Author name cluster

. Mausam

Possible papers associated with this exact author name in Arrow. This page groups case-insensitive exact name matches and is not a full identity disambiguation profile.

3 papers

1 author row

AAAI Conference 2016 Conference Paper

Numerical Relation Extraction with Minimal Supervision

Aman Madaan
Ashish Mittal
. Mausam
Ganesh Ramakrishnan
Sunita Sarawagi

We study a novel task of numerical relation extraction with the goal of extracting relations where one of the arguments is a number or a quantity (e. g. , atomic number(Aluminium, 13), inﬂation rate(India, 10. 9%)). This task presents peculiar challenges not found in standard Information Extraction (IE), such as the difﬁculty of matching numbers in distant supervision and the importance of units. We design two extraction systems that require minimal human supervision per relation: (1) NumberRule, a rule based extractor, and (2) NumberTron, a probabilistic graphical model. We ﬁnd that both systems dramatically outperform MultiR, a state-of-the-art non-numerical IE model, obtaining up to 25 points F-score improvement.

PDF Details

AAAI Conference 2012 Conference Paper

LRTDP Versus UCT for Online Probabilistic Planning

Andrey Kolobov
. Mausam
Daniel Weld

UCT, the premier method for solving games such as Go, is also becoming the dominant algorithm for probabilistic planning. Out of the five solvers at the International Probabilistic Planning Competition (IPPC) 2011, four were based on the UCT algorithm. However, while a UCT-based planner, PROST, won the contest, an LRTDP-based system, GLUTTON, came in a close second, outperforming other systems derived from UCT. These results raise a question: what are the strengths and weaknesses of LRTDP and UCT in practice? This paper starts answering this question by contrasting the two approaches in the context of finite-horizon MDPs. We demonstrate that in such scenarios, UCT’s lack of a sound termination condition is a serious practical disadvantage. In order to handle an MDP with a large finite horizon under a time constraint, UCT forces an expert to guess a non-myopic lookahead value for which it should be able to converge on the encountered states. Mistakes in setting this parameter can greatly hurt UCT’s performance. In contrast, LRTDP’s convergence criterion allows for an iterative deepening strategy. Using this strategy, LRTDP automatically finds the largest lookahead value feasible under the given time constraint. As a result, LRTDP has better performance and stronger theoretical properties. We present an online version of GLUTTON, named GOURMAND, that illustrates this analysis and outperforms PROST on the set of IPPC-2011 problems.

PDF Details

AAAI Conference 2011 Conference Paper

Artificial Intelligence for Artificial Artificial Intelligence

Peng Dai
. Mausam
Daniel Weld

Crowdsourcing platforms such as Amazon Mechanical Turk have become popular for a wide variety of human intelligence tasks; however, quality control continues to be a signiﬁcant challenge. Recently, we propose TURKONTROL, a theoretical model based on POMDPs to optimize iterative, crowdsourced workﬂows. However, they neither describe how to learn the model parameters, nor show its effectiveness in a real crowd-sourced setting. Learning is challenging due to the scale of the model and noisy data: there are hundreds of thousands of workers with high-variance abilities. This paper presents an end-to-end system that ﬁrst learns TURKONTROL’s POMDP parameters from real Mechanical Turk data, and then applies the model to dynamically optimize live tasks. We validate the model and use it to control a successive-improvement process on Mechanical Turk. By modeling worker accuracy and voting patterns, our system produces signiﬁcantly superior artifacts compared to those generated through nonadaptive workﬂows using the same amount of money.

PDF Details