Research on Large-Scale Knowledge Base Management Frameworks for Open-Domain Question Answering Systems

2021 ◽  
pp. 87-92
Author(s):  
Dat Tien Nguyen ◽  
Hao Duc Do
Keyword(s):  
Knowledge Base ◽  
Large Scale ◽  
Question Answering ◽  
Open Domain ◽  
Question Answering Systems ◽  
Base Management ◽  
Knowledge Base Management

Predicting Lexical Answer Types in Open Domain QA

2012 ◽  
Vol 8 (3) ◽  
pp. 74-88 ◽  
Author(s):  
Alfio Massimiliano Gliozzo ◽  
Aditya Kalyanpur
Keyword(s):  
Knowledge Acquisition ◽  
Knowledge Base ◽  
Large Scale ◽  
Question Answering ◽  
General Purpose ◽  
Open Domain ◽  
Lexical Knowledge ◽  
Research Challenge ◽  
Lexical Knowledge Base

Automatic open-domain Question Answering has been a long standing research challenge in the AI community. IBM Research undertook this challenge with the design of the DeepQA architecture and the implementation of Watson. This paper addresses a specific subtask of Deep QA, consisting of predicting the Lexical Answer Type (LAT) of a question. Our approach is completely unsupervised and is based on PRISMATIC, a large-scale lexical knowledge base automatically extracted from a Web corpus. Experiments on the Jeopardy! data shows that it is possible to correctly predict the LAT in a substantial number of questions. This approach can be used for general purpose knowledge acquisition tasks such as frame induction from text.

The TREC question answering track

2001 ◽  
Vol 7 (4) ◽  
pp. 361-378 ◽  
Author(s):  
ELLEN M. VOORHEES
Keyword(s):  
Large Scale ◽  
Question Answering ◽  
Text Retrieval ◽  
Scale Evaluation ◽  
Retrieval Systems ◽  
Question Answering Systems

The Text REtrieval Conference (TREC) question answering track is an effort to bring the benefits of large-scale evaluation to bear on a question answering (QA) task. The track has run twice so far, first in TREC-8 and again in TREC-9. In each case, the goal was to retrieve small snippets of text that contain the actual answer to a question rather than the document lists traditionally returned by text retrieval systems. The best performing systems were able to answer about 70% of the questions in TREC-8 and about 65% of the questions in TREC-9. While the 65% score is a slightly worse result than the TREC-8 scores in absolute terms, it represents a very significant improvement in question answering systems. The TREC-9 task was considerably harder than the TREC-8 task because TREC-9 used actual users’ questions while TREC-8 used questions constructed for the track. Future tracks will continue to challenge the QA community with more difficult, and more realistic, question answering tasks.

scholarly journals Recent Trends in Deep Learning Based Open-Domain Textual Question Answering Systems

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 94341-94356
Author(s):  
Zhen Huang ◽  
Shiyi Xu ◽  
Minghao Hu ◽  
Xinyi Wang ◽  
Jinyan Qiu ◽  
...  
Keyword(s):  
Deep Learning ◽  
Question Answering ◽  
Open Domain ◽  
Question Answering Systems ◽  
Recent Trends
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