Powerset Natural Language Search

Powerset, a company we only remember seeing as conference sponsors, now actually has something working. After receiving an email from them, I tried out several queries. At best, it seems to answer most Wh-questions and certain whole-part relations.

Try out the same query on Google.

People Search on the Web

Wired has an article about spock.com, a people search engine that combines crawled and user added content. From the few searches I did, looks like this is good for celebrity names than a regular person with web content. For instance, searching a name like "David Smith" produces these results. Of the top 10 results, only 3 of them actually have the name "David Smith" or something closer and the first result is not one of them. Compare this with a general purpose search engine like Google. Among a dozen random NLP/ML academic names (professors) I tried, it only got Jason Eisner and Tom Mitchell correct. One reason for this poor recall is probably they don't get content from user home pages.
(Some sites where this data is derived from include MySpace, Friendster, IMDB, Wikipedia, ratemyprofessors.com, etc.)

Nevertheless, this website is a representative of interesting KDD-style problems that one could do with people names. It is also interesting as people names that we look for fall in the "long tail" without sufficient data to support calling for clever machine learning techniques.

Recommending scientific papers

I noticed a new feature in Citeseer which tries suggest an "alternate document" for a paper.

Clearly it does not do what it implies to do and it doesn't show up for all papers. (Experimental?) So, an interesting question is how does one recommend scientific papers? Something more than mere document similarity is required. If I am reading a CRF paper then there is no point in listing all papers containing similar words. Just listing nodes connected to inward and outward links of the paper in the citation graph wont suffice either. Ideal recommendations for a paper would depend on the role the user is playing. When I am reading a paper about some new topic, I would like to get pointed to original papers on the topic, some recent papers on the topic, and may be some survey papers or books. On the other hand when I am writing a paper, I would like to be pointed to all papers related to the topic (recall important than precision here to avoid reviewer comments on "missing reference") in some magical order that puts papers more relevant to your work above. Also these papers might not be related in directly through citations. If there is a recent related work in the Annals of Statistics, for instance, then it should show up when I am working on, say, approximate inference methods for graphical models. (Possible to deduce this from my previous queries?)

In spite of more information being present in a scientific paper than its text, recommending or ranking papers appears to be quite challenging.

Readings from SIGIR 2007

SIGIR 2007 is happening now at Amsterdam!

Latent Concept Expansion Using Markov Random Fields, Donald Metzler, Bruce Croft

Random Walks on the Click Graph, Nick Craswell, Martin Szummer

Towards Automatic Extraction of Event and Place Semantics from Flickr Tags, Tye Rattenbury, Nathaniel Good, Mor Naaman

Clustering of Documents with Local and Global Regularization, Fei Wang, Changshui Zhang, Tao Li

Detecting, Categorizing and Clustering Entity Mentions in Chinese Text, Wenjie Li, Donglei Qian, Chunfa Yuan, Qin Lu

Principles of Hash-based Text Retrieval, Benno Stein

DiffusionRank: A Possible Penicillin for Web Spamming, Haixuan Yang, Irwin King, Michael R. Lyu

Context Sensitive Stemming for Web Search, Fuchun Peng, Nawaaz Ahmed, Xin Li, Yumao Lu

Combining Content and Link for Classification using Matrix Factorization, Shenghuo Zhu, Kai Yu, Yun Chi, Yihong Gong

ARSA: A Sentiment-Aware Model for Predicting Sales Performance Using Blogs, Yang Liu, Jimmy Huang, Aijun An, Xiaohui Yu

Heavy-Tailed Distributions and Multi-Keyword Queries, Arnd Konig, Surajit Chaudhuri, Liying Sui, Kenneth Church

Book Review: Geometry and Meaning

Title: Geometry and Meaning
Author: Dominic Widdows
URL : http://infomap.stanford.edu/book/


This is an excellent book for even high school students to learn about IR. However, if you have read a few papers in this field then reading this book is a waste of time, except for the jewels in boxes throughout the book.

New IR book

Just discovered this book by Chris Manning, Prabhakar Raghavan, and Hinrich Schütze.
This is going to be in my reading list for the IR Course, next spring.

Adversarial Information Retrieval 2007

Contents:

1. AIRWeb'07 Topics
2. Web Spam Challenge
3. Timeline
4. Organizers and Program Committee

1. AIRWEB'07 TOPICS

Adversarial Information Retrieval addresses tasks such as gathering,
indexing, filtering, retrieving and ranking information from collections
wherein a subset has been manipulated maliciously. On the Web, the
predominant form of such manipulation is "search engine spamming" or
spamdexing, i.e., malicious attempts to influence the outcome of ranking
algorithms, aimed at getting an undeserved high ranking for some items
in the collection.

We solicit both full and short papers on any aspect of adversarial
information retrieval on the Web. Particular areas of interest include,
but are not limited to:

* Link spam
* Content spam
* Cloaking
* Comment spam
* Spam-oriented blogging
* Click fraud detection
* Reverse engineering of ranking algorithms
* Web content filtering
* Advertisement blocking
* Stealth crawling
* Malicious tagging

Proceedings of the workshop will be included in the ACM Digital Library.
Full papers are limited to 8 pages; work-in progress will be permitted 4
pages.

For more information, see

2. WEB SPAM CHALLENGE

This year, we are introducing a novel element: a Web Spam Challenge for
testing web spam detection systems. We will be using the WEBSPAM-UK2006
collection for Web Spam Detection .

The collection includes large set of web pages, a web graph, and
human-provided labels for a set of hosts. We will also provide a set of
features extracted from the contents and links in the collection, which
may be used by the participant teams in addition to any automatic
technique they choose to use.

We ask that participants of the Web Spam Challenge submit predictions
(normal/spam) for all unlabeled hosts in the collection. Predictions
will be evaluated and results will be announced at the AIRWeb 2007
workshop.

For more information, see

3. TIMELINE

- 7 February 2007: E-mail intention to submit a workshop paper
(optional, but helpful)
- 15 February 2007: Deadline for workshop paper submissions
- 15 March 2007: Notification of acceptance of workshop papers
- 30 March 2007: Camera-ready copy due
- 30 March 2007: Challenge submissions due
- 8 May 2007: Date of workshop

...

The Dragon Tooolkit

The Dragon Tooolkit is Java-based development package for academic research use in language modeling (LM) and information retrieval (IR). Language modeling has recently emerged as an attractive new framework for text information retrieval and text mining (TM). However, most Java-based free search engines such as Lucene does not support LM very well. The Lemur toolkit is designed for LM and IR, but written in C and C++, which may be a hindrance to people who prefer Java programming. Basically, the dragon toolkit is tailored for researchers who work on large-scale LM and IR and prefer Java programming. Moreover, different from Lucene and Lemur, it provides built-in supports for semantic-based IR and TM. The dragon tookit seamlessly intergrates and implements a set of NLP tools, which enable the toolkit to index text collections with various representation schemes including words, phrases, ontology-based concepts and relationships. However, to minimize the learning time, we intentionally keep the package small and simple. The toolkit does not have some features including distributed IR and cross-language IR which are part of Lemur toolkit.

How to Cite Dragon Toolkit

If you are using the Dragon Toolkit for research work, please cite it in your published papers:

Zhou, X., Zhang, X., and Hu, X., The Dragon Toolkit, Data Mining & Bioinformatics Lab, iSchool at Drexel University, http://www.ischool.drexel.edu/dmbio/dragontool

Download Dragon Toolkit

Get the Dragon Toolkit source code and binary libraries (including external libraries) and necessary supporting data. Click http://www.ischool.drexel.edu/dmbio/dragontool/default.asp to download.