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Computer Science @ Rochester
Friday, July 02, 2010
2:00 PM
CSB 632
Jonathan Gordon and Naushad UzZaman
University of Rochester
Practice Talks
Jonathan Gordon's talk:

@ The AAAI 2010 Workshop on Collaboratively-built Knowledge Sources and Artificial Intelligence (WikiAI 2010).

Title: "Learning from the Web: Extracting General World Knowledge from Noisy Text".

Abstract: The quality and nature of knowledge that can be found by an automated knowledge-extraction system depends on its inputs. For systems that learn by reading text, the Web offers a breadth of topics and currency, but it also presents the problems of dealing with casual, unedited writing, non-textual inputs, and the mingling of languages. The results of knowledge extraction using the KNEXT system on two Web corpora -- Wikipedia and a collection of weblog entries -- indicate that, with automatic filtering of the output, even ungrammatical writing on arbitrary topics can yield an extensive knowledge base, which human judges find to be of good quality, with propositions receiving an average score across both corpora of 2.34 (where the range is 1 to 5 and lower is better) versus 3.00 for unfiltered output from the same sources.

Naushad UzZaman's talk:

@ The ACL 2010 Workshop on Semantic Evaluations (SemEval 2010).

Title: "TRIPS and TRIOS System for TempEval-2: Extracting Temporal Information from Text".

Abstract: Extracting temporal information from raw text is fundamental for deep language understanding, and key to many applications like question answering, information extraction, and document summarization. In this paper, we describe two systems we submitted to the TempEval 2 challenge, for extracting temporal information from raw text. The systems use a combination of deep semantic parsing, Markov Logic Networks and Conditional Random Field classifiers. Our two submitted systems, TRIPS and TRIOS, approached all tasks and outperformed all teams in two tasks. Furthermore, TRIOS mostly had second-best performances in other tasks. TRIOS also outperformed the other teams that attempted all the tasks. Our system is notable in that for tasks C – F, they operated on raw text while all other systems used tagged events and temporal expressions in the corpus as input.