Multilingualism and conceptual modelling

One of the leading motivations behind the multilingual semantic web is to make resources accessible digitally in an online global multilingual context. Consequently, it is fundamental for knowledge bases to find a way to manage multilingualism and thus be equipped with those procedures for its conceptual modelling. In this context, the goal of this paper is to discuss how common-sense knowledge and cultural knowledge are modelled in a multilingual framework. More particularly, multilingualism and conceptual modelling are dealt with from the perspective of FunGramKB, a lexico-conceptual knowledge base for natural language understanding. This project argues for a clear division between the lexical and the conceptual dimensions of knowledge. Moreover, the conceptual layer is organized into three modules, which result from a strong commitment towards capturing semantic knowledge (Ontology), procedural knowledge (Cognicon) and episodic knowledge (Onomasticon). Cultural mismatches are discussed and formally represented at the three conceptual levels of FunGramKB.

Grammar in dictionaries revisited: the case of verbs with se

This paper is a study about the way in which se structures are represented in 20 verb entries of nine dictionaries of Spanish language. There is a large number of these structures and they are problematic for native and non native speakers. Verbs of the analysis are middle-high frequency and, in the most part of the cases, very polysemous, and this allows to observe interconnections between the different se structures and the different meanings of each verb. Data of the lexicographic analysis are cross-checked with corpus analysis of the same units. As a result, it is observed that there is a large variety in the data which are offered in each dictionary and in the way they are offered, inter and intradictionary. The reasons range from the theoretical overall of each Project to practical performance. This leads to the conclusion that it is necessary to further progress in the dictionary model it is being handled, in order to offer lexico-grammatical phenomenon such as se verbs in an accurate, clear and exhaustive way.

Digging up the frequency of phrasal verbs in English for the Police: the case of "up"

The present study focuses on the frequency of phrasal verbs with the particle up in the context of crime and police investigative work. This research emerges from the need to enlarge McCarthy and O’Dell’s (2004) scope from purely criminal behavior to police investigative actions. To do so, we relied on a corpus of 504,124 running words made up of spoken dialogues extracted from the script of the American TV series Castle shown on ABC since 2009. Based on Rudzka-Ostyn’s (2003) cognitive motivations for the particle up, we have identified five different meaning extensions for our phrasal verbs. Drawing from these findings, we have designed pedagogical activities for those L2 learners that study English at the Police Academy.

Motivation and Vocabulary Breadth in CLIL and EFL Contexts. Different age, Same Time of Exposure

Numerous studies have found a positive connection between learners’ motivation towards foreign language and foreign language achievement. The present study examines the role of motivation in receptive vocabulary breadth (size) of two groups of Spanish learners of different ages, but all with 734 hours of instruction in English as a Foreign Language (EFL): a CLIL (Content and Language Integrated Learning) group in primary education and a non-CLIL (or EFL) group in secondary education. Most students in both groups were found to be highly motivated. The primary CLIL group slightly overcame the secondary non-CLIL group with respect to the mean general motivation but this is a non-significant difference. The secondary group surpass significantly the primary group in receptive vocabulary size. No relationship between the receptive vocabulary knowledge and general motivation is found in the primary CLIL group. On the other hand, a positive significant connection, although a very small one, is identified for the secondary non-CLIL group. We will discuss on the type of test, the age of students and the type of instruction as variables that could be influencing the results.

On a first-name basis: Englishization and naming in Flanders

Following and contributing to the ongoing shift from more structuralist, system-oriented to more pragmatic, socio-cultural oriented anglicism research, this paper verifies to what extent the global spread of English affects naming patterns in Flanders. To this end, a diachronic database of first names is constructed, containing the top 75 most popular boy and girl names from 2005 until 2014. In a first step, the etymological background of these names is documented and the evolution in popularity of the English names in the database is tracked. Results reveal no notable surge in the preference for English names. This paper complements these database-driven results with an experimental study, aiming to show how associations through referents are in this case more telling than associations through phonological form (here based on etymology). Focusing on the socio-cultural background of first names in general and of Anglo-American pop culture in particular, the second part of the study specifically reports on results from a survey where participants are asked to name the first three celebrities that leap to mind when hearing a certain first name (e.g. Lana, triggering the response Del Rey). Very clear associations are found between certain first names and specific celebrities from Anglo-American pop culture. Linking back to marketing research and the social turn in onomastics, we will discuss how these celebrities might function as referees, and how social stereotypes surrounding these referees are metonymically attached to their first names. Similar to the country-of-origin-effect in marketing, these metonymical links could very well be the reason why parents select specific “celebrity names”. Although further attitudinal research is needed, this paper supports the importance of including socio-cultural parameters when conducting onomastic research.

