Distributional Analysis of Verbal Neologisms: Task Definition and Dataset Construction

In this paper we introduce the task of interpreting verbal neologism (VNeo) for the Italian language making use of a highly context-sensitive distributional semantic model (DSM). The task is commonly performed manually by lexicographers verifying the contexts in which the VNeo appear. Developing such a task is likely to be of use from a cognitive, social and linguistic perspective. In the following, we first outline the motivation for our study and our goal, then focus on the construction of the dataset and the definition of the task.In questo contributo introduciamo un task di interpretazione dei neologismi verbali (Vneo) in italiano, utilizzando un modello di semantica distribuzionale altamente sensibile al contesto. Questa attività è comunemente svolta manualmente dai lessicografi, i quali verificano il contesto in cui il Vneo appare. Sviluppare questo tipo di task può rivelarsi utile da una prospettiva linguistica, cognitiva e sociale. Di seguito presenteremo inizialmente le motivazioni e gli scopi dell’analisi, concentrandoci poi sulla costruzione del dataset e sulla definizione del task

Amnestic Forgery: an Ontology of Conceptual Metaphors

This paper presents Amnestic Forgery, an ontology for metaphor semantics, based on MetaNet, which is inspired by the theory of Conceptual Metaphor. Amnestic Forgery reuses and extends the Framester schema, as an ideal ontology design framework to deal with both semiotic and referential aspects of frames, roles, mappings, and eventually blending. The description of the resource is supplied by a discussion of its applications, with examples taken from metaphor generation, and the referential problems of metaphoric mappings. Both schema and data are available from the Framester SPARQL endpoint

Analogical reasoning in uncovering the meaning of digital-technology terms: the case of backdoor

