NLP and Global Warming

Those of us who were at EMNLP-CONLL 2007 remember the "NLP and Global Warming" exchange between James Clarke, Jason Eisner, and Dan Bikel at the Q/A session of the Clarke and Lapata paper. The transcript of this funny conversation is now online, thanks to Jason.

I really liked Hal's ending remark.

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.

Digital Reasoning awarded contextual similarity patent?

I was lead to this article on Forbes via Damien's post. The article is about a company Digital Reasoning getting patent on what sounded to me as contextual similarity. Their "white paper" makes reference to a patent number 7249117 (via USPTO). Unlike research papers, reading the patent document was so difficult. Will get to it sometime later but here is an extract from their press release about what their technology can do.

* Learn the meanings of words, classes of words, and other symbols based on how they are used in context in natural language
* Create and manipulate models of this "meaning" - i.e. the mathematical patterns of usage - including the detection of groups or similar categories of words or development of hierarchies or creation of relationships between words
* Improve the models based on human feedback or using other structured information after model construction
* The representation or sharing of this model or learning in an ontology, graph structure, or programming languages

Anyone from the ACL/ML/AI community can immediately recognize this and start citing their favorite papers on these topics starting from at least a decade ago. A promotional video from the company on YouTube can be found here. Excerpt from the video: "... We treat the text representation of human language as a signal ... ".

I think everyone should stop taking patents seriously. Wishful thinking?

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.

Google allows data binging for researchers

Google now opened access to university researchers to its search and MT systems in today's announcement on their research blog. The search API documentation does not mention any restriction on the number of queries that can be posted for search (The earlier limit was 1000). Whatever the number is I am guessing it will be large (Drinking from the firehose?). However, the MT API allows 1000 queries per day with the documentation hinting that this need not be a hard limit.

Looking at the search API output, two things I really miss is the number of hits and the snippet for each search result. The number of hits has been used in several papers for interesting results. The other useful feature is snippets. Every search result from Google is accompanied by a small snippet extracted from the page, as shown below for an example query "Dekang Lin".

The information in the snippets can be used as informative features in different tasks like this one in person name disambiguation. (BTW, Dekang is now at Google)

Despite these minor quibbles, these new APIs will be quite useful to all of us and will certainly result in more papers on Googleology.

Later addition: Turns out we can sort of get the counts by simply counting the number of search results by repeatedly executing the request (only ten results per request) but the API caps this limit to 100. That means you could get a maximum of 1000 results. Which is not quite the same as "Results 1 - 10 of about 779,000,000". Though that number is approximate, it is still indicative of how strong the query is w.r.t the web. For example GoogleCount("Horse+animal") >> GoogleCount("Horse+truck").

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

Readings from AAAI 2007

AAAI 2007 is now going on at Vancouver. Here is my selection of NLP and Learning papers I would like to know more about.

Deriving a Large-Scale Taxonomy from Wikipedia, Simone Paolo Ponzetto, Michael Strube

Relation Extraction from Wikipedia Using Subtree Mining, Dat P. T. Nguyen, Yutaka Matsuo, Mitsuru Ishizuka

Finding Related Pages Using Green Measures: An Illustration with Wikipedia, Yann Ollivier, Pierre Senellart

Graph Partitioning Based on Link Distributions, Bo Long, Mark (Zhongfei) Zhang, Philip S. Yu

Semi-supervised Learning by Mixed Label Propagation, Wei Tong, Rong Jin

Semi-Supervised Learning with Very Few Labeled Training Examples, Zhi-Hua Zhou, De-Chuan Zhan, Qiang Yang

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

Isometric Projection, Deng Cai, Xiaofei He, Jiawei Han

Improving Similarity Measures for Short Segments of Text, Wen-tau Yih, Christopher Meek

Topic Segmentation Algorithms for Text Summarization and Passage Retrieval: An Exhaustive Evaluation, Gaël Dias, Elsa Alves. José Gabriel Pereira Lopes

Robust Estimation of Google Counts for Social Network Extraction, Yutaka Matsuo, Hironori Tomobe, Takuichi Nishimura

Harvesting Relations from the Web - Quantifiying the Impact of Filtering Functions, Sebastian Blohm, Philipp Cimiano, Egon Stemle

Template-Independent News Extraction Based on Visual Consistency, Shuyi Zheng, Ruihua Song, Ji-Rong Wen

Comprehending and Generating Apt Metaphors: A Web-driven, Case-based Approach to Figurative Language, Tony Veale, Yanfen Hao

Mobile Service for Reputation Extraction from Weblogs - Public Experiment and Evaluation, Takahiro Kawamura, Shinichi Nagano, Masumi Inaba, Yumiko Mizoguchi

The Impact of Time on the Accuracy of Sentiment Classifiers Created from a Web Log Corpus, Kathleen T. Durant, Michael D. Smith

Nectar: Learning by Combining Observations and User Edits, Vittorio Castelli, Lawrence Bergman, Daniel Oblinger

Multi-Label Learning by Instance Differentiation, Min-Ling Zhang, Zhi-Hua Zhou

Extracting Influential Nodes for Information Diffusion on a Social Network, Masahiro Kimura, Kazumi Saito, Ryohei Nakano

Temporal and Information Flow Based Event Detection from Social Text Streams, Qiankun Zhao, Prasenjit Mitra, Bi Chen

Analyzing Reading Behavior by Blog Mining, Tadanobu Furukawa, Mitsuru Ishizuka, Yutaka Matsuo, Ikki Ohmukai, Koki Uchiyama