The Experts below are selected from a list of 60456 Experts worldwide ranked by ideXlab platform
Magdy Walid - One of the best experts on this subject based on the ideXlab platform.
-
Stance Detection on Social Media: State of the Art and Trends
2021Co-Authors: Aldayel Abeer, Magdy WalidAbstract:Stance detection on social media is an emerging opinion mining paradigm for various social and political applications in which sentiment analysis may be sub-optimal. There has been a growing research interest for developing effective methods for stance detection methods varying among multiple communities including natural language processing, web science, and social computing. This paper surveys the work on stance detection within those communities and situates its usage within current opinion mining techniques in social media. It presents an exhaustive review of stance detection techniques on social media, including the Task Definition, different types of targets in stance detection, features set used, and various machine learning approaches applied. The survey reports state-of-the-art results on the existing benchmark datasets on stance detection, and discusses the most effective approaches. In addition, this study explores the emerging trends and different applications of stance detection on social media. The study concludes by discussing the gaps in the current existing research and highlights the possible future directions for stance detection on social media.Comment: We request withdrawal of this article sincerely. We will re-edit this paper. Please withdraw this article before we finish the new versio
-
Stance Detection on Social Media: State of the Art and Trends
2020Co-Authors: Aldayel Abeer, Magdy WalidAbstract:Stance detection on social media is an emerging opinion mining paradigm for various social and political applications in which sentiment analysis may be sub-optimal. There has been a growing research interest for developing effective methods for stance detection methods varying among multiple communities including natural language processing, web science, and social computing. This paper surveys the work on stance detection within those communities and situates its usage within current opinion mining techniques in social media. It presents an exhaustive review of stance detection techniques on social media, including the Task Definition, different types of targets in stance detection, features set used, and various machine learning approaches applied. The survey reports state-of-the-art results on the existing benchmark datasets on stance detection, and discusses the most effective approaches. In addition, this study explores the emerging trends and different applications of stance detection on social media. The study concludes by discussing the gaps in the current existing research and highlights the possible future directions for stance detection on social media
Alexander V Boukhanovsky - One of the best experts on this subject based on the ideXlab platform.
-
a technology for bigdata analysis Task description using domain specific languages
International Conference on Conceptual Structures, 2014Co-Authors: Sergey V Kovalchuk, Artem V Zakharchuk, Jiaqi Liao, Sergey V Ivanov, Alexander V BoukhanovskyAbstract:Abstract The artic le presents a technology for dynamic knowledge -based building of Do main -Specific Languages (DSL) to describe data-intensive scientific discovery Tasks using BigData technology. The proposed technology supports high level abstract definit ion of analytic and simulat ion parts of the Task as well as integration into the composite scientific solutions. Automatic translation of the abstract Task Definition enables seamless integration of various data sources within single solution.
Jacob J Kantrowitz - One of the best experts on this subject based on the ideXlab platform.
-
Task Definition annotated dataset and supervised natural language processing models for symptom extraction from unstructured clinical notes
Journal of Biomedical Informatics, 2020Co-Authors: Jackson Steinkamp, Wasif Bala, Abhinav Sharma, Jacob J KantrowitzAbstract:Abstract Introduction Machine learning (ML) and natural language processing have great potential to improve information extraction (IE) within electronic medical records (EMRs) for a wide variety of clinical search and summarization tools. Despite ML advancements, clinical adoption of real time IE tools for patient care remains low. Clinically motivated IE Task Definitions, publicly available annotated clinical datasets, and inclusion of subTasks such as coreference resolution and named entity normalization are critical for the development of useful clinical tools. Materials and methods We provide a Task Definition and comprehensive annotation requirements for a clinically motivated symptom extraction Task. Four annotators labeled symptom mentions within 1108 discharge summaries from two public clinical note datasets for the Tasks of named entity recognition, coreference resolution, and named entity normalization; these annotations will be released to the public. Baseline human performance was assessed and two ML models were evaluated on the symptom extraction Task. Results 16,922 symptom mentions were identified within the discharge summaries, with 11,944 symptom instances after coreference resolution and 1255 unique normalized answer forms. Human annotator performance averaged 92.2% F1. Recurrent network model performance was 85.6% F1 (recall 85.8%, precision 85.4%), and Transformer-based model performance was 86.3% F1 (recall 86.6%, precision 86.1%). Our models extracted vague symptoms, acronyms, typographical errors, and grouping statements. The models generalized effectively to a separate clinical note corpus and can run in real time. Conclusion To our knowledge, this dataset will be the largest and most comprehensive publicly released, annotated dataset for clinically motivated symptom extraction, as it includes annotations for named entity recognition, coreference, and normalization for more than 1000 clinical documents. Our neural network models extracted symptoms from unstructured clinical free text at near human performance in real time. In this paper, we present a clinically motivated Task Definition, dataset, and simple supervised natural language processing models to demonstrate the feasibility of building clinically applicable information extraction tools.
