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 Unpublished doctoral dissertation, University of California, Berkeley.David, Oana, Lakoff, George, and Stickles, Elise. 2016.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

Exploring the impact of linguistic signals transmission on patients’ health consultation choice: web mining of online reviews

Background: Patients face difficulties identifying appropriate physicians owing to the sizeable quantity and uneven quality of information in physician rating websites. Therefore, an increasing dependence of consumers on online platforms as a source of information for decisionmaking has given rise to the need for further research into the quality of information in the form of online physician reviews (OPRs). Methods: Drawing on the signaling theory, this study develops a theoretical model to examine how linguistic signals (affective signals and informative signals) in physician rating websites affect consumers’ decision making. The hypotheses are tested using 5521 physicians’ six-month data drawn from two leading health rating platforms in the U.S (i.e., Healthgrades.com and Vitals.com) during the COVID-19 pandemic. A sentic computing-based sentiment analysis framework is used to implicitly analyze patients’ opinions regarding their treatment choice. Results: The results indicate that negative sentiment, review readability, review depth, review spelling, and information helpfulness play a significant role in inducing patients’ decision-making. The influence of negative sentiment, review depth on patients’ treatment choice was indirectly mediated by information helpfulness. Conclusions: This paper is a first step toward the understanding of the linguistic characteristics of information relating to the patient experience, particularly the emerging field of online health behavior and signaling theory. It is also the first effort to our knowledge that employs sentic computing-based sentiment analysis in this context and provides implications for practice

Proceedings ICPW'07: 2nd International Conference on the Pragmatic Web, 22-23 Oct. 2007, Tilburg: NL

Proceedings ICPW'07: 2nd International Conference on the Pragmatic Web, 22-23 Oct. 2007, Tilburg: N

Knowledge Management

In light of globalisation an intensive global competition has evolved amongst corporations and companies, which have started extending their activities to the international market by establishing more and more subsidiaries all over the world. On the other hand, these extensions have generated problems for managers as their departments and units have got to be diversely located geographically and they have to find a solution for transferring knowledge, the core competences of the organization within their departments, units and teams to sustain the business activities and operations. To bridge over the geographical distance, people within organization have started using non-face-to-face technological (ICT) tools to be able to discuss problems, requests, solutions and develop business solutions or solve tasks were required in different places at the same time. The aim of this thesis is to create a theoretical framework to answer the questions how people use ICT tools for tacit knowledge sharing and which factors influence how actually these tools are used. The framework is built on the results of the inductive study. This study is conducted as a qualitative case study by interviewing nine members of the Hungarian department. The empirical research pointed out that the tacit knowledge sharing through ICT tools (especially email, instant messaging and telephone) within the case company is influenced by organizational, social, relational context and characteristics of ICT tools.fi=Opinnäytetyö kokotekstinä PDF-muodossa.|en=Thesis fulltext in PDF format.|sv=Lärdomsprov tillgängligt som fulltext i PDF-format

JURI SAYS:An Automatic Judgement Prediction System for the European Court of Human Rights

In this paper we present the web platform JURI SAYS that automatically predicts decisions of the European Court of Human Rights based on communicated cases, which are published by the court early in the proceedings and are often available many years before the final decision is made. Our system therefore predicts future judgements of the court. The platform is available at jurisays.com and shows the predictions compared to the actual decisions of the court. It is automatically updated every month by including the prediction for the new cases. Additionally, the system highlights the sentences and paragraphs that are most important for the prediction (i.e. violation vs. no violation of human rights)

The role of consumer-brand engagement in a digital marketing era

The purpose of this thesis is to understand the role of consumer-brand engagement in a digital marketing era. We explore the consumer-brand engagement construct in relation to consumers as the engagement subjects, and brands (i.e., brand/companies) as the engagement objects. Our intention is to contribute to advancing the theoretical knowledge of this subject and to provide useful insights that can be used by practitioners, particularly companies that use interactive platforms to create consumer-brand relationships.O objetivo desta tese é o de compreender o papel do compromisso entre o consumidor e a marca nesta nova era de marketing digital. Exploramos nesse sentido o constructo do compromisso entre o consumidor e a marca, sendo o consumidor o sujeito do compromisso e a marca (isto é, marcas ou empresas) o objeto desse compromisso. É nosso objetivo contribuir para o avanço teórico do conhecimento sobre esta área do saber, bem como fornecer novos conhecimentos que possam ser úteis e utilizados pelos gestores nas empresas, nomeadamente no que diz respeito a empresas que utilizem plataformas interativas para criar relacionamentos entre os consumidores e as marcas

