Semantic Reasoning of Question Answering Over Heroes of the Marshes Based on Concept Knowledge Tree

2017 ◽  
Author(s):  
Ziqi Lin ◽  
Wancheng Ni ◽  
Haidong Zhang ◽  
Meijing Zhao ◽  
Yu Liu
Keyword(s):  
Question Answering ◽  
Semantic Reasoning ◽  
Concept Knowledge

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

REFLECTION OF THE CONCEPT “KNOWLEDGE” USED IN PHRASEOLOGICAL UNITS IN ENGLISH AND KYRGYZ WORLD VIEW

2019 ◽  
pp. 9-11
Author(s):  
Zhusupbek kyzy Aida
Keyword(s):  
Comparative Analysis ◽  
Knowledge Translation ◽  
Cultural Diversity ◽  
Key Words ◽  
World View ◽  
Concept Knowledge ◽  
Key Concepts

Abstract. Thе article aims at researching the concept “knowledge” used in phraseological units which is one of the key concepts in Кyrgyz and English world view. The comparative analysis of the concept “knowledge” in Kyrgyz and English linguistic world view reveals differences and similarities in its content. In addition, the research also shows that Kyrgyz phraseological units differ a lot from the English due to several particular features like cultural diversity, language peculiarities and linguistic world view. Various examples related to the concept “knowledge” are used demonstrating the difficulties in translation and the differences in meaning of the concept “knowledge” in phraseological units in Kyrgyz and English world view. Key words: concept, linguistic world view, phraseological units, idioms, phrase, proverbs and sayings, phraseology, equivalents, knowledge, translation.

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