Improved Compare-Aggregate Model for Chinese Document-Based Question Answering

2018 ◽  
pp. 712-720
Author(s):  
Ziliang Wang ◽  
Weijie Bian ◽  
Si Li ◽  
Guang Chen ◽  
Zhiqing Lin
Keyword(s):  
Question Answering ◽  
Aggregate Model

HCA: Hierarchical Compare Aggregate model for question retrieval in community question answering

2020 ◽  
Vol 57 (6) ◽  
pp. 102318
Author(s):  
Mohammad Sadegh Zahedi ◽  
Maseud Rahgozar ◽  
Reza Aghaeizadeh Zoroofi
Keyword(s):  
Question Answering ◽  
Aggregate Model ◽  
Community Question Answering

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.

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

An Efficient Semantic Analysis Technique for the Question Answering Systems

2019 ◽  
Vol 14 (22) ◽  
pp. 8289-8292
Author(s):  
Ibrahim Mahmoud Ibrahim Alturani ◽  
Mohd Pouzi Bin Hamzah
Keyword(s):  
Question Answering ◽  
Semantic Analysis ◽  
Analysis Technique ◽  
Question Answering Systems

Toward a Question Answering Roadmap

2002 ◽  
Author(s):  
Mark T. Maybury
Keyword(s):  
Question Answering
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