Contextual Authority Tagging: Expertise Location via Social Labeling

This study investigates the possibility of a group of people making explicit their tacit knowledge about one another's areas of expertise. Through a design consisting of a modified Delphi Study, group members are asked to label both their own and each others' areas of expertise over the course of five rounds. Statistical analysis and qualitative evaluation of 10 participating organizations suggest they were successful and that, with simple keywords, group members can convey the salient areas of expertise of their colleagues to a degree that is deemed similar'' and of high quality by both third parties and those being evaluated. More work needs to be done to make this information directly actionable, but the foundational aspects have been identified. In a world with a democratization of voices from all around and increasing demands on our time and attention, this study suggests that simple, aggregated third-party expertise evaluations can augment our ongoing struggle for quality information source selection. These evaluations can serve as loose credentials when more expensive or heavyweight reputation cues may not be viable

Knowledge Management, Trust and Communication in the Era of Social Media

The article entitled "Selected Aspects of Evaluating Knowledge Management Quality in Contemporary Enterprises" broadens the understanding of knowledge management and estimates select aspects of knowledge management quality evaluations in modern enterprises from theoretical and practical perspectives. The seventh article aims to present the results of pilot studies on the four largest Information Communication Technology (ICT) companies' involvement in promoting the Sustainable Development Goals (SDGs) through social media. Studies examine which communication strategy is used by companies in social media. The primary purpose of the eighth article is to present the relationship between trust and knowledge sharing, taking into account the importance of this issue in the efficiency of doing business. The results showed that trust is vital in sharing knowledge and essential in achieving a high-performance efficiency level. The ninth article presents the impact of social media on consumer choices in tourism and tourist products' specificity. The study's main purpose was to indicate the most commonly used social media in selecting a tourist destination and implementing Generation Y's journey. The 10th article aims to identify the most critical purposes of using social media by responding to women's attitudes according to age and their respective countries' economic development. The research was done through an online survey in 2017–2018, followed by an analysis of eight countries' results. The article entitled "Integrated Question-Answering System for Natural Disaster Domains Based on Social Media Messages Posted at the Time of Disaster" presents the framework of a question-answering system that was developed using a Twitter dataset containing more than 9 million tweets compiled during the Osaka North Earthquake that occurred on 18 June 2018. The authors also study the structure of the questions posed and develop methods for classifying them into particular categories to find answers from the dataset using an ontology, word similarity, keyword frequency, and natural language processing. The book provides a theoretical and practical background related to trust, knowledge management, and communication in the era of social media. The editor believes that the collection of articles can be relevant to professionals, researchers, and students' needs. The authors try to diagnose the situation and show the new challenges and future directions in this area

OntoTag - A Linguistic and Ontological Annotation Model Suitable for the Semantic Web