[EN] The paper substantiates the critical role of analogical reasoning and figurative languge in resolving the ambiguity of cybersecurity terms in various expert communities. Dwelling on the divergent interpretations of a backdoor, it uncovers the potential of metaphor to serve both as an interpretative mechanism and as a framing tool in the ongoing digital technologies discourse. By combining methods of corpus research and frame semantics analysis the study examines the challenges of unpacking the meaning of the contested concept of the backdoor. The paper proposes a qualitatively new metaphor-facilitated mode of interpreting cybersecurity vulnerabilities based on MetaNet deep semantic metaphor analysis and outlines the merits of this hierarchically organized metaphor and frames ontology. The utility of the method is demonstrated through analyzing corpus data and top-down extracting of metaphors (linguistic metaphor – conceptual metaphor – entailed metaphor – inferences) with subsequent identifying of metaphor families dominating the cybersecurity discourse. The paper further claims that the predominant metaphors prompt certain decisions and solutions affecting information security policies. Skrynnikova, IV. (2020). Analogical reasoning in uncovering the meaning of digital-technology terms: the case of backdoor. Journal of Computer-Assisted Linguistic Research. 4(1):23-46. https://doi.org/10.4995/jclr.2020.12921OJS234641Betz, David and Stevens, Tim. 2013. "Analogical Reasoning and Cyber Security." Security Dialogue 44, No. 2: 147-164 (2013). https://doi.org/10.1177/0967010613478323David, Oana and Matlock, Teenie. 2018. "Cross-linguistic automated detection of metaphors for poverty and cancer." Language and Cognition 10 (2018), 467-493. UK Cognitive Linguistics Association. https://doi.org/10.1017/langcog.2018.11David, Oana. 2016. Metaphor in the grammar of argument realization. Unpublished doctoral dissertation, University of California, Berkeley.David, Oana, Lakoff, George, and Stickles, Elise. 2016. "Cascades in metaphor and grammar: A case study of metaphors in the gun debate." Constructions and Frames. 8. 10.1075/cf.8.2.04dav. https://doi.org/10.1075/cf.8.2.04davDavies, Mark. 2013. "Corpus of Global Web-Based English: 1.9 billion words from speakers in 20 countries." Available at: http://corpus.byu.edu/glowbe/Davies, Mark. and Fuchs, Robert. 2015. "Expanding horizons in the study of World Englishes with the 1.9 billion word Global Web-based English Corpus (GloWbE)." English World-Wide 36(1), 1-28. https://doi.org/10.1075/eww.36.1.01davDeignan, Alice. 2005. Metaphor and corpus linguistics. Amsterdam/Philadelphia: John Benjamins. https://doi.org/10.1075/celcr.6Demjén, Zsófia, Semino, Elena, and Koller, Veronika. 2016. "Metaphors for 'good' and 'bad' deaths." Metaphor and the Social World 6(1), 1-19. https://doi.org/10.1075/msw.6.1.01demDodge, Ellen. K., Hong, Jisup, and Stickles, Elise. 2015. "MetaNet: deep semantic automatic metaphor analysis." Proceedings of the Third Workshop on Metaphor in NLP, 40-49. Denver, Colorado, 5 June 2015. Association for Computational Linguistics. https://doi.org/10.3115/v1/W15-1405Do Dinh, Erik-Lân and Gurevych, Iryna. 2016. "Token-level metaphor detection using neural networks." Proceedings of the Fourth Workshop on Metaphor in NLP (June), 28-33. https://doi.org/10.18653/v1/W16-1104Dunn, Jonathan. 2013. "What metaphor identification systems can tell us about metaphor-inlanguage." Proceedings of the First Workshop on Metaphor in NLP, Atlanta Georgia, 13 June 2010, 1-10. Available at: http://www.aclweb.org/anthology/W13-0901Fillmore, Charles J. and Atkins, Beryl. T. 1992. "Toward a frame-based lexicon: the semantics of RISK and its neighbors." In Frames, fields, and contrasts: new essays in semantic and lexical organization, edited by A. Lehrer and E. F. Kittay, 75-102. New York/London: Routledge.Gedigian, M., Bryant, J., Narayanan, S., and Ciric, B. 2006. "Catching metaphors." Proceedings of the Third Workshop on Scalable Natural Language Understanding ScaNaLU 06 (June), 41-48. https://doi.org/10.3115/1621459.1621467Gill, Lex. 2018. "Law, Metaphor, and the Encrypted Machine." Osgoode Hall Law Journal 55.2: 440-477. Available at: https://digitalcommons.osgoode.yorku.ca/ohlj/vol55/iss2/3Gutiérrez, E. Dario, Shutova, Ekaterina, Marghetis, Tyler, and Bergen Benjamin. 2016. "Literal and metaphorical senses in compositional distributional semantic models." In Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics, Berlin, Germany, August 7-12, 2016, 183-193. https://doi.org/10.18653/v1/P16-1018Hallam-Baker, Phillip. 2008. dotCrime Manifesto: How to Stop Internet Crime. Addison-Wesley.Jenner, Leontine. 