scholarly journals Examining the role of statistical and linguistic knowledge sources in a general-knowledge question-answering system

2000 ◽  
Author(s):  
Claire Cardie ◽  
Vincent Ng ◽  
David Pierce ◽  
Chris Buckley
Keyword(s):  
Question Answering ◽  
Linguistic Knowledge ◽  
General Knowledge ◽  
Knowledge Sources ◽  
Question Answering System ◽  
Knowledge Question ◽  
General Knowledge Question

scholarly journals A Task-Based Evaluation of French Morphological Resources and Tools

2011 ◽  
Vol 5 ◽  
Author(s):  
Delphine Bernhard ◽  
Bruno Cartoni ◽  
Delphine Tribout
Keyword(s):  
Natural Language Processing ◽  
Language Processing ◽  
Question Answering ◽  
Linguistic Knowledge ◽  
Morphological Knowledge ◽  
Application Performance ◽  
Language Technology ◽  
Technology Applications ◽  
Morphological Processes

Morphology is a key component for many Language Technology applications. However, morphological relations, especially those relying on the derivation and compounding processes, are often addressed in a superficial manner. In this article, we focus on assessing the relevance of deep and motivated morphological knowledge in Natural Language Processing applications. We first describe an annotation experiment whose goal is to evaluate the role of morphology for one task, namely Question Answering (QA). We then highlight the kind of linguistic knowledge that is necessary for this particular task and propose a qualitative analysis of morphological phenomena in order to identify the morphological processes that are most relevant. Based on this study, we perform an intrinsic evaluation of existing tools and resources for French morphology, in order to quantify their coverage. Our conclusions provide helpful insights for using and building appropriate morphological resources and tools that could have a significant impact on the application performance.

Intelligent pig raising knowledge question‐answering system based on neural network schemes

2021 ◽  
Author(s):  
Hsu‐Yang Kung ◽  
Ren‐Wu Yu ◽  
Chi‐Hua Chen ◽  
Chan‐Wei Tsai ◽  
Chia‐Yu Lin
Keyword(s):  
Neural Network ◽  
Question Answering ◽  
Question Answering System ◽  
Knowledge Question

scholarly journals What Does My QA Model Know? Devising Controlled Probes Using Expert Knowledge

2020 ◽  
Vol 8 ◽  
pp. 572-588
Author(s):  
Kyle Richardson ◽  
Ashish Sabharwal
Keyword(s):  
Question Answering ◽  
Expert Knowledge ◽  
Comprehensive Evaluation ◽  
Multiple Choice ◽  
Linguistic Knowledge ◽  
Knowledge Sources ◽  
Open Domain ◽  
Complex Reasoning ◽  
Systematic Control ◽  
Taxonomic Reasoning

Open-domain question answering (QA) involves many knowledge and reasoning challenges, but are successful QA models actually learning such knowledge when trained on benchmark QA tasks? We investigate this via several new diagnostic tasks probing whether multiple-choice QA models know definitions and taxonomic reasoning—two skills widespread in existing benchmarks and fundamental to more complex reasoning. We introduce a methodology for automatically building probe datasets from expert knowledge sources, allowing for systematic control and a comprehensive evaluation. We include ways to carefully control for artifacts that may arise during this process. Our evaluation confirms that transformer-based multiple-choice QA models are already predisposed to recognize certain types of structural linguistic knowledge. However, it also reveals a more nuanced picture: their performance notably degrades even with a slight increase in the number of “hops” in the underlying taxonomic hierarchy, and with more challenging distractor candidates. Further, existing models are far from perfect when assessed at the level of clusters of semantically connected probes, such as all hypernym questions about a single concept.

A question answering system in hadith using linguistic knowledge

2020 ◽  
Vol 60 ◽  
pp. 101023 ◽  
Author(s):  
Asad Abdi ◽  
Shafaatunnur Hasan ◽  
Mohammad Arshi ◽  
Siti Mariyam Shamsuddin ◽  
Norisma Idris
Keyword(s):  
Question Answering ◽  
Linguistic Knowledge ◽  
Question Answering System

scholarly journals Modelling and Implementation of a Knowledge Question-answering System for Product Quality Problem Based on Knowledge Graph

2021 ◽  
Vol 2068 (1) ◽  
pp. 012051
Author(s):  
Hanxu Liu ◽  
Fangxu Dong ◽  
Meiqing Wang ◽  
Qiu Lin
Keyword(s):  
Product Quality ◽  
Template Matching ◽  
Process Model ◽  
Question Answering ◽  
Knowledge Graph ◽  
Knowledge Retrieval ◽  
Semantic Classification ◽  
Question Answering System ◽  
Quality Problem ◽  
Knowledge Question

Abstract Aiming at the problem of difficulty in understanding the semantics of the problem in the traditional quality problem management system, the knowledge retrieval technology of product quality problem based on the knowledge graph is carried out. The process model for knowledge retrieval of quality problem based on semantic templates is constructed. A domain corpus is built, which consisting of thousands of quality problem handling records. The TF-IDF (Term Frequency-inverse Document Frequency) algorithm was used to extracted the vocabulary from the quality problem analysis reports. A natural language question semantic classification process model based on Naive Bayes classifier is established to improve the accuracy of semantic template matching. On the basis of theoretical study, a quality problem knowledge question-answering system-QQ-KQAS based on knowledge graph is developed, and the effectiveness of the proposed method is verified through examples.

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