Receptive Vocabulary of CLIL and Non-CLIL Primary and Secondary School Learners

CLIL instruction has been reported to be beneficial for foreign language vocabulary learning since CLIL students show higher vocabulary profiles than students of their same age in traditional EFL contexts. However, to our knowledge, the receptive vocabulary knowledge of CLIL and non-CLIL learners at the end of primary and secondary education has not been examined yet. Hence, this study aims at comparing the receptive vocabulary size 79 CLIL primary learners with the receptive vocabulary knowledge of 331 non-CLIL learners at the end of primary and secondary school. Sex-based differences were also analysed. The 2k Vocabulary Levels Test (VLT) was used for the purposes of the study. Results revealed that learners’ receptive vocabulary sizes lie within the most frequent 1000 words, non-CLIL secondary school students throw better results than primary students but the differences between the secondary group and the CLIL group are not statistically significant. As for sex-based differences, we found no significant differences among the groups. These findings led us to believe that the CLIL approach offers a benefit for vocabulary acquisition since CLIL learners have been exposed to the foreign language for a shorter period of time and the results are quite similar to their non-CLIL secondary school partners.

Recommending peers for learning: Matching on dissimilarity in interpretations to provoke breakdown

People recommenders are a widespread feature of social networking sites and educational social learning platforms alike. However, when these systems are used to extend learners’ Personal Learning Networks, they often fall short of providing recommendations of learning value to their users. This paper proposes a design of a people recommender based on content-based user profiles, and a matching method based on dissimilarity therein. It presents the results of an experiment conducted with curators of the content curation site Scoop.it!, where curators rated personalized recommendations for contacts. The study showed that matching dissimilarity of interpretations of shared interests is more successful in providing positive experiences of breakdown for the curator than is matching on similarity. The main conclusion of this paper is that people recommenders should aim to trigger constructive experiences of breakdown for their users, as the prospect and potential of such experiences encourage learners to connect to their recommended peers.

Modeling Individual Differences among Writers Using ReaderBench

The current study builds upon a previous study, which examined the degree to which the lexical properties of students’ essays could predict their vocabulary scores. We expand on this previous research by incorporating new natural language processing indices related to both the surface- and discourse-levels of students’ essays. Additionally, we investigate the degree to which these NLP indices can be used to account for variance in students’ reading comprehension skills. We calculated linguistic essay features using our framework, ReaderBench, which is an automated text analysis tools that calculates indices related to linguistic and rhetorical features of text. University students (n = 108) produced timed (25 minutes), argumentative essays, which were then analyzed by ReaderBench. Additionally, they completed the Gates-MacGinitie Vocabulary and Reading comprehension tests. The results of this study indicated that two indices were able to account for 32.4% of the variance in vocabulary scores and 31.6% of the variance in reading comprehension scores. Follow-up analyses revealed that these models further improved when only considering essays that contained multiple paragraph (R2 values = .61 and .49, respectively). Overall, the results of the current study suggest that natural language processing techniques can help to inform models of individual differences among student writers.

Document Cohesion Flow: Striving towards Coherence

Text cohesion is an important element of discourse processing. This paper presents a new approach to modeling, quantifying, and visualizing text cohesion using automated cohesion flow indices that capture semantic links among paragraphs. Cohesion flow is calculated by applying Cohesion Network Analysis, a combination of semantic distances, Latent Semantic Analysis, and Latent Dirichlet Allocation, as well as Social Network Analysis. Experiments performed on 315 timed essays indicated that cohesion flow indices are significantly correlated with human ratings of text coherence and essay quality. Visualizations of the global cohesion indices are also included to support a more facile understanding of how cohesion flow impacts coherence in terms of semantic dependencies between paragraphs.

(Semi-)automatisch ondertitelen en vertalen van leermateriaal

This presentation summarizes experience with the automated speech recognition and translation approach realised in the context of the European project EMMA.