2018. "Backdoor: how a metaphor turns into a weapon." Available at: https://www.hiig.de/en/backdoor-how-a-metaphor-turns-into-a-weapon/Krishnakumaran, Saisuresh and Zhu, Xiaojin. 2007. "Hunting elusive metaphors using lexical resources." In Proceedings of the Workshop on Computational Approaches to Figurative Language, 13-20. Association for Computational Linguistics. https://doi.org/10.3115/1611528.1611531Kupers, Wendelin M. 2013. "Embodied transformative metaphors and narratives in organisational life‐worlds of change." Journal of Organizational Change Management, Vol. 26 Issue: 3, 494-528. https://doi.org/10.1108/09534811311328551Lakoff, George. 1993. "The contemporary theory of metaphor". In Metaphor and thought, edited by A. Ortony, 202-251. New York, NY, US: Cambridge University Press. https://doi.org/10.1017/CBO9781139173865.013Lakoff, George, and Johnson, Mark. 1980. Metaphors we live by. Chicago, IL: University of Chicago Press.Landwehr, C., Bull, A. R., McDermott, J. P., and Choi, W. S. 1994. "A Taxonomy of Computer Program Security Flaws, with Examples." ACM Computing Surv., vol. 26, no. 3, 211-254. https://doi.org/10.1145/185403.185412Lederer, Jenny. (2013). "Assessing claims of metaphorical salience through corpus data." In Proceedings of the 37th Annual Meeting of the Cognitive Science Society, editored by D. C. Noelle, R. Dale, A. S. Warlaumont, J. Yoshimi, T. Matlock, C. D. Jennings and P. P. Maglio, 1255-1260. Austin, TX: Cognitive Science Society.Lönneker, Birte. 2003. "Is there a way to represent metaphors in WordNets? Insights from the Hamburg Metaphor Database." Proceedings of the ACL 2003 Workshop on Lexicon and Figurative Language - Volume 14, 18-27. https://doi.org/10.3115/1118975.1118978Martin, James H. 2006. "A corpus-based analysis of context effects on metaphor comprehension." In Corpus-based approaches to metaphor and metonymy edited by S. T. Gries and A. Stefanowitsch, 214-236. Berlin: Mouton de Gruyter.Martin, James H. 1994. "MetaBank: a knowledge-base of metaphoric language conventions." Computational Intelligence 10(2), 134-149. https://doi.org/10.1111/j.1467-8640.1994.tb00161.xMason, Z. J. 2004. "CorMet: a computational, corpus-based conventional metaphor extraction system." Computational Linguistics 30(1), 23-44.https://doi.org/10.1162/089120104773633376Philip, G. 2004. "Locating metaphor candidates in specialized corpora using raw frequency and keyword lists." In Metaphor in use: context, culture, and communication edited by F. MacArthur, J. L. Oncins-Martínez, M. Sánchez-García and A. M. Piquer-Píriz, 85-105.Amsterdam: John Benjamins.Pragglejaz Group. 2007. "MIP: a method for identifying metaphorically used words in discourse." Metaphor and Symbol 22(1), 1-39. https://doi.org/10.1080/10926480709336752Shutova, Ekaterina, Teufel, Simone, and Korhonen, Anna. 2012. "Statistical metaphor processing." Computational Linguistics 39(2), 301-353. https://doi.org/10.1162/COLI_a_00124Shutova, Ekaterina and Sun, Lin. 2013. "Unsupervised metaphor identification using hierarchical graph factorization clustering." In Proceedings of NAACL-HLT 2013, Atlanta, Georgia, 9-14 June 2013, 978-988. Available at: http://www.aclweb.org/anthology/N13-1118Skrynnikova, Inna, Astafurova, Tatiana, and Sytina, Nadezhda. 2017. "Power of metaphor: cultural narratives in political persuasion." Proceedings of the 7th International Scientific and Practical Conference "Current issues of linguistics and didactics: The interdisciplinary approach in humanities" (CILDIAH 2017). https://doi.org/10.2991/cildiah-17.2017.50Steen, Gerard J., Dorst, Aletta, Berenike, Herrmann J., Kaal, Anna A., Krennmayr, Tina, and Pasma, Trijntje. 2010. A method for linguistic metaphor identification: from MIP to MIPVU. Amsterdam: John Benjamins. https://doi.org/10.1075/celcr.14Steen, Gerard, J. 1999. "From linguistic to conceptual metaphor in five steps." In Metaphor in cognitive linguistics, edited by R. W. Gibbs and G. J. Steen (Eds.), 57-77. Amsterdam/Philadelphia: John Benjamins. https://doi.org/10.1075/cilt.175.05steStefanowitsch, Anatol, and Gries, Stefan Th., eds. 2006. Corpus based approaches to metaphor and metonymy. Berlin/New York: Mouton de Gruyter. https://doi.org/10.1515/9783110199895Stickles, Elise, David, Oana, Dodge, Ellen K., and Hong, Jisup. 2016. "Formalizing contemporary conceptual metaphor theory." Constructions and Frames 8(2), 166-213. https://doi.org/10.1075/cf.8.2.03stiWolff, Josephine. 2014. "Cybersecurity as Metaphor: Policy and Defense Implications of Computer Security Metaphors." Paper presented at TPRC Conference, March 31, 2014. https://doi.org/10.2139/ssrn.241863

