164 research outputs found
Computational Sociolinguistics: A Survey
Language is a social phenomenon and variation is inherent to its social
nature. Recently, there has been a surge of interest within the computational
linguistics (CL) community in the social dimension of language. In this article
we present a survey of the emerging field of "Computational Sociolinguistics"
that reflects this increased interest. We aim to provide a comprehensive
overview of CL research on sociolinguistic themes, featuring topics such as the
relation between language and social identity, language use in social
interaction and multilingual communication. Moreover, we demonstrate the
potential for synergy between the research communities involved, by showing how
the large-scale data-driven methods that are widely used in CL can complement
existing sociolinguistic studies, and how sociolinguistics can inform and
challenge the methods and assumptions employed in CL studies. We hope to convey
the possible benefits of a closer collaboration between the two communities and
conclude with a discussion of open challenges.Comment: To appear in Computational Linguistics. Accepted for publication:
18th February, 201
Language Use As a Reflection of Socialization in Online Communities
In this paper we investigate the connection between language and community membership of long time community participants through computational modeling techniques. We report on findings from an analysis of language usage within a popular online discussion forum with participation of thousands of users spanning multiple years. We find community norms of long time participants that are characterized by forum specific jargon and a style that is highly informal and shows familiarity with specific other participants and high emotional involvement in the discussion. We also find quantitative evidence of persistent shifts in language usage towards these norms across users over the course of the first year of community participation. Our observed patterns suggests language stabilization after 8 or 9 months of participation.
Supportive technologies for group discussion in MOOCs
A key hurdle that prevents MOOCs from reaching their transformative potential in terms of making valuable learning experiences available to the masses is providing support for students to make use of the resources they can provide for each other. This paper lays the foundation for meeting this challenge by beginning with a case study and computational modeling of social interaction data. The analysis yields new knowledge that informs design and development of novel, real-time support for building healthy learning communities that foster a high level of engagement and learning. We conclude by suggesting specific areas for potential impact of new technology
Adapting Collaborative Chat for Massive Open Online Courses: Lessons Learned
Abstract. In this paper we explore how to import intelligent support for group learning that has been demonstrated as effective in classroom instruction into a Massive Open Online Course (MOOC) context. The Bazaar agent architecture paired with an innovative Lobby tool to enable coordination for synchronous reflection exercises provides a technical foundation for our work. We describe lessons learned, directions for future work, and offer pointers to resources for other researchers interested in computer supported collaborative learning in MOOCs
Robust Knowledge Graph Completion with Stacked Convolutions and a Student Re-Ranking Network
Knowledge Graph (KG) completion research usually focuses on densely connected
benchmark datasets that are not representative of real KGs. We curate two KG
datasets that include biomedical and encyclopedic knowledge and use an existing
commonsense KG dataset to explore KG completion in the more realistic setting
where dense connectivity is not guaranteed. We develop a deep convolutional
network that utilizes textual entity representations and demonstrate that our
model outperforms recent KG completion methods in this challenging setting. We
find that our model's performance improvements stem primarily from its
robustness to sparsity. We then distill the knowledge from the convolutional
network into a student network that re-ranks promising candidate entities. This
re-ranking stage leads to further improvements in performance and demonstrates
the effectiveness of entity re-ranking for KG completion.Comment: The Joint Conference of the 59th Annual Meeting of the Association
for Computational Linguistics and the 11th International Joint Conference on
Natural Language Processing (ACL-IJCNLP 2021
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