scholarly journals Vidiam: Corpus-based Development of a Dialogue Manager for Multimodal Question Answering

2011 ◽  
pp. 25-56 ◽  
Author(s):  
Boris van Schooten ◽  
Rieks op den Akker
Keyword(s):  
Question Answering ◽  
Dialogue Manager

Using a Dialogue Manager to Improve Semantic Web Search

2016 ◽  
Vol 12 (1) ◽  
pp. 62-78 ◽  
Author(s):  
Dora Melo ◽  
Irene Pimenta Rodrigues ◽  
Vitor Beires Nogueira
Keyword(s):  
Semantic Web ◽  
Natural Language ◽  
Knowledge Base ◽  
System Performance ◽  
Web Search ◽  
Question Answering ◽  
Similar Question ◽  
User Intentions ◽  
Question Answering Systems ◽  
Dialogue Manager

Question Answering systems that resort to the Semantic Web as a knowledge base can go well beyond the usual matching words in documents and, preferably, find a precise answer, without requiring user help to interpret the documents returned. In this paper, the authors introduce a Dialogue Manager that, through the analysis of the question and the type of expected answer, provides accurate answers to the questions posed in Natural Language. The Dialogue Manager not only represents the semantics of the questions, but also represents the structure of the discourse, including the user intentions and the questions context, adding the ability to deal with multiple answers and providing justified answers. The authors' system performance is evaluated by comparing with similar question answering systems. Although the test suite is slight dimension, the results obtained are very promising.

scholarly journals Semantic Web Search Through Natural Language Dialogues

2018 ◽  
pp. 329-349 ◽  
Author(s):  
Dora Melo ◽  
Irene Pimenta Rodrigues ◽  
Vitor Beires Nogueira
Keyword(s):  
Semantic Web ◽  
Natural Language ◽  
System Performance ◽  
Web Search ◽  
Question Answering ◽  
Small Dimension ◽  
User Intentions ◽  
Question Answering Systems ◽  
Dialogue Manager ◽  
User Intervention

The Semantic Web as a knowledge base gives to the Question Answering systems the capabilities needed to go well beyond the usual word matching in the documents and find a more accurate answer, without needing the user intervention to interpret the documents returned. In this chapter, the authors introduce a Dialogue Manager that, throughout the analysis of the question and the type of expected answer, provides accurate answers to the questions posed in Natural Language. The Dialogue Manager not only represents the semantics of the questions but also represents the structure of the discourse, including the user intentions and the questions' context, adding the ability to deal with multiple answers and providing justified answers. The system performance is evaluated by comparing with similar question answering systems. Although the test suite is of small dimension, the results obtained are very promising.

Using a Dialogue Manager to Improve Semantic Web Search

2018 ◽  
pp. 1166-1183
Author(s):  
Dora Melo ◽  
Irene Pimenta Rodrigues ◽  
Vitor Beires Nogueira
Keyword(s):  
Semantic Web ◽  
Natural Language ◽  
Knowledge Base ◽  
System Performance ◽  
Web Search ◽  
Question Answering ◽  
Similar Question ◽  
User Intentions ◽  
Question Answering Systems ◽  
Dialogue Manager

Question Answering systems that resort to the Semantic Web as a knowledge base can go well beyond the usual matching words in documents and, preferably, find a precise answer, without requiring user help to interpret the documents returned. In this paper, the authors introduce a Dialogue Manager that, through the analysis of the question and the type of expected answer, provides accurate answers to the questions posed in Natural Language. The Dialogue Manager not only represents the semantics of the questions, but also represents the structure of the discourse, including the user intentions and the questions context, adding the ability to deal with multiple answers and providing justified answers. The authors' system performance is evaluated by comparing with similar question answering systems. Although the test suite is slight dimension, the results obtained are very promising.

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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