An English language question answering system for a large relational database

Since early days Question Answering (QA) has been an intuitive way of understanding the concept by humans. Considering its inevitable importance it has been introduced to children from very early age and they are promoted to ask more and more questions. With the progress in Machine Learning & Ontological semantics, Natural Language Question Answering (NLQA) has gained more popularity in recent years. In this paper QUASE (QUestion Answering System for Education) question answering system for answering natural language questions has been proposed which help to find answer for any given question in a closed domain containing finite set of documents. Th e QA s y st em m a inl y focuses on factoid questions. QUASE has used Question Taxonomy for Question Classification. Several Natural Language Processing techniques like Part of Speech (POS) tagging, Lemmatization, Sentence Tokenization have been applied for document processing to make search better and faster. DBPedia ontology has been used to validate the candidate answers. By application of this system the learners can gain knowledge on their own by getting precise answers to their questions asked in natural language instead of getting back merely a list of documents. The precision, recall and F measure metrics have been taken into account to evaluate the performance of answer type evaluation. The metric Mean Reciprocal Rank has been considered to evaluate the performance of QA system. Our experiment has shown significant improvement in classifying the questions in to correct answer types over other methods with approximately 91% accuracy and also providing better performance as a QA system in closed domain search.

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Quantum, a French/English Cross-Language Question Answering System

Comparative Evaluation of Multilingual Information Access Systems - Lecture Notes in Computer Science ◽

10.1007/978-3-540-30222-3_53 ◽

2004 ◽

pp. 549-558 ◽

Cited By ~ 2

Author(s):

Luc Plamondon ◽

George Foster

Keyword(s):

Question Answering ◽

Question Answering System ◽

Cross Language ◽

Language Question

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SPARQL QUERY GENERATION FOR COMPLEX QUESTION ANSWERING WITH BERT AND BILSTM-BASED MODEL

Computational Linguistics and Intellectual Technologies ◽

10.28995/2075-7182-2020-19-270-282 ◽

2020 ◽

Author(s):

D. A. Evseev ◽

◽

M. Yu. Arkhipov ◽

Keyword(s):

Natural Language ◽

Knowledge Base ◽

Question Answering ◽

Sparql Query ◽

Question Answering System ◽

Model Ranking ◽

Natural Language Question ◽

Query Generation ◽

Language Question ◽

Complex Question

In this paper we describe question answering system for answering of complex questions over Wikidata knowledge base. Unlike simple questions, which require extraction of single fact from the knowledge base, complex questions are based on more than one triplet and need logical or comparative reasoning. The proposed question answering system translates a natural language question into a query in SPARQL language, execution of which gives an answer. The system includes the models which define the SPARQL query template corresponding to the question and then fill the slots in the template with entities, relations and numerical values. For entity detection we use BERTbased sequence labelling model. Ranking of candidate relations is performed in two steps with BiLSTM and BERT-based models. The proposed models are the first solution for LC-QUAD2.0 dataset. The system is capable of answering complex questions which involve comparative or boolean reasoning.

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Question Answering Model in Thai by using Squad Thai Wikipedia dataset

10.36227/techrxiv.17195000.v1 ◽

2021 ◽

Author(s):

Wicharn Rueangkhajorn ◽

Jonathan H. Chan

Keyword(s):

Natural Language Processing ◽

Natural Language ◽

Language Processing ◽

English Language ◽

Question Answering ◽

Thai Language ◽

Model Sharing ◽

Fine Tune ◽

Language Question ◽

Better Than

Nowadays, Question Answering is one of the challenge applications in the Natural language processing domain. There are plenty of English language Question Answering model distributed on the model sharing website such as Hugging face hub. Unlike Thai language, there is on a few Thai language Question Answering model distributed on the model sharing website. So, we decided to fine-tune a multilingual Question Answering model to a specify language which is Thai language. The datasets that we will use for training is a Thai Wikipedia dataset from iApp Technology. We have tried to fine-tune on two multilingual model. We also create another dataset to evaluate adaptivity of the model. The result came out to be as satisfy. Both fine-tuned models perform better than base model on evaluation score. We have published Question Answering model to Hugging face hub that will allow people to using these models for others application later.

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