Evaluating retrieval performance for Japanese question answering

2003 ◽  
Author(s):  
Tetsuya Sakai ◽  
Tomoharu Kokubu
Keyword(s):  
Question Answering ◽  
Retrieval Performance

Answering engineers' questions using semantic annotations

2007 ◽  
Vol 21 (2) ◽  
pp. 155-171 ◽  
Author(s):  
SANGHEE KIM ◽  
ROB H. BRACEWELL ◽  
KEN M. WALLACE
Keyword(s):  
Question Answering ◽  
Structure Theory ◽  
Preliminary Evaluation ◽  
Retrieval Performance ◽  
Rhetorical Structure ◽  
Semantic Annotations ◽  
Recent Trends ◽  
Multiple Documents

Question–answering (QA) systems have proven to be helpful, especially to those who feel uncomfortable entering keywords, sometimes extended with search symbols such as +, *, and so forth. In developing such systems, the main focus has been on the enhanced retrieval performance of searches, and recent trends in QA systems center on the extraction of exact answers. However, when their usability was evaluated, some users indicated that they found it difficult to accept the answers because of the absence of supporting context and rationale. Current approaches to address this problem include providing answers with linking paragraphs or with summarizing extensions. Both methods are believed to be sufficient to answer questions seeking the names of objects or quantities that have only a single answer. However, neither method addresses the situation when an answer requires the comparison and integration of information appearing in multiple documents or in several places in a single document. This paper argues that coherent answer generation is crucial for such questions, and that the key to this coherence is to analyze texts to a level beyond sentence annotations. To demonstrate this idea, a prototype has been developed based on rhetorical structure theory, and a preliminary evaluation has been carried out. The evaluation indicates that users prefer to see the extended answers that can be generated using such semantic annotations, provided that additional context and rationale information are made available.

scholarly journals An Intelligent Question Answering Platform for Graduate Enrollment

2021 ◽  
Author(s):  
Mengyuan Zhang ◽  
Yuting Wang ◽  
Jianxia Chen ◽  
Yu Cheng
Keyword(s):  
Question Answering ◽  
Short Term Memory ◽  
Retrieval Model ◽  
Retrieval Performance ◽  
Short Term ◽  
Data Sparseness ◽  
Proposed Model ◽  
Graduate Enrollment ◽  
Long Short Term Memory

To enhance the competitiveness of colleges and universities in the graduate enrollment and reduce the pressure on candidates for examination and consultation, it is necessary and practically significant to develop an intelligent Q&A platform, which can understand and analyze users' semantics and accurately return the information they need. However, there are problems such as the low volume and low quality of the corpus in the graduate enrollment, this paper develops a question answering platform based on a novel retrieval model including density-based logistic regression and the combination of convolutional neural networks and bidirectional long short-term memory. The experimental results show that the proposed model can effectively alleviate the problem of data sparseness and greatly improve the accuracy of the retrieval performance for the graduate enrollment.

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

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