Question Answering System

From GM-RKB
Jump to: navigation, search

A Question Answering (QA) System is an dialog system (that applies a question answering algorithm to solve a question answering task.



References

2018a

2018b

2017

Many of the classic algorithms for question-answering consist of multiple stages, such as question processing, answer type classification and answer selection, and there is a lot of engineering involved in each step [4].

Many of the modern QA algorithms learn to embed both question and answer into a low-dimensional space and select the answer by finding the similarity of their features. Due to tremendous performance of deep neural network for many problems in recent years, there have been many works using deep learning models for designing question-answering systems. In [5], Iyyer introduced a recursive neural network architecture for question answering. In [6], Weston proposed a memory network model for question answering, where the proposed network can reason using a long-term memory component. In [7], Bordes addressed the problem of question-answering with weakly supervised embedding models. In [8], a convolutional neural network (CNN) based model is proposed for modeling sentences. This CNN model is used by Feng [9] for answer selection in QA systems. Long-short term memory models (LSTM) [10] has also been used a lot for different problems in NLP. Tan [11], explored the applications of LSTM based models for answer selection.

2016a

2016b

2015a

2015b

2015c

2014a

2014b

2014c

2014d

2008a

2008b

2005

2004

2003

2002

1999

1997

1977

  • (Woods & Kaplan, 1977) ⇒ William A. Woods, and R. Kaplan (1977). “Lunar rocks in natural English: Explorations in natural language question answering”, Linguistic structures processing, 5. 5: 521569

1961