Jointly Modeling WSD and SRL with Markov Logic
نویسندگان
چکیده
Semantic role labeling (SRL) and word sense disambiguation (WSD) are two fundamental tasks in natural language processing to find a sentence-level semantic representation. To date, they have mostly been modeled in isolation. However, this approach neglects logical constraints between them. We therefore exploit some pipeline systems which verify the automatic all word sense disambiguation could help the semantic role labeling and vice versa. We further propose a Markov logic model that jointly labels semantic roles and disambiguates all word senses. By evaluating our model on the OntoNotes 3.0 data, we show that this joint approach leads to a higher performance for word sense disambiguation and semantic role labeling than those pipeline approaches.
منابع مشابه
Jointly Identifying Predicates, Arguments and Senses using Markov Logic
In this paper we present a Markov Logic Network for Semantic Role Labelling that jointly performs predicate identification, frame disambiguation, argument identification and argument classification for all predicates in a sentence. Empirically we find that our approach is competitive: our best model would appear on par with the best entry in the CoNLL 2008 shared task open track, and at the 4th...
متن کامل1 Markov Logic: A Unifying Framework for Statistical Relational Learning
Interest in statistical relational learning (SRL) has grown rapidly in recent years. Several key SRL tasks have been identified, and a large number of approaches have been proposed. Increasingly, a unifying framework is needed to facilitate transfer of knowledge across tasks and approaches, to compare approaches, and to help bring structure to the field. We propose Markov logic as such a framew...
متن کاملMarkov Logic: A Unifying Framework for Statistical Relational Learning
Interest in statistical relational learning (SRL) has grown rapidly in recent years. Several key SRL tasks have been identified, and a large number of approaches have been proposed. Increasingly, a unifying framework is needed to facilitate transfer of knowledge across tasks and approaches, to compare approaches, and to help bring structure to the field. We propose Markov logic as such a framew...
متن کاملStatistical Relational Learning for Proteomics: Function, Interactions and Evolution
In recent years, the field of Statistical Relational Learning (SRL) [1, 2] has produced new, powerful learning methods that are explicitly designed to solve complex problems, such as collective classification, multi-task learning and structured output prediction, which natively handle relational data, noise, and partial information. Statistical-relational methods rely on some FirstOrder Logic a...
متن کاملMarkov Logic Networks: Theory, Algorithms and Applications
Most real world problems are characterized by relational structure i.e. entities and relationships between them. Further, they are inherently uncertain in nature. Theory of logic gives the framework to represent relations. Statistics provides the tools to handle uncertainty. Combining the power of two becomes important for accurate modeling of many real world domains. Last decade has seen the e...
متن کامل