Newcomer Integration in Online Knowledge Building Communities: Automated Dialogue Analysis in Integrative vs. Non-Integrative Blogger Communities

Online knowledge building communities (OKBC) reunite participants engaged in collaborative discourse. OKBCs can be made „smart“ by adding tools that predict how likely an OKBC is to integrate newcomers in existing dialogues and socio-cognitive structures. Starting from Bakhtin’s dialogical approach and polyphony theory, and building on the concept of inter- animation of voices, this study explores the relationship between newcomer integration and dialogue quality in OKBCs. The automated analysis tool “Important Moments” was employed to compare two dialogues, from an integrative and from a non-integrative blog-based OKBC. In the former, the concepts, lexical chains and inter-animation moments occurred more frequently than in the latter. Also, newcomer comments were linked to less lexical chains in the integrative community than in the non-integrative OKBC. These findings suggest close relationships between dialogue quality and newcomer integration, which can be used for designing smart OKBCs

Improving Intent Classification Using Unlabeled Data from Large Corpora

Intent classification is a central component of a Natural Language Understanding (NLU) pipeline for conversational agents. The quality of such a component depends on the quality of the training data, however, for many conversational scenarios, the data might be scarce; in these scenarios, data augmentation techniques are used. Having general data augmentation methods that can generalize to many datasets is highly desirable. The work presented in this paper is centered around two main components. First, we explore the influence of various feature vectors on the task of intent classification using RASA’s text classification capabilities. The second part of this work consists of a generic method for efficiently augmenting textual corpora using large datasets of unlabeled data. The proposed method is able to efficiently mine for examples similar to the ones that are already present in standard, natural language corpora. The experimental results show that using our corpus augmentation methods enables an increase in text classification accuracy in few-shot settings. Particularly, the gains in accuracy raise up to 16% when the number of labeled examples is very low (e.g., two examples). We believe that our method is important for any Natural Language Processing (NLP) or NLU task in which labeled training data are scarce or expensive to obtain. Lastly, we give some insights into future work, which aims at combining our proposed method with a semi-supervised learning approach

MALAPROPISMS DETECTION AND CORRECTION USING A PARONYMS DICTIONARY, A SEARCH ENGINE AND WORDNET

Abstract: This paper presents a method for the automatic detection and correction of malapropism errors found in documents using the WordNet lexical database, a search engine (Google) and a paronyms dictionary. The malapropisms detection is based on the evaluation of the cohesion of the local context using the search engine, while the correction is done using the whole text cohesion evaluated in terms of lexical chains built using the linguistic ontology. The correction candidates, which are taken from the paronyms dictionary, are evaluated versus the local and the whole text cohesion in order to find the best candidate that is chosen for replacement. The testing methods of the application are presented, along with the obtained results

Advanced Strategies for Monitoring Water Consumption Patterns in Households Based on IoT and Machine Learning

Water resource management represents a fundamental aspect of a modern society. Urban areas present multiple challenges requiring complex solutions, which include multidomain approaches related to the integration of advanced technologies. Water consumption monitoring applications play a significant role in increasing awareness, while machine learning has been proven for the design of intelligent solutions in this field. This paper presents an approach for monitoring and predicting water consumption from the most important water outlets in a household based on a proposed IoT solution. Data processing pipelines were defined, including K-means clustering and evaluation metrics, extracting consumption events, and training classification methods for predicting consumption sources. Continuous water consumption monitoring offers multiple benefits toward improving decision support by combining modern processing techniques, algorithms, and methods

Advanced Techniques for Monitoring and Management of Urban Water Infrastructures—An Overview

Water supply systems are essential for a modern society. This article presents an overview of the latest research related to information and communication technology systems for water resource monitoring, control and management. The main objective of our review is to show how emerging technologies offer support for smart administration of water infrastructures. The paper covers research results related to smart cities, smart water monitoring, big data, data analysis and decision support. Our evaluation reveals that there are many possible solutions generated through combinations of advanced methods. Emerging technologies open new possibilities for including new functionalities such as social involvement in water resource management. This review offers support for researchers in the area of water monitoring and management to identify useful models and technologies for designing better solutions