Writing style

The sweetest thing ever written in a paper: "The reader who is unfamiliar with this field or who has allowed his or her facility with some of its concepts to fall into disrepair may profit from a brief perusal of Feller (1950) and Gallagher (1968)."

- Brown et. al., "Class based n-gram Models of Natural Language.", Computational Linguistics, 1990

Searching ACL anthology

If you are looking up the ACL anthology regularly, my friend Markus has a nice firefox search plugin to do that. You can get that and others from this page.

ACL accepted papers

Hal posted a while back about the ACL accepted papers that I just read now -- I've been living under a rock for some time. You can get a printer friendly version here. I know, my paper did not make it to that list :(

New additions to my reading list:

Distributional Identification of Non-Referential Pronouns
Shane Bergsma, Dekang Lin and Randy Goebel

An Unsupervised Approach to Biography Production using Wikipedia
Fadi Biadsy, Julia Hirschberg and Elena Filatova

Resolving Personal Names in Email Using Context Expansion
Tamer Elsayed, Douglas Oard and Galileo Namata

Mining Wiki Resources for Multilingual Named Entity Recognition
Alexander Richman and Patrick Schone

Inducing Gazetteers for Named Entity Recognition by Large-scale Clustering of Dependency Relations
Jun'ichi Kazama and Kentaro Torisawa

Name Translation in Statistical Machine Translation - Learning When to Transliterate
Ulf Hermjakob, Kevin Knight and Hal Daume



The Tradeoffs Between Open and Traditional Relation Extraction
Michele Banko and Oren Etzioni

(Longest paper title)
Unsupervised Discovery of Generic Relationships Using Pattern Clusters and its Evaluation by Automatically Generated SAT Analogy Questions
Dmitry Davidov and Ari Rappoport

Finding Contradictions in Text
Marie-Catherine de Marneffe, Anna Rafferty and Christopher Manning

Extracting Question-Context-Answer Triples from Online Forums
Shilin Ding, Gao Cong, Chin-Yew Lin and Xiaoyan Zhu

EM Can Find Pretty Good HMM POS-Taggers (When Given a Good Start)
Yoav Goldberg, Meni Adler and Michael Elhadad

Extraction of Entailed Semantic Relations Through Syntax-based Comma Resolution
Vivek Srikumar, Roi Reichart, Mark Sammons, Ari Rappoport and Dan Roth

Learning Bigrams from Unigrams
Xiaojin Zhu, Andrew Goldberg, Michael Rabbat and Robert Nowak

Evaluating Roget's Thesauri
Alistair Kennedy and Stan Szpakowicz

Randomized Language Models via Perfect Hash Functions
David Talbot and Thorsten Brants

Solving Relational Similarity Problems Using the Web as a Corpus
Preslav Nakov and Marti Hearst

What do you do?

As a grad student working on NLP how do you explain what you are working on, to friends and family? I inevitably end up referring to the Google search engine even though what I do is quite far from IR. Actually, thats not true. These days IR seems to consume everything but thats another story.

This reminds me of a funny conversation at CLSP recently:

Sanjeev is telling us about an incident where a concerned parent of a young child with a speaking disability is asking him for his opinion. Apparently, she is confused about "Language and Speech Processing" in CLSP.

Keith butts in: "Run a few more iterations of EM and he'll be fine."

A song on parsing

We all know Jason's love for parsing from his work but it takes a different level of dedication to write a Valentine's Day song about parsing.

As Jason says, "Parsers just want to be appreciated, like everyone else."

Funny bone

The frequentist exclaimed, "All your Bayes are belong to us!" to which the Bayesian responded, "Well, it depends."

NIPS papers are out

For a full list see here. Some papers I want to read based on my current interests:

Random Projections for Manifold Learning
Chinmay Hegde, Michael Wakin, Richard Baraniuk

The Distribution Family of Similarity Distances
Gertjan Burghouts, Arnold Smeulders, Jan-Mark Geusebroek

Manifold Sculpting
Michael Gashler, Dan Ventura, Tony Martinez

A learning framework for nearest neighbor search
Lawrence Cayton, Sanjoy Dasgupta

Learning Bounds for Domain Adaptation
John Blitzer, Koby Crammer, Alex Kulesza, Fernando Pereira, Jennifer Wortman

Convex Relaxations of EM
Yuhong Guo, Dale Schuurmans

A Randomized Algorithm for Large Scale Support Vector Learning
Krishnan Kumar, Chiru Bhattacharya, Ramesh Hariharan

Bundle Methods for Machine Learning
Alex Smola, S V N Vishwanathan, Quoc Le

Regularized Boost for Semi-Supervised Learning
Ke Chen, Shihai Wang

Learning the structure of manifolds using random projections
Yoav Freund, Sanjoy Dasgupta, Mayank Kabra, Nakul Verma

A complexity measure for intuitive theories
Charles Kemp, Noah Goodman, Joshua Tenenbaum