Although research in other languages is increasing, much of the work in subjectivity analysis has been applied to English data, mainly due to the large body of electronic resources and tools that are available for this language. In this paper, we propose and evaluate methods that can be employed to transfer a repository of subjectivity resources across languages. Specifically, we attempt to leverage on the resources available for English and, by employing machine translation, generate resources for subjectivity analysis in other languages. Through comparative evaluations on two different languages (Romanian and Spanish), we show that automatic translation is a viable alternative for the construction of resources and tools for subjectivity analysis in a new target language.
In this paper, we address the task of crosslingual semantic relatedness. We introduce a method that relies on the information extracted from Wikipedia, by exploiting the interlanguage links available between Wikipedia versions in multiple languages. Through experiments performed on several language pairs, we show that the method performs well, with a performance comparable to monolingual measures of relatedness. MotivationGiven the accelerated growth of the number of multilingual documents on the Web and elsewhere, the need for effective multilingual and cross-lingual text processing techniques is becoming increasingly important. In this paper, we address the task of cross-lingual semantic relatedness, and introduce a method that relies on Wikipedia in order to calculate the relatedness of words across languages. For instance, given the word factory in English and the word lavoratore in Italian (En. worker), the method can measure the relatedness of these two words despite the fact that they belong to two different languages.Measures of cross-language relatedness are useful for a large number of applications, including cross-language information retrieval (Nie et al., 1999;Monz and Dorr, 2005), cross-language text classification (Gliozzo and Strapparava, 2006), lexical choice in machine translation (Och and Ney, 2000;Bangalore et al., 2007), induction of translation lexicons (Schafer and Yarowsky, 2002), cross-language annotation and resource projections to a second language (Riloff et al., 2002;Hwa et al., 2002;Mohammad et al., 2007).The method we propose is based on a measure of closeness between concept vectors automatically built from Wikipedia, which are mapped via the Wikipedia interlanguage links. Unlike previous methods for cross-language mapping, which are typically limited by the availability of bilingual dictionaries or parallel texts, the method proposed in this paper can be used to measure the relatedness of word pairs in any of the 250 languages for which a Wikipedia version exists.The paper is organized as follows. We first provide a brief overview of Wikipedia, followed by a description of the method to build concept vectors based on this encyclopedic resource. We then show how these concept vectors can be mapped across languages for a cross-lingual measure of word relatedness. Through evaluations run on six language pairs, connecting English, Spanish, Arabic and Romanian, we show that the method is effective at capturing the cross-lingual relatedness of words, with results comparable to the monolingual measures of relatedness.
Abstract:"Code is law" refers to the idea that, with the advent of digital technology, code has progressively established itself as the predominant way to regulate the behavior of Internet users. Yet, while computer code can enforce rules more efficiently than legal code, it also comes with a series of limitations, mostly because it is difficult to transpose the ambiguity and flexibility of legal rules into a formalized language which can be interpreted by a machine. With the advent of blockchain technology and associated smart contracts, code is assuming an even stronger role in regulating people's interactions over the Internet, as many contractual transactions get transposed into smart contract code. In this paper, we describe the shift from the traditional notion of "code is law" (i.e. code having the effect of law) to the new conception of "law is code" (i.e. law being defined as code). IntroductionThere are various ways in which law and technology can influence each other. The two interact through a complex system of dependencies and interdependencies, as both contribute (to a greater or lesser extent) to regulate the behavior of individuals. With the advent of modern information and communication technology, the relationship between the two has significantly evolved, as the latter is increasingly used as a complement or a supplement to the former. Lawyers, judges and policy makers are increasingly surrounded by digital information and software tools, which they use in their daily routine. While these tools can be used to support their activities, technological innovation also raises a variety of challenges, which the legal profession will eventually need to address. Specifically, it is possible to identify four distinct phases, in the late 20th and early 21st century, that represent the evolving relationship between law and technology.
Blockchain technologies have generated enthusiasm, yet their potential to enable new forms of governance remains largely unexplored. Two confronting standpoints dominate the emergent debate around blockchain-based governance: discourses characterized by the presence of techno-determinist and market-driven values, which tend to ignore the complexity of social organization; and critical accounts of such discourses which, while contributing to identifying limitations, consider the role of traditional centralized institutions as inherently necessary to enable democratic forms of governance. In this article, we draw on Ostrom’s principles for self-governance of communities to explore the transformative potential of blockchain beyond such standpoints. We approach blockchain through the identification and conceptualization of six affordances that this technology may provide to communities: tokenization, self-enforcement and formalization of rules, autonomous automatization, decentralization of power over the infrastructure, increasing transparency, and codification of trust. For each affordance, we carry out a detailed analysis situating each in the context of Ostrom’s principles, considering both the potentials of algorithmic governance and the importance of incorporating communities’ social practices into blockchain-based tools to foster forms of self-governance. The relationships found between these affordances and Ostrom’s principles allow us to provide a perspective focused on blockchain-based commons governance.
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