基于动态分类的隐喻识别方法

隐喻计算是自然语言处理领域中的重要问题.本文尝试以差异性计算为基础,结合语言、心理和认知的角度对英语隐喻识别进行了深入的分析和探索.对人类而言,隐喻识别是一个动态分类的过程,动态分类是从多个角度来度量事物之间的差异性.本文研究了如何模仿人类来获取概念的特征、选择分类角度、在特定分类角度下计算差异性,并进行了英语名词性隐喻识别的实验.本文的方法对隐喻/常规表达识别的准确率达到85.4%,实验结果表明本文的方法是有效的

基于动态分类的隐喻识别方法

隐喻计算是自然语言处理领域中的重要问题.尝试以差异性计算为基础,结合语言、心理和认知的角度对英语隐喻识别进行深入分析和探索对人类而言,隐喻识别是一个动态分类的过程,动态分类是从多个角度来度量事物之间的差异性.研究了如何模仿人类来获取概念的特征、选择分类角度、在特定分类角度下计算差异性,并进行了英语名词性隐喻识别的实验.该方法对隐喻/常规表达识别的准确率达到85.4%,实验结果表明,该方法是有效的.国家自然科学基金(61075058)~

Re-Representing Metaphor: Modeling Metaphor Perception Using Dynamically Contextual Distributional Semantics

In this paper, we present a novel context-dependent approach to modeling word meaning, and apply it to the modeling of metaphor. In distributional semantic approaches, words are represented as points in a high dimensional space generated from co-occurrence statistics; the distances between points may then be used to quantifying semantic relationships. Contrary to other approaches which use static, global representations, our approach discovers contextualized representations by dynamically projecting low-dimensional subspaces; in these ad hoc spaces, words can be re-represented in an open-ended assortment of geometrical and conceptual configurations as appropriate for particular contexts. We hypothesize that this context-specific re-representation enables a more effective model of the semantics of metaphor than standard static approaches. We test this hypothesis on a dataset of English word dyads rated for degrees of metaphoricity, meaningfulness, and familiarity by human participants. We demonstrate that our model captures these ratings more effectively than a state-of-the-art static model, and does so via the amount of contextualizing work inherent in the re-representational process

Proceedings of the Fifth Italian Conference on Computational Linguistics CLiC-it 2018 : 10-12 December 2018, Torino

On behalf of the Program Committee, a very warm welcome to the Fifth Italian Conference on Computational Linguistics (CLiC-­‐it 2018). This edition of the conference is held in Torino. The conference is locally organised by the University of Torino and hosted into its prestigious main lecture hall “Cavallerizza Reale”. The CLiC-­‐it conference series is an initiative of the Italian Association for Computational Linguistics (AILC) which, after five years of activity, has clearly established itself as the premier national forum for research and development in the fields of Computational Linguistics and Natural Language Processing, where leading researchers and practitioners from academia and industry meet to share their research results, experiences, and challenges

Proceedings of the Fifth Italian Conference on Computational Linguistics CLiC-it 2018

On behalf of the Program Committee, a very warm welcome to the Fifth Italian Conference on Computational Linguistics (CLiC-­‐it 2018). This edition of the conference is held in Torino. The conference is locally organised by the University of Torino and hosted into its prestigious main lecture hall “Cavallerizza Reale”. The CLiC-­‐it conference series is an initiative of the Italian Association for Computational Linguistics (AILC) which, after five years of activity, has clearly established itself as the premier national forum for research and development in the fields of Computational Linguistics and Natural Language Processing, where leading researchers and practitioners from academia and industry meet to share their research results, experiences, and challenges