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Deriving a Large Scale Taxonomy from Wikipedia
 In Proceedings of AAAI
, 2007
"... We take the category system in Wikipedia as a conceptual network. We label the semantic relations between categories using methods based on connectivity in the network and lexicosyntactic matching. As a result we are able to derive a large scale taxonomy containing a large amount of subsumption, i.e ..."
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Cited by 98 (5 self)
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We take the category system in Wikipedia as a conceptual network. We label the semantic relations between categories using methods based on connectivity in the network and lexicosyntactic matching. As a result we are able to derive a large scale taxonomy containing a large amount of subsumption, i.e. isa, relations. We evaluate the quality of the created resource by comparing it with ResearchCyc, one of the largest manually annotated ontologies, as well as computing semantic similarity between words in benchmarking datasets.
Markov logic in infinite domains
 In Proc. UAI07
, 2007
"... Combining firstorder logic and probability has long been a goal of AI. Markov logic (Richardson & Domingos, 2006) accomplishes this by attaching weights to firstorder formulas and viewing them as templates for features of Markov networks. Unfortunately, it does not have the full power of first ..."
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Combining firstorder logic and probability has long been a goal of AI. Markov logic (Richardson & Domingos, 2006) accomplishes this by attaching weights to firstorder formulas and viewing them as templates for features of Markov networks. Unfortunately, it does not have the full power of firstorder logic, because it is only defined for finite domains. This paper extends Markov logic to infinite domains, by casting it in the framework of Gibbs measures (Georgii, 1988). We show that a Markov logic network (MLN) admits a Gibbs measure as long as each ground atom has a finite number of neighbors. Many interesting cases fall in this category. We also show that an MLN admits a unique measure if the weights of its nonunit clauses are small enough. We then examine the structure of the set of consistent measures in the nonunique case. Many important phenomena, including systems with phase transitions, are represented by MLNs with nonunique measures. We relate the problem of satisfiability in firstorder logic to the properties of MLN measures, and discuss how Markov logic relates to previous infinite models. 1
Structured machine learning: the next ten years
, 2008
"... The field of inductive logic programming (ILP) has made steady progress, since the first ILP workshop in 1991, based on a balance of developments in theory, implementations and applications. More recently there has been an increased emphasis on Probabilistic ILP and the related fields of Statistic ..."
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Cited by 21 (2 self)
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The field of inductive logic programming (ILP) has made steady progress, since the first ILP workshop in 1991, based on a balance of developments in theory, implementations and applications. More recently there has been an increased emphasis on Probabilistic ILP and the related fields of Statistical Relational Learning (SRL) and Structured Prediction. The goal of the current paper is to consider these emerging trends and chart out the strategic directions and open problems for the broader area of structured machine learning for the next 10 years.
ILP turns 20  Biography and future challenges
 MACH LEARN
, 2011
"... Inductive Logic Programming (ILP) is an area of Machine Learning which has now reached its twentieth year. Using the analogy of a human biography this paper recalls the development of the subject from its infancy through childhood and teenage years. We show how in each phase ILP has been characteri ..."
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Cited by 13 (9 self)
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Inductive Logic Programming (ILP) is an area of Machine Learning which has now reached its twentieth year. Using the analogy of a human biography this paper recalls the development of the subject from its infancy through childhood and teenage years. We show how in each phase ILP has been characterised by an attempt to extend theory and implementations in tandem with the development of novel and challenging realworld applications. Lastly, by projection we suggest directions for research which will help the subject coming of age.
Markov Logic Networks for Situated Incremental Natural Language Understanding
 In Proceedings of SIGdial 2012, Seoul, Korea. Association for Computational Linguistics
, 2012
"... We present work on understanding natural language in a situated domain, that is, language that possibly refers to visually present entities, in an incremental, wordbyword fashion. Such type of understanding is required in conversational systems that need to act immediately on language input, such ..."
