Special Issue Introduction: Semantic Role Labeling: An Introduction to the Special Issue

By Lluís Màrquez i Villodre, Xavier Carreras, K. Litkowski, Suzanne Stevenson. International Conference on Computational Logic, 2008

299 citations recorded by the source corpus.

Semantic role labeling, the computational identification and labeling of arguments in text, has become a leading task in computational linguistics today. Although the issues for this task have been studied for decades, the availability of large resources and the development of statistical machine learning methods have heightened the amount of effort in this field. This special issue presents selected and representative work in the field. This overview describes linguistic background of the problem, the movement from linguistic theories to computational practice, the major resources that are being used, an overview of steps taken in computational systems, and a description of the key issues and results in semantic role labeling (as revealed in several international evaluations). We assess weaknesses in semantic role labeling and identify important challenges facing the field. Overall, the opportunities and the potential for useful further research in semantic role labeling are considerable.

Subfields: Artificial Intelligence

View Paper: https://www.semanticscholar.org/paper/f84529b40be07ba1f8fdd3d318ddeacd7394c908

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Citations in the corpus

Semantic Frame Identification with Distributed Word Representations (2014, result): “ond, we present results on PropBank-style semantic role labeling (Palmer et al., 2005; Meyers et al., 2004; Màrquez et al., 2008), that approach strong baselines, and are on par with prior state of the art (Punyakanok et al.”
Efficient Inference and Structured Learning for Semantic Role Labeling (2015, result): “Moreover, the semantic role labeling step has typically been divided into two stages: first identifying the spans that serve as semantic arguments and then labeling them with their roles (Màrquez et al., 2008).”
Construction of an annotated corpus to support biomedical information extraction (2009, background): “Initial experiments have demonstrated the feasibility of training an SRL using the corpus and as such, we hope to exploit the corpus in future shared tasks with such an aim There is also evidence to suggest that combining the GREC with other larger biomedical corpora may help to train more accurate ”
Automatic Crime Prediction Using Events Extracted from Twitter Posts (2012, background): “Gildea and Jurafsky documented the seminal investigation into SRL [9] and the NLP community has had a sustained interest in the technique since then (see [11] for a more recent survey and references).”
Automatically Solving Number Word Problems by Semantic Parsing and Reasoning (2015, background): “In semantic role labeling and frame-semantic parsing (Gildea & Jurafsky, 2002; Carreras & Marquez, 2004; Marquez et al., 2008; Baker et al., 2007; Das et al., 2014), predicate-argument structures are discovered from text as their shallow semantic representation.”
Challenges in Sentiment Analysis (2017, background): “Detecting these various roles is essentially a semantic rolelabeling problem (Gildea and Jurafsky 2002; Màrquez et al. 2008; Palmer et al. 2010), and it is possible that they can be modeled jointly to improve detection accuracy.”