Comparing Semantic Role Labeling with Typed Dependency Parsing in Computational Metaphor Identification
نویسندگان
چکیده
Most computational approaches to metaphor have focused on discerning between metaphorical and literal text. Recent work on computational metaphor identification (CMI) instead seeks to identify overarching conceptual metaphors by mapping selectional preferences between source and target corpora. This paper explores using semantic role labeling (SRL) in CMI. Its goals are two-fold: first, to demonstrate that semantic roles can effectively be used to identify conceptual metaphors, and second, to compare SRL to the current use of typed dependency parsing in CMI. The results show that SRL can be used to identify potential metaphors and that it overcomes some of the limitations of using typed dependencies, but also that SRL introduces its own set of complications. The paper concludes by suggesting future directions, both for evaluating the use of SRL in CMI, and for fostering critical and creative thinking about metaphors.
منابع مشابه
برچسبزنی خودکار نقشهای معنایی در جملات فارسی به کمک درختهای وابستگی
Automatic identification of words with semantic roles (such as Agent, Patient, Source, etc.) in sentences and attaching correct semantic roles to them, may lead to improvement in many natural language processing tasks including information extraction, question answering, text summarization and machine translation. Semantic role labeling systems usually take advantage of syntactic parsing and th...
متن کاملJoint learning of dependency parsing and semantic role labeling
When natural language processing tasks overlap in their linguistic input space, they can be technically merged. Applying machine learning algorithms to the new joint task and comparing the results of joint learning with disjoint learning of the original tasks may bring to light the linguistic relatedness of the two tasks. We present a joint learning experiment with dependency parsing and semant...
متن کاملCMILLS: Adapting Semantic Role Labeling Features to Dependency Parsing
We describe a system for semantic role labeling adapted to a dependency parsing framework. Verb arguments are predicted over nodes in a dependency parse tree instead of nodes in a phrase-structure parse tree. Our system participated in SemEval-2015 shared Task 15, Subtask 1: CPA parsing and achieved an Fscore of 0.516. We adapted features from prior semantic role labeling work to the dependency...
متن کاملبرچسبزنی نقش معنایی جملات فارسی با رویکرد یادگیری مبتنی بر حافظه
Abstract Extracting semantic roles is one of the major steps in representing text meaning. It refers to finding the semantic relations between a predicate and syntactic constituents in a sentence. In this paper we present a semantic role labeling system for Persian, using memory-based learning model and standard features. Our proposed system implements a two-phase architecture to first identify...
متن کاملDiscriminative vs. Generative Approaches in Semantic Role Labeling
This paper describes the two algorithms we developed for the CoNLL 2008 Shared Task “Joint learning of syntactic and semantic dependencies”. Both algorithms start parsing the sentence using the same syntactic parser. The first algorithm uses machine learning methods to identify the semantic dependencies in four stages: identification and labeling of predicates, identification and labeling of ar...
متن کامل