OntoTag - A Linguistic and Ontological Annotation Model Suitable for the Semantic Web 1. INTRODUCTION. LINGUISTIC TOOLS AND ANNOTATIONS: THEIR LIGHTS AND SHADOWS Computational Linguistics is already a consolidated research area. It builds upon the results of other two major ones, namely Linguistics and Computer Science and Engineering, and it aims at developing computational models of human language (or natural language, as it is termed in this area). Possibly, its most well-known applications are the different tools developed so far for processing human language, such as machine translation systems and speech recognizers or dictation programs. These tools for processing human language are commonly referred to as linguistic tools. Apart from the examples mentioned above, there are also other types of linguistic tools that perhaps are not so well-known, but on which most of the other applications of Computational Linguistics are built. These other types of linguistic tools comprise POS taggers, natural language parsers and semantic taggers, amongst others. All of them can be termed linguistic annotation tools. Linguistic annotation tools are important assets. In fact, POS and semantic taggers (and, to a lesser extent, also natural language parsers) have become critical resources for the computer applications that process natural language. Hence, any computer application that has to analyse a text automatically and ‘intelligently’ will include at least a module for POS tagging. The more an application needs to ‘understand’ the meaning of the text it processes, the more linguistic tools and/or modules it will incorporate and integrate. However, linguistic annotation tools have still some limitations, which can be summarised as follows: 1. Normally, they perform annotations only at a certain linguistic level (that is, Morphology, Syntax, Semantics, etc.). 2. They usually introduce a certain rate of errors and ambiguities when tagging. This error rate ranges from 10 percent up to 50 percent of the units annotated for unrestricted, general texts. 3. Their annotations are most frequently formulated in terms of an annotation schema designed and implemented ad hoc. A priori, it seems that the interoperation and the integration of several linguistic tools into an appropriate software architecture could most likely solve the limitations stated in (1). Besides, integrating several linguistic annotation tools and making them interoperate could also minimise the limitation stated in (2). Nevertheless, in the latter case, all these tools should produce annotations for a common level, which would have to be combined in order to correct their corresponding errors and inaccuracies. Yet, the limitation stated in (3) prevents both types of integration and interoperation from being easily achieved. In addition, most high-level annotation tools rely on other lower-level annotation tools and their outputs to generate their own ones. For example, sense-tagging tools (operating at the semantic level) often use POS taggers (operating at a lower level, i.e., the morphosyntactic) to identify the grammatical category of the word or lexical unit they are annotating. Accordingly, if a faulty or inaccurate low-level annotation tool is to be used by other higher-level one in its process, the errors and inaccuracies of the former should be minimised in advance. Otherwise, these errors and inaccuracies would be transferred to (and even magnified in) the annotations of the high-level annotation tool. Therefore, it would be quite useful to find a way to (i) correct or, at least, reduce the errors and the inaccuracies of lower-level linguistic tools; (ii) unify the annotation schemas of different linguistic annotation tools or, more generally speaking, make these tools (as well as their annotations) interoperate. Clearly, solving (i) and (ii) should ease the automatic annotation of web pages by means of linguistic tools, and their transformation into Semantic Web pages (Berners-Lee, Hendler and Lassila, 2001). Yet, as stated above, (ii) is a type of interoperability problem. There again, ontologies (Gruber, 1993; Borst, 1997) have been successfully applied thus far to solve several interoperability problems. Hence, ontologies should help solve also the problems and limitations of linguistic annotation tools aforementioned. Thus, to summarise, the main aim of the present work was to combine somehow these separated approaches, mechanisms and tools for annotation from Linguistics and Ontological Engineering (and the Semantic Web) in a sort of hybrid (linguistic and ontological) annotation model, suitable for both areas. This hybrid (semantic) annotation model should (a) benefit from the advances, models, techniques, mechanisms and tools of these two areas; (b) minimise (and even solve, when possible) some of the problems found in each of them; and (c) be suitable for the Semantic Web. The concrete goals that helped attain this aim are presented in the following section. 