commonvision:

Anyone can apply as long as you’re part of the UMBC community (students, staff, faculty, alumni, adjunct professors, commuters, residents, part time, full time, grad students, bio majors, math minors, etc.). 

commonvision:

Come and get your free buttons from the storefront! We’re making creative buttons all the time =)

suicabear:

Guess who is playing at Flat Tuesdays for free this Friday?!?!??!?!?!?!?!?

Us & Us Only

Yeah, that’s right.

Free show

Walking distance from your shitty dorm

Be there.

Also with;

Prison Music

The Better Don’ts

(non-UMBC friends, it’s free for you too)

Alan Kreizenbeck, chair of theater, has been selected by the Japan Studies Association to be a participant in the 2012 Freeman Summer Institute on Japan. The Institute will operate at Hawaii Tokai International College in Honolulu from Sunday, May 20 thru Friday, June 8, 2012.

The workshop aims to provide knowledge about Japan that faculty can use for curriculum development at their home institution. Kreizenbeck will be developing a course that focuses on various forms of Japanese theatre such as Noh, Kabuki, Kyogen and Bunraku.  Most expenses are covered by the Freeman Institute, and additional fees are covered by the Dean of the College of Arts, Humanities and Social Sciences and the Asian Studies Program.

Ph.D. Dissertation Defense

Mining Commonsense Knowledge from the Web:
Towards Inducing Script-like Structures From Large-scale Text Sources

Niels Kasch

10:00am Friday, March 9th, 2012, ITE 325B

Knowing the sequences of events in situations such as eating at a restaurant is an example of commonsense knowledge needed for a broad range of cognitive tasks (e.g., language understanding). This thesis outlines an approach to mine information about sequential, every day situations in a topic-driven fashion to produce declarative, script-like representations (c.f., Schank's scripts). Given a topic such as eating at a restaurant, we produce graphs of temporally ordered events involved with the activity referenced by the topic. Our work utilizes large-scale data sources (e.g., the Web) to avoid data sparseness issues of narrow corpora.

We describe steps that address the scale and noisiness of the Web to make it accessible for script extraction. Boilerplate elements (e.g., navigation bars and advertising) on web pages skew distributional statistics of words and obstruct information retrieval tasks. To make the web usable as a corpus, we introduce a machine learning technique to separate boilerplate elements from content in arbitrary web pages.

A key element for commonsense knowledge extraction is the generation of a topic-specific corpus that facilitates script extraction in a topic-driven manner. We introduce Concept Modeling for Scripts as an efficient method to induce concepts containing script elements (e.g., events, people, and objects) from topic-specific corpora. Our experiments and user studies conducted on the 2011 ICWSM Spinn3r dataset show that our method outperforms state of the art topic-modeling approaches such as Latent Dirichlet Allocation (LDA) on this task when applied to unbalanced (topic-specific) corpora.

Concept Modeling serves as a starting point for automated methods to discover events relevant to a script. We demonstrate event detection methods in topic-specific corpora based on (1) learned dependency paths indicative of individual event structures, (2) semantic cohesiveness of event pairs, and (3) surface structures indicative of golden sentences containing sequential information. Events extracted for a given topic can be arranged in a graph. The detection methods exploit graph analysis methods to identify strongly connected components to prune the event set such that related and central events are predominant in the structure. User studies demonstrate that (1) the Web is suitable for mining script-like knowledge and (2) the resulting graph structures portray events strongly related to a given topic.

Script-like structures, by definition, impose temporal ordering on the events contained within the structure. This work also presents a novel method to induce ordering information from topic-specific corpora based on a counting framework to judge the presence and strength of a temporal happens-before relation. The framework is extensible to several counting methods, where a counting method provides co-occurrence and ordering statistics. We present, among others, a novel naive counting methods that uses a simple sentence position assumption for temporal order. Comparisons to existing temporal resources show that our naive method, in conjunction with connected components analysis, induces temporal relationship with similar accuracy than more sophisticated methods, yet with a smaller computational footprint.

Committee

• Dr. Tim Oates (chair)
• Dr. Ronnie W. Smith
• Dr. Matt Schmill
• Dr. Tim Finin
• Dr. Charles Nicholas