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We present work on understanding natural language in a situated domain, that is, language that possibly refers to visually present entities, in an incremental, wordbyword fashion. Such type of understanding is required in conversational systems that need to act immediately on language input, such as multimodal systems or dialogue systems for robots. We explore a set of models specified as Markov Logic Networks, and show that a model that has access to information about the visual context of an utterance, its discourse context, as well as the linguistic structure of the utterance performs best. We explore its incremental properties, and also its use in a joint parsing and understanding module. We conclude that MLNs offer a promising framework for specifying such models in a general, possibly domainindependent way. 1
Accelerated Adaptive Markov Chain for Partition Function Computation
"... We propose a novel Adaptive Markov Chain Monte Carlo algorithm to compute the partition function. In particular, we show how to accelerate a flat histogram sampling technique by significantly reducing the number of “null moves ” in the chain, while maintaining asymptotic convergence properties. Our ..."
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We propose a novel Adaptive Markov Chain Monte Carlo algorithm to compute the partition function. In particular, we show how to accelerate a flat histogram sampling technique by significantly reducing the number of “null moves ” in the chain, while maintaining asymptotic convergence properties. Our experiments show that our method converges quickly to highly accurate solutions on a range of benchmark instances, outperforming other stateoftheart methods such as IJGP, TRW, and Gibbs sampling both in runtime and accuracy. We also show how obtaining a socalled density of states distribution allows for efficient weight learning in Markov Logic theories.
Creating a Knowledge Base From a Collaboratively Generated Encyclopedia
 Proc. of HLTNAACL '07
"... We present our work on using Wikipedia as a knowledge source for Natural Language Processing. We first describe our previous work on computing semantic relatedness from Wikipedia, and its application to a machine learning based coreference resolution system. Our results suggest that Wikipedia repres ..."
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We present our work on using Wikipedia as a knowledge source for Natural Language Processing. We first describe our previous work on computing semantic relatedness from Wikipedia, and its application to a machine learning based coreference resolution system. Our results suggest that Wikipedia represents a semantic resource to be treasured for NLP applications, and accordingly present the work directions to be explored in the future. 1
Reinforcement learning with markov logic networks
 In Proceedigns of European Workshop on Reinforcement Learning
, 2008
"... Abstract. In this paper, we propose a method to combine reinforcement learning (RL) and Markov logic networks (MLN). RL usually does not consider the inherent relations or logical connections of the features. Markov logic networks combines firstorder logic and graphical model and it can represent a ..."
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Abstract. In this paper, we propose a method to combine reinforcement learning (RL) and Markov logic networks (MLN). RL usually does not consider the inherent relations or logical connections of the features. Markov logic networks combines firstorder logic and graphical model and it can represent a wide variety of knowledge compactly and abstractly. We propose a new method, reinforcement learning algorithm with Markov logic networks (RLMLN), to deal with many difficult problems in RL which have much prior knowledge to employ and need some relational representation of states. With RLMLN, prior knowledge can be easily introduced to the learning systems and the learning process will become more efficient. Experiments on blocks world illustrate that RLMLN is a promising method. 1
Incremental Construction of Robust but Deep Semantic Representations for Use in Responsive Dialogue Systems
"... It is widely acknowledged that current dialogue systems are held back by a lack of flexibility, both in their turntaking model (typically, allowing only a strict backandforth between user and system) and in their interpretation capabilities (typically, restricted to slot filling). We have develop ..."
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It is widely acknowledged that current dialogue systems are held back by a lack of flexibility, both in their turntaking model (typically, allowing only a strict backandforth between user and system) and in their interpretation capabilities (typically, restricted to slot filling). We have developed a component for NLU that attempts to address both of these challenges, by a) constructing robust but deep meaning representations that support a range of further user intention determination techniques from inference / reasoningbased ones to ones based on more basic structures, and b) constructing these representations incrementally and hence providing semantic information on which system reactions can be based concurrently to the ongoing user utterance. The approach is based on an existing semantic representation formalism, Robust Minimal Recursion Semantics, which we have modified to suit incremental construction. We present the modifications, our implementation, and discuss applications within a dialogue system context, showing that the approach indeed promises to meet the requirements for more flexibility.