Марковские логические сети

Chinese named entity recognition (NER) has re- cently been viewed as a classification or sequence labeling problem, and many approaches have been proposed. However, they tend to address this problem …...

We propose a new integrated approach based on Markov logic networks (MLNs), an effective com- bination of probabilistic graphical models and first- order logic for statistical relational learning, to ex- …...

Extracting temporal information from raw text is fundamental for deep language understand- ing, and key to many applications like ques- tion answering, information extraction, and document summarization. In this paper, …...

We present the first work on antecedent se- lection for bridging resolution without restric- tions on anaphor or relation types. Our model integrates global constraints on top of a rich …...

We combine logical and distributional rep- resentations of natural language meaning by transforming distributional similarity judg- ments into weighted inference rules using Markov Logic Networks (MLNs). We show that this …...

This paper takes a discourse-oriented per- spective for disambiguating common and proper noun mentions with respect to Wikipedia. Our novel approach mod- els the relationship between disambigua- tion and aspects …...

Probabilistic Soft Logic (PSL) is a re- cently developed framework for proba- bilistic logic. We use PSL to combine logical and distributional representations of natural-language meaning, where distri- butional information …...

Several state-of-the-art event extraction sys- tems employ models based on Support Vec- tor Machines (SVMs) in a pipeline architec- ture, which fails to exploit the joint depen- dencies that typically …...

We represent natural language semantics by combining logical and distributional in- formation in probabilistic logic. We use Markov Logic Networks (MLN) for the RTE task, and Probabilistic Soft Logic (PSL) …...

Elementary-level science exams pose sig- nificant knowledge acquisition and rea- soning challenges for automatic question answering. We develop a system that rea- sons with knowledge derived from text- books, represented …...

AbstractEvent coreference resolution is a challenging problem since it relies on several components of the information extraction pipeline that typically yield noisy outputs. We hypothesize that exploiting the inter-dependencies between …...

AbstractIdentifying dialogue acts and dialogue modes during tutorial interactions is an extremely crucial sub-step in understanding patterns of effective tutor-tutee interactions. In this work, we develop a novel joint inference …...

AbstractJoint inference approaches such as Integer Linear Programming (ILP) and Markov Logic Networks (MLNs) have recently been successfully applied to many natural language processing (NLP) tasks, often outperforming their pipeline …...