Awareness of L1 and L2 word-formation mechanisms for the development of a more autonomous L2 learner

Unlike traditional approaches, new communicative trends disregard the role of word-formation mechanisms. They tend to focus on syntax and/or vocabulary without analyzing the mechanisms involved in the creation of lexical items. In this paper, based on the analysis of the use of prefixes by L2 learners in oral and written productions, as provided by the SULEC, we emphasize the advantages that word-formation awareness and knowledge may have for the learners in terms of production, creativity, understanding, autonomy, and proficiency. Through the teaching of word-formation learners may more easily decipher, decode and/or encode messages, create words they have never seen before, etc.

Improving subjectivity detection using unsupervised subjectivity word sense disambiguation

En este trabajo se presenta un método para la detección de subjetividad a nivel de oraciones basado en la desambiguación subjetiva del sentido de las palabras. Para ello se extiende un método de desambiguación semántica basado en agrupamiento de sentidos para determinar cuándo las palabras dentro de la oración están siendo utilizadas de forma subjetiva u objetiva. En nuestra propuesta se utilizan recursos semánticos anotados con valores de polaridad y emociones para determinar cuándo un sentido de una palabra puede ser considerado subjetivo u objetivo. Se presenta un estudio experimental sobre la detección de subjetividad en oraciones, en el cual se consideran las colecciones del corpus MPQA y Movie Review Dataset, así como los recursos semánticos SentiWordNet, Micro-WNOp y WordNet-Affect. Los resultados obtenidos muestran que nuestra propuesta contribuye de manera significativa en la detección de subjetividad.

Redrawing Identity Boundaries through Integration Policies: Strategies of Inclusion/Exclusion of Immigrants in Québec and South Tyrol. EDAP Paper 01/2013

Following the research agenda introduced by Will Kymlicka, this qualitative study offers an interpretation of how the sub-national elites of Québec and South Tyrol police the integration of immigrants. For these national minority groups, which are constantly undergoing a process of redefinition of their collective identities by differentiating themselves from the Others who do not belong to the in-group, immigrants have progressively become the most significant Others as they are not part of the original system of compromises. This article questions how sub-national elites are handling this relatively new kind of ethnocultural diversity brought about by large-scale permanent immigration on two levels: first, the political narrative of the ruling sub-national parties, their electoral appeals, manifestos and speeches; second, the policy arrangements for the integration of immigrants in education, language and social policy. The initial approach of the article is pessimistic, as it assumes that sub-national elites will marginalize immigrants to please core nationalist supporters. In fact, the hypotheses to be tested are whether the national minority groups of Québec and South Tyrol engage in a process of reconstruction of their ethnic identity bounded by opposition to real or imagined Others – the newcomers; and whether they adopt practical measures that force newcomers to be assimilated into the group or to be marginalized. The comparison between Québec and South Tyrol provides a basic understanding of the impact of immigration in two sub-national polities that are very different, but still adopt similar political narratives and policy strategies with regard to the integration of newcomers.

Breaking the boundaries. The transformation of Central European gas markets. OSW Point of View Number 46, December 2014

According to the European Council decision of February 2011, the process of creating the European Union’s internal gas market should be completed by the end of 2014. Therefore, it is worth summarising the changes which have taken place in the gas markets of Central Europe so far. The past few years have seen not only a period of gradual ‘marketisation’ of the national gas sectors, but also the building of new gas infrastructure, a redrawing of the gas flow map, and changes in the ownership of the Central European gas companies. Another change in Central Europe is the fact that individual states and companies are moving away from their traditional focus on their national gas markets; instead, they are beginning to develop a variety of concepts for the regional integration of Central European markets. This publication attempts to grasp the main elements of the ongoing transformation of Central Europe’s gas markets, with particular emphasis on the situation in Poland, the Czech Republic, Slovakia and Hungary.

Lightweight Internet Traffic Classification - A Subject Based Solution with Word Embeddings

Internet traffic classification is a relevant and mature research field, anyway of growing importance and with still open technical challenges, also due to the pervasive presence of Internet-connected devices into everyday life. We claim the need for innovative traffic classification solutions capable of being lightweight, of adopting a domain-based approach, of not only concentrating on application-level protocol categorization but also classifying Internet traffic by subject. To this purpose, this paper originally proposes a classification solution that leverages domain name information extracted from IPFIX summaries, DNS logs, and DHCP leases, with the possibility to be applied to any kind of traffic. Our proposed solution is based on an extension of Word2vec unsupervised learning techniques running on a specialized Apache Spark cluster. In particular, learning techniques are leveraged to generate word-embeddings from a mixed dataset composed by domain names and natural language corpuses in a lightweight way and with general applicability. The paper also reports lessons learnt from our implementation and deployment experience that demonstrates that our solution can process 5500 IPFIX summaries per second on an Apache Spark cluster with 1 slave instance in Amazon EC2 at a cost of $ 3860 year. Reported experimental results about Precision, Recall, F-Measure, Accuracy, and Cohen's Kappa show the feasibility and effectiveness of the proposal. The experiments prove that words contained in domain names do have a relation with the kind of traffic directed towards them, therefore using specifically trained word embeddings we are able to classify them in customizable categories. We also show that training word embeddings on larger natural language corpuses leads improvements in terms of precision up to 180%.