Markov Logic
Implementing Weighted Abduction in Markov Logic
... We also simplify predications by removing unnecessary arguments. The most natural way to convert FrameNet frames to axioms is to treat a frame as a predicate whose arguments are the frame elements for all of its roles. ...
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Jointly Identifying Predicates, Arguments and Senses using Markov Logic
... To assess the performance of our model, and it to evaluate the possible gains to be made from consid- ering a joint model of the complete SRL pipeline, we set up several systems. The full system uses a Markov ...
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Knowledge Extraction and Joint Inference Using Tractable Markov Logic
... The development of knowledge base creation systems has mainly focused on information extraction without considering how to effec- tively reason over their databases of facts. One reason for this is that the inference ...
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Markov Logic Networks for Situated Incremental Natural Language Understanding
... As mentioned above, Markov Logic allows the spec- ification of knowledge bases through first order for- mulae. A straightforward representation of the game board would simply assert salient properties of ...
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Using Markov logic for automation of mobile network management
... Markov logic [22] is a language that combines first-order logic and Markov networks to define probability distributions in relational ...In Markov logic, unlike in first-order ...
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Markov Logic Network: Unify Framework for Ontology Learning
... The Markov Logic Networks[1] provide a strong probabilistic modeling framework based on First-Order Logic. The statistical relative learning combines the communicatory power of data representation ...
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Jointly Disambiguating and Clustering Concepts and Entities with Markov Logic
... knowledge about which mentions refer to the same concept can support disambiguation decisions. On the other hand, disambiguation influences clustering decisions. In contrast, local approaches which disambiguate mentions ...
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Integrating Logical Representations with Probabilistic Information using Markov Logic
... Distributional models for lexical meaning. Distributional models describe the meaning of a word through the context in which it appears (Landauer and Dumais, 1997; Lund and Burgess, 1996), where contexts can be ...
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A Markov Logic Approach to Bio Molecular Event Extraction
... introduce Markov Logic by considering the event extraction task (as relational structure over tokens as generated by algorithm ...In Markov Logic we can model this task by rst introducing a ...
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End to end Relation Extraction using Neural Networks and Markov Logic Networks
... End-to-end relation extraction refers to identifying boundaries of entity mentions, entity types of these mentions and appro- priate semantic relation for each pair of mentions. Traditionally, separate predic- tive ...
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Incorporating General Incident Knowledge into Automatic Incident Detection: A Markov Logic Network Method
... network, Markov Logic Network (MLN), was proposed in computer science, which can effectively overcome some limitations of BN and also bring the flexibility of applying various ...
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Markov Logic Networks for Text Mining: A Qualitative and Empirical Comparison with Integer Linear Programming
... and Markov Logic Networks (MLNs) have recently been successfully applied to many natural language processing (NLP) tasks, often outperforming their pipeline ...
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Joint Learning of Entity Linking Constraints Using a Markov-Logic Network
... the Markov Logic Network (MLN) (Richardson et ...order logic and Markov networks, to capture the bottom-up decisions derived from the process illustrated in Figure ...
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Jointly Extracting Japanese Predicate Argument Relation with Markov Logic
... our Markov Logic approach creates a joint model for the three cases and finds the most probable assignments taking into consideration the dependency between ...
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Joint Learning for Coreference Resolution with Markov Logic
... We evaluate Markov logic-based method on the dataset from CoNLL-2011 shared task. Our ex- periment results demonstrate the advantage of joint learning of pairwise classification and mention clus- tering ...
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Entity Disambiguation Using a Markov Logic Network
... grounding of each predicate appearing in . The value of the node is 1 if the ground predicate is true, and 0 otherwise. The probability distribution over possible worlds is given by (∑ ∑ ) where is the partition ...
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Jointly Modeling WSD and SRL with Markov Logic
... It is also easy to express the joint relation be- tween word sense disambiguation and semantic role labeling with Markov logic. What we need to do is just adding some global formulas. The relation between ...
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Joint Unsupervised Coreference Resolution with Markov Logic
... is Markov logic, a probabilistic extension of first-order logic (Richardson & Domingos, ...A Markov logic network (MLN) is a set of weighted first-order ...a Markov network ...
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Multilingual Semantic Role Labelling with Markov Logic
... With the ML predicates we specify a set of weighted first order formulae that define a distribu- tion over sets of ground atoms of these predicates (or so-called possible worlds). A set of weighted formu- lae is called a ...
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Exploring Markov Logic Networks for Question Answering
... Markov Logic Networks (MLNs) seem a natural model for expressing such knowl- edge, but the exact way of leveraging MLNs is by no means obvious. We in- vestigate three ways of applying MLNs to our task. ...
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