4,981 research outputs found
Introduction to the special issue on cross-language algorithms and applications
With the increasingly global nature of our everyday interactions, the need for multilingual technologies to support efficient and efective information access and communication cannot be overemphasized. Computational modeling of language has been the focus of
Natural Language Processing, a subdiscipline of Artificial Intelligence. One of the current challenges for this discipline is to design methodologies and algorithms that are cross-language in order to create multilingual technologies rapidly. The goal of this JAIR special
issue on Cross-Language Algorithms and Applications (CLAA) is to present leading research in this area, with emphasis on developing unifying themes that could lead to the development of the science of multi- and cross-lingualism. In this introduction, we provide the reader with the motivation for this special issue and summarize the contributions of the papers that have been included. The selected papers cover a broad range of cross-lingual technologies including machine translation, domain and language adaptation for sentiment
analysis, cross-language lexical resources, dependency parsing, information retrieval and knowledge representation. We anticipate that this special issue will serve as an invaluable resource for researchers interested in topics of cross-lingual natural language processing.Postprint (published version
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
Transfer Learning for Low-Resource Sentiment Analysis
Sentiment analysis is the process of identifying and extracting subjective
information from text. Despite the advances to employ cross-lingual approaches
in an automatic way, the implementation and evaluation of sentiment analysis
systems require language-specific data to consider various sociocultural and
linguistic peculiarities. In this paper, the collection and annotation of a
dataset are described for sentiment analysis of Central Kurdish. We explore a
few classical machine learning and neural network-based techniques for this
task. Additionally, we employ an approach in transfer learning to leverage
pretrained models for data augmentation. We demonstrate that data augmentation
achieves a high F score and accuracy despite the difficulty of the task.Comment: 14 pages - under review at ACM TALLI
Social media mining under the COVID-19 context: Progress, challenges, and opportunities
Social media platforms allow users worldwide to create and share information, forging vast sensing networks that
allow information on certain topics to be collected, stored, mined, and analyzed in a rapid manner. During the
COVID-19 pandemic, extensive social media mining efforts have been undertaken to tackle COVID-19 challenges
from various perspectives. This review summarizes the progress of social media data mining studies in the
COVID-19 contexts and categorizes them into six major domains, including early warning and detection, human
mobility monitoring, communication and information conveying, public attitudes and emotions, infodemic and
misinformation, and hatred and violence. We further document essential features of publicly available COVID-19
related social media data archives that will benefit research communities in conducting replicable and repro�ducible studies. In addition, we discuss seven challenges in social media analytics associated with their potential
impacts on derived COVID-19 findings, followed by our visions for the possible paths forward in regard to social
media-based COVID-19 investigations. This review serves as a valuable reference that recaps social media mining
efforts in COVID-19 related studies and provides future directions along which the information harnessed from
social media can be used to address public health emergencies
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