A Relateness-Based Ranking Method for Knowledge-Based Question Answering

2018 ◽  
pp. 393-400
Author(s):  
Han Ni ◽  
Liansheng Lin ◽  
Ge Xu
Keyword(s):  
Question Answering ◽  
Ranking Method ◽  
Knowledge Based

A Knowledge-Based Health Question Answering System

2017 ◽  
pp. 286-291 ◽  
Author(s):  
Hongxia Liu ◽  
Qingcheng Hu ◽  
Yong Zhang ◽  
Chunxiao Xing ◽  
Ming Sheng
Keyword(s):  
Question Answering ◽  
Question Answering System ◽  
Knowledge Based ◽  
Health Question

LIS4: Lesk Inspired Sense Specific Semantic Similarity using WordNet

2021 ◽  
pp. 2150006
Author(s):  
Saravanakumar Kandasamy ◽  
Aswani Kumar Cherukuri
Keyword(s):  
Information Retrieval ◽  
Natural Language Processing ◽  
Natural Language ◽  
Semantic Similarity ◽  
Language Processing ◽  
Gold Standard ◽  
Question Answering ◽  
Knowledge Based ◽  
Benchmark Datasets ◽  
Processing Information

Semantic similarity quantification between concepts is one of the inevitable parts in domains like Natural Language Processing, Information Retrieval, Question Answering, etc. to understand the text and their relationships better. Last few decades, many measures have been proposed by incorporating various corpus-based and knowledge-based resources. WordNet and Wikipedia are two of the Knowledge-based resources. The contribution of WordNet in the above said domain is enormous due to its richness in defining a word and all of its relationship with others. In this paper, we proposed an approach to quantify the similarity between concepts that exploits the synsets and the gloss definitions of different concepts using WordNet. Our method considers the gloss definitions, contextual words that are helping in defining a word, synsets of contextual word and the confidence of occurrence of a word in other word’s definition for calculating the similarity. The evaluation based on different gold standard benchmark datasets shows the efficiency of our system in comparison with other existing taxonomical and definitional measures.

Deep-gAnswer: A Knowledge Based Question Answering System

2021 ◽  
pp. 434-439
Author(s):  
Yinnian Lin ◽  
Minhao Zhang ◽  
Ruoyu Zhang ◽  
Lei Zou
Keyword(s):  
Question Answering ◽  
Question Answering System ◽  
Knowledge Based

scholarly journals Answering Clinical Questions with Knowledge-Based and Statistical Techniques

2007 ◽  
Vol 33 (1) ◽  
pp. 63-103 ◽  
Author(s):  
Dina Demner-Fushman ◽  
Jimmy Lin
Keyword(s):  
Evidence Based Medicine ◽  
Information Needs ◽  
Semantic Processing ◽  
Question Answering ◽  
Clinical Medicine ◽  
Statistical Techniques ◽  
Evidence Based ◽  
Knowledge Based ◽  
Clinical Questions ◽  
Based Medicine

The combination of recent developments in question-answering research and the availability of unparalleled resources developed specifically for automatic semantic processing of text in the medical domain provides a unique opportunity to explore complex question answering in the domain of clinical medicine. This article presents a system designed to satisfy the information needs of physicians practicing evidence-based medicine. We have developed a series of knowledge extractors, which employ a combination of knowledge-based and statistical techniques, for automatically identifying clinically relevant aspects of MEDLINE abstracts. These extracted elements serve as the input to an algorithm that scores the relevance of citations with respect to structured representations of information needs, in accordance with the principles of evidence-based medicine. Starting with an initial list of citations retrieved by PubMed, our system can bring relevant abstracts into higher ranking positions, and from these abstracts generate responses that directly answer physicians' questions. We describe three separate evaluations: one focused on the accuracy of the knowledge extractors, one conceptualized as a document reranking task, and finally, an evaluation of answers by two physicians. Experiments on a collection of real-world clinical questions show that our approach significantly outperforms the already competitive PubMed baseline.

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.

Knowledge-based question answering by tree-to-sequence learning

2020 ◽  
Vol 372 ◽  
pp. 64-72 ◽  
Author(s):  
Shuguang Zhu ◽  
Xiang Cheng ◽  
Sen Su
Keyword(s):  
Sequence Learning ◽  
Question Answering ◽  
Knowledge Based

scholarly journals Knowledge Graph Construction and Applications for Web Search and Beyond

2019 ◽  
Vol 1 (4) ◽  
pp. 333-349 ◽  
Author(s):  
Peilu Wang ◽  
Hao Jiang ◽  
Jingfang Xu ◽  
Qi Zhang
Keyword(s):  
Search Engine ◽  
Intelligent Systems ◽  
Large Scale ◽  
Web Search ◽  
Question Answering ◽  
Knowledge Graph ◽  
Graph Databases ◽  
Distributed Search ◽  
Knowledge Based ◽  
Dialog System

Knowledge graph (KG) has played an important role in enhancing the performance of many intelligent systems. In this paper, we introduce the solution of building a large-scale multi-source knowledge graph from scratch in Sogou Inc., including its architecture, technical implementation and applications. Unlike previous works that build knowledge graph with graph databases, we build the knowledge graph on top of SogouQdb, a distributed search engine developed by Sogou Web Search Department, which can be easily scaled to support petabytes of data. As a supplement to the search engine, we also introduce a series of models to support inference and graph based querying. Currently, the data of Sogou knowledge graph that are collected from 136 different websites and constantly updated consist of 54 million entities and over 600 million entity links. We also introduce three applications of knowledge graph in Sogou Inc.: entity detection and linking, knowledge based question answering and knowledge based dialog system. These applications have been used in Web search products to help user acquire information more efficiently.

A knowledge based Arabic question answering system (AQAS)

1993 ◽  
Vol 4 (4) ◽  
pp. 21-30 ◽  
Author(s):  
F. A. Mohammed ◽  
Khaled Nasser ◽  
H. M. Harb
Keyword(s):  
Question Answering ◽  
Question Answering System ◽  
Knowledge Based
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