2. GOALS OF THE PRESENT WORK As mentioned above, the main goal of this work was to specify a hybrid (that is, linguistically-motivated and ontology-based) model of annotation suitable for the Semantic Web (i.e. it had to produce a semantic annotation of web page contents). This entailed that the tags included in the annotations of the model had to (1) represent linguistic concepts (or linguistic categories, as they are termed in ISO/DCR (2008)), in order for this model to be linguistically-motivated; (2) be ontological terms (i.e., use an ontological vocabulary), in order for the model to be ontology-based; and (3) be structured (linked) as a collection of ontology-based triples, as in the usual Semantic Web languages (namely RDF(S) and OWL), in order for the model to be considered suitable for the Semantic Web. Besides, to be useful for the Semantic Web, this model should provide a way to automate the annotation of web pages. As for the present work, this requirement involved reusing the linguistic annotation tools purchased by the OEG research group (http://www.oeg-upm.net), but solving beforehand (or, at least, minimising) some of their limitations. Therefore, this model had to minimise these limitations by means of the integration of several linguistic annotation tools into a common architecture. Since this integration required the interoperation of tools and their annotations, ontologies were proposed as the main technological component to make them effectively interoperate. From the very beginning, it seemed that the formalisation of the elements and the knowledge underlying linguistic annotations within an appropriate set of ontologies would be a great step forward towards the formulation of such a model (henceforth referred to as OntoTag). Obviously, first, to combine the results of the linguistic annotation tools that operated at the same level, their annotation schemas had to be unified (or, preferably, standardised) in advance. This entailed the unification (id. standardisation) of their tags (both their representation and their meaning), and their format or syntax. Second, to merge the results of the linguistic annotation tools operating at different levels, their respective annotation schemas had to be (a) made interoperable and (b) integrated. And third, in order for the resulting annotations to suit the Semantic Web, they had to be specified by means of an ontology-based vocabulary, and structured by means of ontology-based triples, as hinted above. Therefore, a new annotation scheme had to be devised, based both on ontologies and on this type of triples, which allowed for the combination and the integration of the annotations of any set of linguistic annotation tools. This annotation scheme was considered a fundamental part of the model proposed here, and its development was, accordingly, another major objective of the present work. All these goals, aims and objectives could be re-stated more clearly as follows: Goal 1: Development of a set of ontologies for the formalisation of the linguistic knowledge relating linguistic annotation. Sub-goal 1.1: Ontological formalisation of the EAGLES (1996a; 1996b) de facto standards for morphosyntactic and syntactic annotation, in a way that helps respect the triple structure recommended for annotations in these works (which is isomorphic to the triple structures used in the context of the Semantic Web). Sub-goal 1.2: Incorporation into this preliminary ontological formalisation of other existing standards and standard proposals relating the levels mentioned above, such as those currently under development within ISO/TC 37 (the ISO Technical Committee dealing with Terminology, which deals also with linguistic resources and annotations). Sub-goal 1.3: Generalisation and extension of the recommendations in EAGLES (1996a; 1996b) and ISO/TC 37 to the semantic level, for which no ISO/TC 37 standards have been developed yet. Sub-goal 1.4: Ontological formalisation of the generalisations and/or extensions obtained in the previous sub-goal as generalisations and/or extensions of the corresponding ontology (or ontologies). Sub-goal 1.5: Ontological formalisation of the knowledge required to link, combine and unite the knowledge represented in the previously developed ontology (or ontologies). Goal 2: Development of OntoTag’s annotation scheme, a standard-based abstract scheme for the hybrid (linguistically-motivated and ontological-based) annotation of texts. Sub-goal 2.1: Development of the standard-based morphosyntactic annotation level of OntoTag’s scheme. This level should include, and possibly extend, the recommendations of EAGLES (1996a) and also the recommendations included in the ISO/MAF (2008) standard draft. Sub-goal 2.2: Development of the standard-based syntactic annotation level of the hybrid abstract scheme. This level should include, and possibly extend, the recommendations of EAGLES (1996b) and the ISO/SynAF (2010) standard draft. Sub-goal 2.3: Development of the standard-based semantic annotation level of OntoTag’s (abstract) scheme. Sub-goal 2.4: Development of the mechanisms for a convenient integration of the three annotation levels already mentioned. These mechanisms should take into account the recommendations included in the ISO/LAF (2009) standard draft. Goal 3: Design of OntoTag’s (abstract) annotation architecture, an abstract architecture for the hybrid (semantic) annotation of texts (i) that facilitates the integration and interoperation of different linguistic annotation tools, and (ii) whose results comply with OntoTag’s annotation scheme. Sub-goal 3.1: Specification of the decanting processes that allow for the classification and separation, according to their corresponding levels, of the results of the