Computer Science and Engineering

Abstract The paper presents a metaphor processing centred system developed for the WWW, that assist students learning a foreign language. Metaphors may be of a real help in teaching activities for providing insights that are fundamental for true understanding. In fact, every good teacher uses metaphors in his lectures.

In AIED research, providing feedback for learning entails measuring differences among learners; between learners and their desired characteristics (eg, knowledge, competences, motivation, self-regulation processes); or between learners and their looked-for resources (eg, web-links, articles, courses) has often been performed by computing and analysing 'distances' using several techniques like factorial analysis, instance-based learning, clustering, and so on.

Online collaboration among communities of practice using text-based tools, such as instant messaging, forums and web logs (blogs), has become very popular in the last years, but it is difficult to automatically analyze all their content due to the problems of natural language understanding software. However, useful socio-semantic data can be retrieved from a chat conversation using ontology-based text mining techniques. In this paper, a novel approach for detecting several kinds of semantic data from a chat conversation is presented. This method uses a combination of a dialogistic, socio-cultural perspective and of classical knowledge-based text processing methods. Lexical and domain ontologies are used. A tool has been developed for the discovery of the most important topics and of the contribution of each participant in the conversation. The system also discovers new, implicit references among the utterances of the chat in order to offer a multi-voiced representation of the conversation. The application offers a panel for visualizing the threading of the subjects in the chat and the contributions function. The system was experimented on chat sessions of small groups of students participating in courses on Human-Computer Interaction and Natural Language Processing in ”Politehnica” University of Bucharest, Romania.

The paper describes a complex e-learning experiment that has involved over 700 students that attended the Human-Computer Interaction course at the “Politehnica” University of Bucharest during the last 4 years. The experiment consisted in using social web technologies like blogs and chat conferences to engage students in collaborative learning. The paper presents the learning scenario, the problems encountered and the tools developed for solving these problems and assisting tutors in evaluating the activity of the students. The results of the experiment and of using the blog and chat analysis tools are also covered. Moreover, we show the benefits of using such a scenario for the learning community formed by the students that attended this course in order to supplement the classical teaching and learning paradigm.

Abstract Depending on the user's intention, the queries processed by a search engine can be classified in transactional, informational and navigational [1]. In order to meet the three types of searches, at this moment search engines basically use algorithmic analysis of the links between pages improved by a factor that depends on the number of occurrences of the keywords in the query and the order of these words on each web page returned as a result. For transactional and informational queries, the relevance of the results returned by the ...

Although Computer-Supported Collaborative Learning (CSCL) advocates the use of instant messaging and discussion forums for collaboration between learners, there is a scarcity of tools for leveraging the information in this kind of conversations. Thus, these technologies are primarily used for communication and, once the conversation is over, the raw data is rarely manually analyzed by tutors, teachers and other learners. This paper presents a methodology and a system that can be used for providing feedback and support to learners and tutors that are involved in tasks that make use of chats and forums. In order to achieve this objective, PolyCAFe employs Natural Language Processing and Social Network Analysis techniques to discover polyphony and inter-animation in textual collaborations. To evaluate the proposed approach and the designed system a first validation experiment has been performed and the results are discussed and analyzed in the end of the paper.

This paper presents a model and an application that can be used to assess chat conversations according to their content, which is related to a number of imposed topics, and to the personal involvement of the participants. The main theoretical ideas that stand behind this application are Bakhtin’s polyphony theory and Tannen’s ideas related to the use of repetitions. The results of the application are validated against the gold standard provided by two teachers from the Human-Computer Interaction evaluating the same chats and after that the verification is done using another teacher from the same domain. During the verification we also show that the model used for chat evaluation is dependent on the number of participants to that chat.

The growing number of statistical topic models led
to the need to better evaluate their output. Tradi-
tional evaluation means estimate the model’s fit-
ness to unseen data. It has recently been proven
than the output of human judgment can greatly dif-
fer from these measures. Thus the need for me-
thods that better emulate human judgment is strin-
gent. In this paper we present a system that com-
putes the conceptual relevance of individual topics
from a given model on the basis of information
drawn from a given concept hierarchy, in this case
WordNet. The notion of conceptual relevance is
regarded as the ability to attribute a concept to each
topic and separate words related to the topic from
the unrelated ones based on that concept. In mul-
tiple experiments we prove the correlation between
the automatic evaluation method and the answers
received from human evaluators, for various corpo-
ra and difficulty levels. By changing the evaluation
focus from a statistical one to a conceptual one we
were able to detect which topics are conceptually
meaningful and rank them accordingly.

We investigated the influence of valence shifters on sentiment analysis within a new model built to extract opinions from economic texts. The system relies on implicit convictions that emerge from the studied texts through co-occurrences of economic indicators and future state modifiers. The polarity of the modifiers can however easily be reversed using negations, diminishers or intensifiers. We compared the system results with and without counting the effect of negations and future state modifier strength and we found that ...

This report is about the way to deliver relevant feedback to students' written production, either for free texts (eg, essays, syntheses, notes) or chat conversation, in order for the students to build knowledge. This report presents an overview and a selection of existing models, methods and resources for: 1) the automatic analysis of learner interactions using language technologies or social network analysis (Task 5.1) and 2) the automatic analysis of learner text (Task 5.2). Secondly, a proposition of the tools to be developed for the project ...