Abductive Inference for Interpretation of Metaphors

Ovchinnikova, Ekaterina; Israel, Ross; Wertheim, Suzanne; Zaytsev, Vladimir; Montazeri, Niloofar; Hobbs, Jerry R.

doi:10.3115/v1/w14-2305

Cited by 13 publications

(12 citation statements)

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“…The metaphor mapping performance we have achieved is due to two advances over the work of Ovchinnikova et al (2014): a focus on source and target spans in each sentence and the creation of new knowledge bases. A span is a minimal excerpt of a sentence that is sufficient to mentally trigger the source or target concept.…”

Section: Knowledge Bases and Mapping Performancementioning

confidence: 99%

“…Ovchinnikova et al (2011) presented a semantic discourse processing framework based on abduction, which uses the Mini-Tacitus reasoner (Mulkar et al, 2007) to interpret a sentence by proving its logical form, merging redundancies wherever possible, and making any necessary assumptions. This work was extended by Ovchinnikova et al (2014) to address the interpretation of metaphor. They presented an end-toend metaphor interpretation system, going from the recognition of linguistic metaphors in text through to 2 Hobbs (1992) gives an example: "John is an elephant" should be interpreted as meaning that he is clumsy if it follows "Mary is graceful".…”

Section: Related Workmentioning

confidence: 99%

“…Computational research on metaphor has focused on the problems of (1) identifying linguistic metaphors in text (e.g., Steen et al, 2010;Shutova et al, 2010;Li and Sporleder, 2010) and then (2) identifying the source and target concepts invoked by each linguistic metaphor (e.g., Narayanan, 1997;Ovchinnikova et al, 2014;Gordon et al, 2015).…”

Section: Related Workmentioning

confidence: 99%

“…These encode information about what words or phrases trigger which source and target concepts. Ovchinnikova et al (2014) used collections of manually authored axioms for English and Russian, bootstrapped by finding related words and expressions in ConceptNet (Havasi et al, 2007). Manually authoring a knowledge base exploits the intuitions of the knowledge engineer, but these can fail to match the data.…”

Section: Knowledge Bases and Mapping Performancementioning

confidence: 99%

“…In this paper, we present work that performs this mapping within the abductive reasoning framework proposed by Hobbs (1992) and implemented by Ovchinnikova et al (2014). By handling metaphor mapping within a general framework for knowledgebased discourse processing, it is possible to extend conceptual mapping to give deeper analysis, as discussed in section 5.…”

Section: Introductionmentioning

confidence: 99%

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Proceedings of the Third Workshop on Metaphor in NLP

2015

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ii IntroductionCharacteristic to all areas of human activity (from poetic to ordinary to scientific) and, thus, to all types of discourse, metaphor becomes an important problem for natural language processing. Its ubiquity in language has been established in a number of corpus studies and the role it plays in human reasoning has been confirmed in psychological experiments. This makes metaphor an important research area for computational and cognitive linguistics, and its automatic identification and interpretation indispensable for any semantics-oriented NLP application. This year's workshop is the third Metaphor in NLP workshop, following the first workshop held at NAACL 2013 and the second workshop held at ACL 2014. In 2013, accepted papers dealt with metaphor annotation, features for metaphor identification, and with generalization of the techniques across languages. These themes were also represented in the 2014 workshop, along with interpretation, applications, and relationships with related phenomena. In 2015, prominent themes include creation and utilization of semantic resources for metaphor identification and interpretation; features for metaphor identification that capture properties of concepts such as concreteness, imageability, affect, and sensorial modalities; relationships between social dynamic and individual history and metaphor use; and metaphor generation. We received 13 submissions and accepted 10, based on detailed and careful reviews by members of the Program Committee.Creation and utilization of semantic resources to support metaphor identification is a recurrent theme in the 2015 workshop. An invited talk by Prof. Martha Palmer and Dr. Susan Brown about metaphor in VerbNet was followed by a number of contributions describing the creation of resources in support of metaphor identification and analysis. Li, Bai ,Yin, and Xu describe the construction of a resource where salient properties of concepts expressed by thousands of Chinese verbs and nouns are collected. Dodge, Hong, and Stickles describe MetaNet, a system combining a repository of metaphors and frames, and a metaphor detection component that utilizes the repository. Gordon, Jobbs, May, and Morbini describe an enhancement to their knowledge-based metaphor identification system that infers lexical axioms -rules which encode information about what words or phrases trigger particular source and target concepts.Gordon, Hobbs, May, Mohler, Morbini, Rink, Tomlinson, and Wertheim describe their ontology of commonly used source domains and release a corpus of manually validated annotations of linguistic metaphors about governance, economy, and gun control with source and target domains, as well as specific roles (slots) that support the interpretation of the metaphor. For example, according to the ontology, a metaphor drawing on the source domain of JOURNEY can be annotated with elements such as source, target, agent, goal, facilitator, barrier, change, and type of change (increase or decrease). The goal of the dataset is to support the an...

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Section: Knowledge Bases and Mapping Performancementioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Knowledge Bases and Mapping Performancementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Proceedings of the Third Workshop on Metaphor in NLP

2015

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A Multi-feature Classifier for Verbal Metaphor Identification in Russian Texts

Badryzlova

Panicheva

2018

Communications in Computer and Information Science

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Do Topics Make a Metaphor? Topic Modeling for Metaphor Identification and Analysis in Russian

Badryzlova

Nikiforova

Lyashevskaya

2021

Lecture Notes in Computer Science

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The paper examines the efficiency of topic models as features for computational identification and conceptual analysis of linguistic metaphor on Russian data. We train topic models using three algorithms (LDA and ARTM -sparse and dense) and evaluate their quality. We compute topic vectors for sentences of a metaphor-annotated Russian corpus and train several classifiers to identify metaphor with these vectors. We compare the performance of the topic modeling classifiers with other state-of-the-art features (lexical, morphosyntactic, semantic coherence, and concreteness-abstractness) and their different combinations to see how topics contribute to metaphor identification. We show that some of the topics are more frequent in metaphoric contexts while others are more characteristic of non-metaphoric sentences, thus constituting topic predictors of metaphoricity, and discuss whether these predictors align with the conceptual mappings attested in literature. We also compare the topical heterogeneity of metaphoric and non-metaphoric contexts in order to test the hypothesis that metaphoric discourse should display greater topical variability due to the presence of Source and Target domains.

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Abductive Inference for Interpretation of Metaphors

Cited by 13 publications

References 14 publications

Proceedings of the Third Workshop on Metaphor in NLP

Proceedings of the Third Workshop on Metaphor in NLP

A Multi-feature Classifier for Verbal Metaphor Identification in Russian Texts

Do Topics Make a Metaphor? Topic Modeling for Metaphor Identification and Analysis in Russian

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