linguistic tools annotating at several different levels. Sub-goal 3.2: Specification of the standardisation processes that allow (a) complying with the standardisation requirements of OntoTag’s annotation scheme, as well as (b) combining the results of those linguistic tools that share some level of annotation. Sub-goal 3.3: Specification of the merging processes that allow for the combination of the output annotations and the interoperation of those linguistic tools that share some level of annotation. Sub-goal 3.4: Specification of the merge processes that allow for the integration of the results and the interoperation of those tools performing their annotations at different levels. Goal 4: Generation of OntoTagger’s schema, a concrete instance of OntoTag’s abstract scheme for a concrete set of linguistic annotations. These linguistic annotations result from the tools and the resources available in the research group, namely • Bitext’s DataLexica (http://www.bitext.com/EN/datalexica.asp), • LACELL’s (POS) tagger (http://www.um.es/grupos/grupo-lacell/quees.php), • Connexor’s FDG (http://www.connexor.eu/technology/machinese/glossary/fdg/), and • EuroWordNet (Vossen et al., 1998). This schema should help evaluate OntoTag’s underlying hypotheses, stated below. Consequently, it should implement, at least, those levels of the abstract scheme dealing with the annotations of the set of tools considered in this implementation. This includes the morphosyntactic, the syntactic and the semantic levels. Goal 5: Implementation of OntoTagger’s configuration, a concrete instance of OntoTag’s abstract architecture for this set of linguistic tools and annotations. This configuration (1) had to use the schema generated in the previous goal; and (2) should help support or refute the hypotheses of this work as well (see the next section). Sub-goal 5.1: Implementation of the decanting processes that facilitate the classification and separation of the results of those linguistic resources that provide annotations at several different levels (on the one hand, LACELL’s tagger operates at the morphosyntactic level and, minimally, also at the semantic level; on the other hand, FDG operates at the morphosyntactic and the syntactic levels and, minimally, at the semantic level as well). Sub-goal 5.2: Implementation of the standardisation processes that allow (i) specifying the results of those linguistic tools that share some level of annotation according to the requirements of OntoTagger’s schema, as well as (ii) combining these shared level results. In particular, all the tools selected perform morphosyntactic annotations and they had to be conveniently combined by means of these processes. Sub-goal 5.3: Implementation of the merging processes that allow for the combination (and possibly the improvement) of the annotations and the interoperation of the tools that share some level of annotation (in particular, those relating the morphosyntactic level, as in the previous sub-goal). Sub-goal 5.4: Implementation of the merging processes that allow for the integration of the different standardised and combined annotations aforementioned, relating all the levels considered. Sub-goal 5.5: Improvement of the semantic level of this configuration by adding a named entity recognition, (sub-)classification and annotation subsystem, which also uses the named entities annotated to populate a domain ontology, in order to provide a concrete application of the present work in the two areas involved (the Semantic Web and Corpus Linguistics). 3. MAIN RESULTS: ASSESSMENT OF ONTOTAG’S UNDERLYING HYPOTHESES The model developed in the present thesis tries to shed some light on (i) whether linguistic annotation tools can effectively interoperate; (ii) whether their results can be combined and integrated; and, if they can, (iii) how they can, respectively, interoperate and be combined and integrated. Accordingly, several hypotheses had to be supported (or rejected) by the development of the OntoTag model and OntoTagger (its implementation). The hypotheses underlying OntoTag are surveyed below. Only one of the hypotheses (H.6) was rejected; the other five could be confirmed. H.1 The annotations of different levels (or layers) can be integrated into a sort of overall, comprehensive, multilayer and multilevel annotation, so that their elements can complement and refer to each other. • CONFIRMED by the development of: o OntoTag’s annotation scheme, o OntoTag’s annotation architecture, o OntoTagger’s (XML, RDF, OWL) annotation schemas, o OntoTagger’s configuration. H.2 Tool-dependent annotations can be mapped onto a sort of tool-independent annotations and, thus, can be standardised. • CONFIRMED by means of the standardisation phase incorporated into OntoTag and OntoTagger for the annotations yielded by the tools. H.3 Standardisation should ease: H.3.1: The interoperation of linguistic tools. H.3.2: The comparison, combination (at the same level and layer) and integration (at different levels or layers) of annotations. • H.3 was CONFIRMED by means of the development of OntoTagger’s ontology-based configuration: o Interoperation, comparison, combination and integration of the annotations of three different linguistic tools (Connexor’s FDG, Bitext’s DataLexica and LACELL’s tagger); o Integration of EuroWordNet-based, domain-ontology-based and named entity