Vasin Punyakanok

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We present a general framework for semantic role labeling. The framework combines a machine learning technique with an integer linear programming based inference procedure, which incorporates linguistic and structural constraints into a global decision process. Within this framework, we study the role of syntactic parsing information in semantic role(More)
We present a system for the semantic role labeling task. The system combines a machine learning technique with an inference procedure based on integer linear programming that supports the incorporation of linguistic and structural constraints into the decision process. The system is tested on the data provided in CoNLL2004 shared task on semantic role(More)
We present an approach to semantic role labeling (SRL) that takes the output of multiple argument classifiers and combines them into a coherent predicateargument output by solving an optimization problem. The optimization stage, which is solved via integer linear programming, takes into account both the recommendation of the classifiers and a set of problem(More)
We provide an experimental study of the role of syntactic parsing in semantic role labeling. Our conclusions demonstrate that syntactic parse information is clearly most relevant in the very first stage – the pruning stage. In addition, the quality of the pruning stage cannot be determined solely based on its recall and precision. Instead it depends on the(More)
We study learning structured output in a discriminative framework where values of the output variables are estimated by local classifiers. In this framework, complex dependencies among the output variables are captured by constraints and dictate which global labels can be inferred. We compare two strategies, learning independent classifiers and inference(More)
We study the problem of combining the outcomes of several different classifiers in a way that provides a coherent inference that satisfies some constraints. In particular, we develop two general approaches for an important subproblem identifying phrase structure. The first is a Markovian approach that extends standard HMMs to allow the use of a rich(More)
A SNoW based learning approach to shallow parsing tasks is presented and studied experimentally. The approach learns to identify syntactic patterns by combining simple predictors to produce a coherent inference. Two instantiations of this approach are studied and experimental results for Noun-Phrases (NP) and Subject-Verb (SV) phrases that compare favorably(More)
We us('. seven machine h;arning algorithms tbr one task: idenl;it~ying l)ase holm phrases. The results have 1)een t)rocessed by ditt'erent system combination methods and all of these (mtt)erformed the t)est individual result. We have apt)lied the seven learners with the best (:omt)inatot, a majori ty vote of the top tive systenls, to a s tandard (lata set(More)
Semantic entailment is the problem of determining if the meaning of a given sentence entails that of another. This is a fundamental problem in natural language understanding that provides a broad framework for studying language variability and has a large number of applications. We present a principled approach to this problem that builds on inducing(More)
This paper presents an approach to partial parsing of natural language sentences that makes global inference on top of the outcome of hierarchically learned local classifiers. The best decomposition of a sentence into clauses is chosen using a dynamic programming based scheme that takes into account previously identified partial solutions. This inference(More)