Mariefrancine Moens - One of the best experts on this subject based on the ideXlab platform.
-
Moens, Spatial role labeling: Task Definition and annotation scheme
2015Co-Authors: Parisa Kordjamshidi, Martijn Van Otterlo, Mariefrancine MoensAbstract:One of the essential functions of natural language is to talk about spatial relationships between objects. Linguistic constructs can express highly complex, relational structures of objects, spatial relations between them, and patterns of motion through spaces relative to some reference point. Learning how to map this information onto a formal representation from a text is a challenging problem. At present no well-defined framework for automatic spatial information extraction exists that can handle all of these issues. In this paper we introduce the Task of spatial role labeling and propose an annotation scheme that is language-independent and facilitates the application of machine learning techniques. Our framework consists of a set of spatial roles based on the theory of holistic spatial semantics with the intent of covering all aspects of spatial concepts, including both static and dynamic spatial relations. We illustrate our annotation scheme with many examples throughout the paper, and in addition we highlight how to connect to spatial calculi such as region connection calculus and also how our approach fits into related work. 1
-
spatial role labeling Task Definition and annotation scheme
Language Resources and Evaluation, 2010Co-Authors: Parisa Kordjamshidi, Martijn Van Otterlo, Mariefrancine MoensAbstract:One of the essential functions of natural language is to talk about spatial relationships between objects. Linguistic constructs can express highly complex, relational structures of objects, spatial relations between them, and patterns of motion through spaces relative to some reference point. Learning how to map this information onto a formal representation from a text is a challenging problem. At present no well-defined framework for automatic spatial information extraction exists that can handle all of these issues. In this paper we introduce the Task of spatial role labeling and propose an annotation scheme that is language-independent and facilitates the application of machine learning techniques. Our framework consists of a set of spatial roles based on the theory of holistic spatial semantics with the intent of covering all aspects of spatial concepts, including both static and dynamic spatial relations. We illustrate our annotation scheme with many examples throughout the paper, and in addition we highlight how to connect to spatial calculi such as region connection calculus and also how our approach fits into related work.
Shinji Watanabe - One of the best experts on this subject based on the ideXlab platform.
-
The third 'CHiME' speech separation and recognition challenge: Dataset, Task and baselines
2015 IEEE Workshop on Automatic Speech Recognition and Understanding ASRU 2015 - Proceedings, 2016Co-Authors: Jon Barker, Emmanuel Vincent, Ricard Marxer, Shinji WatanabeAbstract:The CHiME challenge series aims to advance far field speech recog- nition technology by promoting research at the interface of signal processing and automatic speech recognition. This paper presents the design and outcomes of the 3rd CHiME Challenge, which targets the performance of automatic speech recognition in a real-world, commercially-motivated scenario: a person talking to a tablet de- vice that has been fitted with a six-channel microphone array. The paper describes the data collection, the Task Definition and the base- line systems for data simulation, enhancement and recognition. The paper then presents an overview of the 26 systems that were submit- ted to the challenge focusing on the strategies that proved to be most successful relative to theMVDRarray processing andDNNacoustic modeling reference system. Challenge findings related to the role of simulated data in system training and evaluation are discussed.
