Answer Extraction in Technical Domains

2002 ◽  
pp. 360-369 ◽  
Author(s):  
Fabio Rinaldi ◽  
Michael Hess ◽  
Diego Mollá ◽  
Rolf Schwitter ◽  
James Dowdall ◽  
...  
Keyword(s):  
Answer Extraction

An Answer Extraction Algorithm Based on Syntax Structure Feature Parsing and Classification

2009 ◽  
Vol 31 (4) ◽  
pp. 662-676 ◽  
Author(s):  
Bao-Shun HU ◽  
Da-Ling WANG ◽  
Ge YU ◽  
Ting MA
Keyword(s):  
Structure Feature ◽  
Answer Extraction ◽  
Extraction Algorithm

Entity answer extraction of web table

2010 ◽  
Author(s):  
Yangbo Xu ◽  
Zhengtao Yu ◽  
Cunli Mao ◽  
Yasheng Wang ◽  
Jianyi Guo
Keyword(s):  
Answer Extraction

A Persian Medical Question Answering System

2020 ◽  
Vol 29 (06) ◽  
pp. 2050019
Author(s):  
Hadi Veisi ◽  
Hamed Fakour Shandi
Keyword(s):  
Natural Language ◽  
Language Processing ◽  
Question Answering ◽  
Document Retrieval ◽  
Main Concept ◽  
Question Answering System ◽  
Detection Algorithms ◽  
Persian Language ◽  
Part Of Speech ◽  
Answer Extraction

A question answering system is a type of information retrieval that takes a question from a user in natural language as the input and returns the best answer to it as the output. In this paper, a medical question answering system in the Persian language is designed and implemented. During this research, a dataset of diseases and drugs is collected and structured. The proposed system includes three main modules: question processing, document retrieval, and answer extraction. For the question processing module, a sequential architecture is designed which retrieves the main concept of a question by using different components. In these components, rule-based methods, natural language processing, and dictionary-based techniques are used. In the document retrieval module, the documents are indexed and searched using the Lucene library. The retrieved documents are ranked using similarity detection algorithms and the highest-ranked document is selected to be used by the answer extraction module. This module is responsible for extracting the most relevant section of the text in the retrieved document. During this research, different customized language processing tools such as part of speech tagger and lemmatizer are also developed for Persian. Evaluation results show that this system performs well for answering different questions about diseases and drugs. The accuracy of the system for 500 sample questions is 83.6%.

Question Answering

2012 ◽  
Author(s):  
Sanda Harabagiu ◽  
Dan Moldovan
Keyword(s):  
Language Processing ◽  
Question Answering ◽  
Document Retrieval ◽  
Semantic Features ◽  
Knowledge Based ◽  
Answer Extraction ◽  
Small Set ◽  
Processing Module ◽  
On Line ◽  
Processing Techniques

Textual Question Answering (QA) identifies the answer to a question in large collections of on-line documents. By providing a small set of exact answers to questions, QA takes a step closer to information retrieval rather than document retrieval. A QA system comprises three modules: a question-processing module, a document-processing module, and an answer extraction and formulation module. Questions may be asked about any topic, in contrast with Information Extraction (IE), which identifies textual information relevant only to a predefined set of events and entities. The natural language processing (NLP) techniques used in open-domain QA systems may range from simple lexical and semantic disambiguation of question stems to complex processing that combines syntactic and semantic features of the questions with pragmatic information derived from the context of candidate answers. This article reviews current research in integrating knowledge-based NLP methods with shallow processing techniques for QA.

Question Classification and Answer Extraction for Developing a Cooking QA System

2020 ◽  
Vol 24 (2) ◽  
Author(s):  
Abdullah Faiz Ur Rahman Khilji ◽  
Riyanka Manna ◽  
Sahinur Rahman Laskar ◽  
Partha Pakray ◽  
Dipankar Das ◽  
...  
Keyword(s):  
Answer Extraction ◽  
Question Classification
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