Traian Rebedea

A polyphonic model and system for inter-animation analysis in chat conversations with multiple participants

Discourse in instant messenger conversations (chats) with multiple participants is often composed of several intertwining threads. Some chat environments for Computer-Supported Collaborative Learning (CSCL) support and encourage the existence of parallel threads by providing explicit referencing facilities. The paper proposes a discourse model for such chats, based on Mikhail Bakhtins dialogic theory. It considers that multiple voices (which do not limit to the participants) inter-animate, sometimes in a polyphonic, counterpointal way. An implemented system is also presented, which analyzes such chat logs for detecting additional, implicit links among utterances and threads and, more important for CSCL, for detecting the involvement (inter-animation) of the participants in problem solving. The system begins with a NLP pipe and concludes with inter-animation identification in order to generate feedback and to propose grades for the learners.

Polyphonic Inter-Animation of Voices in VMT

This chapter introduces a theoretical framework for analyzing collaborative problem solving in chats, based on the concept of polyphony and Bakhtin’s theory of dialog. Polyphony, a notion taken from music theory, may be considered as a general model for interaction and creativity by a group of people (“voices,” in an extended sense) following patterns of counterpoint. As Bakhtin emphasized, polyphony may occur in texts; we will show that it can occur in problem-solving chat texts. One of the features of polyphonic music is its potential development of complex architectures starting from a given theme. Polyphonic structuring of dialogs may transform the interaction into a “thinking device”: Different voices jointly construct a melody (story or solution), sometimes adopting different positions and then generating, idenepsying or solving dissonances (unsound, rickety stories or solutions). Polyphony consists of several “horizontal,” longitudinal melody lines that are “vertically,” transversally integrated. Similarly, in chats, the continuations of utterances are tied together over time providing a melodic line. Simultaneously, they are coordinated with the utterances of others, maintaining the integration toward unity across various themes and variations that sometimes can introduce differences. This chapter also proposes software tools for the visualization of the polyphonic weaving in chats. These tools idenepsy and visualize the explicit and implicit links among utterances, and may determine or visualize the contributions of each participant in a chat.

Automatic assessment of collaborative chat conversations with PolyCAFe

The wider acceptance and usage of instant messaging (chat) represents one of the consequences of undertaking Computer-Supported Collaborative Learning (CSCL) practices in formal education settings. However, the difficulty of analyzing these textual artifacts of learners in order to offer them feedback represents a serious problem in further extending the usage of chat conversations. PolyCAFe is a system that was designed to support the tutors and to provide automatic feedback for the learners engaged in collaborative chat conversations and discussion forums. The architecture of the system is presented by focusing on two key components: the assessment of the utterances and of the collaborative discourse. PolyCAFes effectiveness has been proved in a validation experiment with students and tutors from a University course. The main findings from this trial, together with the conclusions of domain experts verifying the accuracy of the assessment provided by PolyCAFe, are also analyzed and commented in detail.

PolyCAFe—automatic support for the polyphonic analysis of CSCL chats

Chat conversations and other types of online communication environments are widely used within CSCL educational scenarios. However, there is a lack of theoretical and methodological background for the analysis of collaboration. Manual assessing of non-moderated chat discussions is difficult and time-consuming, having as a consequence that learning scenarios have not been widely adopted, neither in formal education nor in informal learning contexts. An analysis method of collaboration and individual participation is needed. Moreover, computer-support tools for the analysis and assessment of these conversations are required. In this paper, we start from the “polyphonic framework” as a theoretical foundation suitable for the analysis of textual and even gestural interactions within collaborative groups. This framework exploits the notions of dialogism, inter-animation and polyphony for assessing interactions between participants. The basics of the polyphonic framework are discussed and a systematic presentation of the polyphonic analysis method is included. Then, we present the PolyCAFe system, which provides tools that support the polyphonic analysis of chat conversations and online discussion forums of small groups of learners. Natural Language Processing (NLP) is used in order to identify topics, semantic similarities and links between utterances. The detected links are then used to build a graph of utterances, which forms the central element for the polyphonic analysis and for providing automatic feedback and support to both tutors and learners. Social Network Analysis is used for computing quantitative measures for the interactions between participants. Two evaluation experiments have been undertaken with PolyCAFe. Learners find the system useful and efficient. In addition to these advantages, tutors reflecting on the conversation can provide quicker manual feedback.

Overview and preliminary results of using polyCAFe for collaboration analysis and feedback generation

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.

A System for the Automatic Analysis of Computer-Supported Collaborative Learning Chats

The paper presents a system for helping the analysis of Computer-Supported Collaborative Learning chat (instant messenger) sessions, starting from a polyphonic model of the discourse inspired by the dialogistics of Bakhtin. Some theoretical basics of the model are presented, followed by implementation details and validation results.

Sentence selection with neural networks using string kernels

Abstract In recent years, there have been several advancements in question answering systems. These were achieved both due to the availability of a greater number of datasets, some of them significantly larger in size than any of the existing corpora, and to the recent advancements in deep learning for text classification. In this paper, we explore the improvements achieved by employing neural networks using the features computed by a string kernel for sentence/answer selection. We have validated this approach using two different standard corpora used as benchmarks in question answering and we have found a significant improvement over string kernels and other unsupervised methods for sentence selection.

Continuous User Authentication Using Machine Learning on Touch Dynamics

In the context of constantly evolving carry-on technology and its increasing accessibility, namely smart-phones and tablets, a greater need for reliable authentication means comes into sight. The current study offers an alternative solution of uninterrupted testing towards verifying user legitimacy. A continuously collected dataset of 41 users’ touch-screen inputs provides a good starting point into modeling each user’s behavior and later differentiate among users. We introduce a system capable of processing features based on raw data extracted from user-screen interactions and attempting to assign each gesture to its originator. Achieving an accuracy of over 83 %, we prove that this type of authentication system is feasible and that it can be further integrated as a continuous way of disclosing intruders within given mobile applications.

Detecting and Describing Historical Periods in a Large Corpora

Many historic periods (or events) are remembered by slogans, expressions or words that are strongly linked to them. Educated people are also able to determine whether a particular word or expression is related to a specific period in human history. The present paper aims to establish correlations between significant historic periods (or events) and the texts written in that period. In order to achieve this, we have developed a system that automatically links words (and topics discovered using Latent Dirichlet Allocation) to periods of time in the recent history. For this analysis to be relevant and conclusive, it must be undertaken on a representative set of texts written throughout history. To this end, instead of relying on manually selected texts, the Google Books Ngram corpus has been chosen as a basis for the analysis. Although it provides only word n-gram statistics for the texts written in a given year, the resulting time series can be used to provide insights about the most important periods and events in recent history, by automatically linking them with specific keywords or even LDA topics.

Autonomous news clustering and classification for an intelligent web portal

The paper presents an autonomous text classification module for a news web portal for the Romanian language. Statistical natural language processing techniques are combined in order to achieve a completely autonomous functionality of the portal. The news items are automatically collected from a large number of news sources using web syndication. Afterward, machine-learning techniques are used for achieving an automatic classification of the news stream. Firstly, the items are clustered using an agglomerative algorithm and the resulting groups correspond to the main news topics. Thus, more information about each of the main topics is acquired from various news sources. Secondly, text classification algorithms are applied to automatically label each cluster of news items in a predetermined number of classes. More than a thousand news items were employed for both the training and the evaluation of the classifiers. The paper presents a complete comparison of the results obtained for each method.