scholarly journals HybridQA: A Dataset of Multi-Hop Question Answering over Tabular and Textual Data

2020 ◽  
Author(s):  
Wenhu Chen ◽  
Hanwen Zha ◽  
Zhiyu Chen ◽  
Wenhan Xiong ◽  
Hong Wang ◽  
...  
Keyword(s):  
Question Answering ◽  
Textual Data

scholarly journals A sui generis QA approach using RoBERTa for adverse drug event identification

2021 ◽  
Vol 22 (S11) ◽  
Author(s):  
Harshit Jain ◽  
Nishant Raj ◽  
Suyash Mishra
Keyword(s):  
Question Answering ◽  
Adverse Drug Events ◽  
Short Term Memory ◽  
Domain Adaptation ◽  
Relation Extraction ◽  
Biomedical Literature ◽  
Drug Event ◽  
Textual Data ◽  
Entity Relation Extraction ◽  
End To End

Abstract Background Extraction of adverse drug events from biomedical literature and other textual data is an important component to monitor drug-safety and this has attracted attention of many researchers in healthcare. Existing works are more pivoted around entity-relation extraction using bidirectional long short term memory networks (Bi-LSTM) which does not attain the best feature representations. Results In this paper, we introduce a question answering framework that exploits the robustness, masking and dynamic attention capabilities of RoBERTa by a technique of domain adaptation and attempt to overcome the aforementioned limitations. With formulation of an end-to-end pipeline, our model outperforms the prior work by 9.53% F1-Score. Conclusion An end-to-end pipeline that leverages state of the art transformer architecture in conjunction with QA approach can bolster the performances of entity-relation extraction tasks in the biomedical domain. In particular, we believe our research would be helpful in identification of potential adverse drug reactions in mono as well as combination therapy related textual data.

Reports of the Workshops Held at the 2019 AAAI Conference on Artificial Intelligence

AI Magazine ◽  
2019 ◽  
Vol 40 (3) ◽  
pp. 67-78
Author(s):  
Guy Barash ◽  
Mauricio Castillo-Effen ◽  
Niyati Chhaya ◽  
Peter Clark ◽  
Huáscar Espinoza ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Language Processing ◽  
Cyber Security ◽  
Question Answering ◽  
Intent Recognition ◽  
Affective Content ◽  
Learning Plan ◽  
Dialog System ◽  
Affective Content Analysis ◽  
Games And Simulations

The workshop program of the Association for the Advancement of Artificial Intelligence’s 33rd Conference on Artificial Intelligence (AAAI-19) was held in Honolulu, Hawaii, on Sunday and Monday, January 27–28, 2019. There were fifteen workshops in the program: Affective Content Analysis: Modeling Affect-in-Action, Agile Robotics for Industrial Automation Competition, Artificial Intelligence for Cyber Security, Artificial Intelligence Safety, Dialog System Technology Challenge, Engineering Dependable and Secure Machine Learning Systems, Games and Simulations for Artificial Intelligence, Health Intelligence, Knowledge Extraction from Games, Network Interpretability for Deep Learning, Plan, Activity, and Intent Recognition, Reasoning and Learning for Human-Machine Dialogues, Reasoning for Complex Question Answering, Recommender Systems Meet Natural Language Processing, Reinforcement Learning in Games, and Reproducible AI. This report contains brief summaries of the all the workshops that were held.

Patients’ Perceptions of Caring and Uncaring Nursing Encounters in Inpatient Rehabilitation Settings

2019 ◽  
Vol 21 (2) ◽  
Author(s):  
Joan C Cheruiyot ◽  
Petra Brysiewicz
Keyword(s):  
South Africa ◽  
Content Analysis ◽  
Inpatient Rehabilitation ◽  
Structured Interview ◽  
The Self ◽  
Personal Relationships ◽  
Individual Interviews ◽  
Textual Data ◽  
Interview Guide ◽  
Being There

This study explores and describes caring and uncaring nursing encounters from the perspective of the patients admitted to inpatient rehabilitation settings in South Africa. The researchers used an exploratory descriptive design. A semi-structured interview guide was used to collect data through individual interviews with 17 rehabilitation patients. Content analysis allowed for the analysis of textual data. Five categories of nursing encounters emerged from the analysis: noticing and acting, and being there for you emerged as categories of caring nursing encounters, and being ignored, being a burden, and deliberate punishment emerged as categories of uncaring nursing encounters. Caring nursing encounters make patients feel important and that they are not alone in the rehabilitation journey, while uncaring nursing encounters makes the patients feel unimportant and troublesome to the nurses. Caring nursing encounters give nurses an opportunity to notice and acknowledge the existence of vulnerability in the patients and encourage them to be present at that moment, leading to empowerment. Uncaring nursing encounters result in patients feeling devalued and depersonalised, leading to discouragement. It is recommended that nurses strive to develop personal relationships that promote successful nursing encounters. Further, nurses must strive to minimise the patients’ feelings of guilt and suffering, and to make use of tools, for example the self-perceived scale, to measure this. Nurses must also perform role plays on how to handle difficult patients such as confused, demanding and rude patients in the rehabilitation settings.

Automated question answering in Webclopedia

2002 ◽  
Author(s):  
Ulf Hermjakob ◽  
Eduard Hovy ◽  
Chin-Yew Lin
Keyword(s):  
Question Answering

Pencarian Question-Answer Menggunakan Convolutional Neural Network Pada Topik Agama Berbahasa Indonesia

2018 ◽  
Vol 10 (1) ◽  
pp. 57-64 ◽  
Author(s):  
Rizqa Raaiqa Bintana ◽  
Chastine Fatichah ◽  
Diana Purwitasari
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Question Answering ◽  
Mean Average Precision ◽  
Average Precision ◽  
Community Based ◽  
Network Methods ◽  
The Mean ◽  
Index Terms ◽  
Search Information

Community-based question answering (CQA) is formed to help people who search information that they need through a community. One condition that may occurs in CQA is when people cannot obtain the information that they need, thus they will post a new question. This condition can cause CQA archive increased because of duplicated questions. Therefore, it becomes important problems to find semantically similar questions from CQA archive towards a new question. In this study, we use convolutional neural network methods for semantic modeling of sentence to obtain words that they represent the content of documents and new question. The result for the process of finding the same question semantically to a new question (query) from the question-answer documents archive using the convolutional neural network method, obtained the mean average precision value is 0,422. Whereas by using vector space model, as a comparison, obtained mean average precision value is 0,282. Index Terms—community-based question answering, convolutional neural network, question retrieval

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