Expressing Sentiments in Game Reviews

Opinion mining and sentiment analysis are important research areas of Natural Language Processing (NLP) tools and have become viable alternatives for automatically extracting the affective information found in texts. Our aim is to build an NLP model to analyze gamers’ sentiments and opinions expressed in a corpus of 9750 game reviews. A Principal Component Analysis using sentiment analysis features explained 51.2 % of the variance of the reviews and provides an integrated view of the major sentiment and topic related dimensions expressed in game reviews. A Discriminant Function Analysis based on the emerging components classified game reviews into positive, neutral and negative ratings with a 55 % accuracy.

Combining Taxonomies using Word2vec

Taxonomies have gained a broad usage in a variety of fields due to their extensibility, as well as their use for classification and knowledge organization. Of particular interest is the digital document management domain in which their hierarchical structure can be effectively employed in order to organize documents into content-specific categories. Common or standard taxonomies (e.g., the ACM Computing Classification System) contain concepts that are too general for conceptualizing specific knowledge domains. In this paper we introduce a novel automated approach that combines sub-trees from general taxonomies with specialized seed taxonomies by using specific Natural Language Processing techniques. We provide an extensible and generalizable model for combining taxonomies in the practical context of two very large European research projects. Because the manual combination of taxonomies by domain experts is a highly time consuming task, our model measures the semantic relatedness between concept labels in CBOW or skip-gram Word2vec vector spaces. A preliminary quantitative evaluation of the resulting taxonomies is performed after applying a greedy algorithm with incremental thresholds used for matching and combining topic labels.

D6.3 – Semantic Content Annotation Support

The Semantic Annotation component is a software application that provides support for automated text classification, a process grounded in a cohesion-centered representation of discourse that facilitates topic extraction. The component enables the semantic meta-annotation of text resources, including automated classification, thus facilitating information retrieval within the RAGE ecosystem. It is available in the ReaderBench framework (http://readerbench.com/) which integrates advanced Natural Language Processing (NLP) techniques. The component makes use of Cohesion Network Analysis (CNA) in order to ensure an in-depth representation of discourse, useful for mining keywords and performing automated text categorization. Our component automatically classifies documents into the categories provided by the ACM Computing Classification System (http://dl.acm.org/ccs_flat.cfm), but also into the categories from a high level serious games categorization provisionally developed by RAGE. English and French languages are already covered by the provided web service, whereas the entire framework can be extended in order to support additional languages.

MixKMeans: Clustering Question-Answer Archives

Community-driven Question Answering (CQA) systems that crowdsource experiential information in the form of questions and answers and have accumulated valuable reusable knowledge. Clustering of QA datasets from CQA systems provides a means of organizing the content to ease tasks such as manual curation and tagging. In this paper, we present a clustering method that exploits the two-part question-answer structure in QA datasets to improve clustering quality. Our method, {\it MixKMeans}, composes question and answer space similarities in a way that the space on which the match is higher is allowed to dominate. This construction is motivated by our observation that semantic similarity between question-answer data (QAs) could get localized in either space. We empirically evaluate our method on a variety of real-world labeled datasets. Our results indicate that our method significantly outperforms state-of-the-art clustering methods for the task of clustering question-answer archives.

Wie lässt sich das Interesse am Erlernen von Deutsch als Fremdsprache (wieder) steigern? : Warum lernt man als schwedischer Schüler neben der eigenen Muttersprache gerade Deutsch als zweite Fremdsprache? Was sind Anreize dafür?

By the means of a questionnaire the present work examines the attitudes among pupils between the 5th and 9th grade towards choosing French, Spanish or German as their third language. The main question to be answered is "What needs to be improved to raise the interest in choosing specifically German as their preferred third language?". The other questions posed are for example "Do they want to study a language at all?", "Which language do they want to study and why?" or "What motivates them to keep studying generally?". The results show a high motivation and that the most pupils have already decided for a specific language at the middle of the 5th grade. Family and friends play a crucial role in choosing their language in combination with other factors such as the experiences of visiting countries or settings where the target language is used. To raise the popularity of German as the chosen language is not a short time project. More variation in teaching and real contact with German people, for instance language trips, needs to be done or improved. Nearly all of the pupils want to use modern techniques like chat or video conversations instead of just reading a text book.