Markov logic networks based alias links identification and canonical mention selection in text

Abstract

Text analysis, specifically, narratives, wherein identification of distinct and independent participants (entities of interest) in a narrative is an important task for many NLP applications. This task becomes challenging because these participants are often referred to using multiple aliases. Identifying aliases of participants in a narrative is crucial for NLP applications. Existing conventional methods are supervised for alias identification which requires a large amount of manually annotated (labeled) data and are also prone to errors. Embodiments of the present disclosure provide systems and methods that implement Markov Logic Network (MLN) to encode linguistic knowledge into rules for identification of aliases for aliases mention identification using proper nouns, pronouns or noun phrases with common noun headword.