annotations at the semantic level. o Integration of morphosyntactic, syntactic and semantic annotations. H.4 Ontologies and Semantic Web technologies (can) play a crucial role in the standardisation of linguistic annotations, by providing consensual vocabularies and standardised formats for annotation (e.g., RDF triples). • CONFIRMED by means of the development of OntoTagger’s RDF-triple-based annotation schemas. H.5 The rate of errors introduced by a linguistic tool at a given level, when annotating, can be reduced automatically by contrasting and combining its results with the ones coming from other tools, operating at the same level. However, these other tools might be built following a different technological (stochastic vs. rule-based, for example) or theoretical (dependency vs. HPS-grammar-based, for instance) approach. • CONFIRMED by the results yielded by the evaluation of OntoTagger. H.6 Each linguistic level can be managed and annotated independently. • REJECTED: OntoTagger’s experiments and the dependencies observed among the morphosyntactic annotations, and between them and the syntactic annotations. In fact, Hypothesis H.6 was already rejected when OntoTag’s ontologies were developed. We observed then that several linguistic units stand on an interface between levels, belonging thereby to both of them (such as morphosyntactic units, which belong to both the morphological level and the syntactic level). Therefore, the annotations of these levels overlap and cannot be handled independently when merged into a unique multileveled annotation. 4. OTHER MAIN RESULTS AND CONTRIBUTIONS First, interoperability is a hot topic for both the linguistic annotation community and the whole Computer Science field. The specification (and implementation) of OntoTag’s architecture for the combination and integration of linguistic (annotation) tools and annotations by means of ontologies shows a way to make these different linguistic annotation tools and annotations interoperate in practice. Second, as mentioned above, the elements involved in linguistic annotation were formalised in a set (or network) of ontologies (OntoTag’s linguistic ontologies). • On the one hand, OntoTag’s network of ontologies consists of − The Linguistic Unit Ontology (LUO), which includes a mostly hierarchical formalisation of the different types of linguistic elements (i.e., units) identifiable in a written text; − The Linguistic Attribute Ontology (LAO), which includes also a mostly hierarchical formalisation of the different types of features that characterise the linguistic units included in the LUO; − The Linguistic Value Ontology (LVO), which includes the corresponding formalisation of the different values that the attributes in the LAO can take; − The OIO (OntoTag’s Integration Ontology), which Includes the knowledge required to link, combine and unite the knowledge represented in the LUO, the LAO and the LVO; Can be viewed as a knowledge representation ontology that describes the most elementary vocabulary used in the area of annotation. • On the other hand, OntoTag’s ontologies incorporate the knowledge included in the different standards and recommendations for linguistic annotation released so far, such as those developed within the EAGLES and the SIMPLE European projects or by the ISO/TC 37 committee: − As far as morphosyntactic annotations are concerned, OntoTag’s ontologies formalise the terms in the EAGLES (1996a) recommendations and their corresponding terms within the ISO Morphosyntactic Annotation Framework (ISO/MAF, 2008) standard; − As for syntactic annotations, OntoTag’s ontologies incorporate the terms in the EAGLES (1996b) recommendations and their corresponding terms within the ISO Syntactic Annotation Framework (ISO/SynAF, 2010) standard draft; − Regarding semantic annotations, OntoTag’s ontologies generalise and extend the recommendations in EAGLES (1996a; 1996b) and, since no stable standards or standard drafts have been released for semantic annotation by ISO/TC 37 yet, they incorporate the terms in SIMPLE (2000) instead; − The terms coming from all these recommendations and standards were supplemented by those within the ISO Data Category Registry (ISO/DCR, 2008) and also of the ISO Linguistic Annotation Framework (ISO/LAF, 2009) standard draft when developing OntoTag’s ontologies. Third, we showed that the combination of the results of tools annotating at the same level can yield better results (both in precision and in recall) than each tool separately. In particular, 1. OntoTagger clearly outperformed two of the tools integrated into its configuration, namely DataLexica and FDG in all the combination sub-phases in which they overlapped (i.e. POS tagging, lemma annotation and morphological feature annotation). As far as the remaining tool is concerned, i.e. LACELL’s tagger, it was also outperformed by OntoTagger in POS tagging and lemma annotation, and it did not behave better than OntoTagger in the morphological feature annotation layer. 2. As an immediate result, this implies that a) This type of combination architecture configurations can be applied in order to improve significantly the accuracy of linguistic annotations; and b) Concerning the morphosyntactic level, this could be regarded as a way of constructing more robust and more accurate POS tagging systems. Fourth, Semantic Web annotations are usually pe