jose and Saratoga on my grandmother’s birthday, March 19, since that is where she grew up. I randomly saw someone tweet about the ACM Data Mining Camp unconference that happened to be the next day, March 20, only a couple of miles from our hotel in Santa Clara. This was an opportunity I could not pass up.

Upon arriving at eBay/PayPal’s “Town Hall” building, I was greeted by some very hyper people! Surrounding me were a lot of people my age and my interest. I finally felt like I was in my element. The organizers of the event also had a predetermined Twitter hashtag for the event #DMCAMP, and also set up a blog where people could add material and write comments about the sessions. I felt like a kid in a candy shop when I saw the proposed sessions for the breakout sessions.

Some of the proposed topics I found really interesting:

Anonamly Detection
Natural Language Processing
Collaborative Filtering and a Netflix Paper
CPC Optimization for Events
Data Mining Programming Tools
Structured Tags
Status of Mahout
Machine Learning with Parallel Processors
Sentiment Analysis
Parallel R

About half of these actually made it onto the schedule. Unfortunately, I was only able to attend 4 [...]