scholarly journals Architecture of an Ontology-Based Domain-Specific Natural Language Question Answering System

2013 ◽  
Vol 4 (4) ◽  
pp. 31-39 ◽  
Author(s):  
Athira P.M ◽  
Sreeja M ◽  
Reghuraj P.C
Keyword(s):  
Natural Language ◽  
Question Answering ◽  
Question Answering System ◽  
Domain Specific ◽  
Natural Language Question ◽  
Language Question

scholarly journals QUASE: AN Ontology-Based Domain Specific Natural Language Question Answering System

2019 ◽  
Vol 8 (4) ◽  
pp. 261-268
Keyword(s):  
Natural Language ◽  
Language Processing ◽  
Question Answering ◽  
Closed Domain ◽  
Question Answering System ◽  
Pos Tagging ◽  
Part Of Speech ◽  
Natural Language Question ◽  
Finite Set ◽  
Language Question

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.

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

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.

scholarly journals PERANCANGAN APLIKASI QUESTION ANSWERING SYSTEM PADA TERJEMAHAN ALQURAN

2019 ◽  
Vol 1 (1) ◽  
pp. 8-12
Author(s):  
Arfiani Nur Khusna ◽  
Murein Miksa Mardhia
Keyword(s):  
Natural Language ◽  
Question Answering ◽  
Question Answering System ◽  
Natural Language Question ◽  
Language Question

Al quran merupakan tuntunan bagi umat Islam. Suatu permasalahan tidak hanya mengacu pada satu ayat ataupun satu surat sehingga dibutuhkan waktu yang lama dalam proses pencarian secara manual, mengingat banyaknya jumlah ayat dan surat yang terkandung dalam Al quran. Berdasarkan hasil kuisioner yang dibagikan kepada 50 responden, terdapat 75% responden tidak dapat atau kesulitan dalam mencari jawaban terhadap makna atau masalah yang didasarkan pada terjemahan Al quran. Question answering system adalah sistem yang mengijinkan user menyatakan kebutuhan informasinya dalam bentuk natural language question (pertanyaan dalam bahasa alami), dan mengembalikan kutipan teks singkat atau bahkan frase sebagai jawaban. Penelitian ini merancang aplikasi question answering system pada terjemahan al quran untuk membantu pengguna menemukan jawaban terjemahan Al quran dengan menggunakan pertanyaan yaitu dimana, apa, siapa, berapa, kapan dan mengapa. Berdasarkan hasil pengujian rancangan aplikasi diperoleh nilai usability 28 dari 35 yang menunjukkan bahwa rancangan aplikasi layak dikembangkan sebagai alat bantu dalam mencari jawaban terjemahan Al quran dan sesuai kebutuhan pengguna.

Processing of unknown words in a natural language question-answering system

1988 ◽  
Vol 4 (2) ◽  
pp. 205-211 ◽  
Author(s):  
J. P. Fournier ◽  
P. Herman ◽  
G. Sabah ◽  
A. Vilnat ◽  
N. Burgaud ◽  
...  
Keyword(s):  
Natural Language ◽  
Question Answering ◽  
Question Answering System ◽  
Natural Language Question ◽  
Unknown Words ◽  
Language Question

scholarly journals Natural Language Access to Enterprise Data

AI Magazine ◽  
2014 ◽  
Vol 35 (1) ◽  
pp. 38 ◽  
Author(s):  
Ulli Waltinger ◽  
Dan Tecuci ◽  
Mihaela Olteanu ◽  
Vlad Mocanu ◽  
Sean Sullivan
Keyword(s):  
Natural Language ◽  
Question Answering ◽  
Data Sources ◽  
Easy Access ◽  
Data Set ◽  
Language Access ◽  
Question Answering System ◽  
Natural Language Question ◽  
Language Question

This paper describes USI Answers — a natural language question answering system for enterprise data. We report on the progress towards the goal of offering easy access to enterprise data to a large number of business users, most of whom are not familiar with the specific syntax or semantics of the underlying data sources. Additional complications come from the nature of the data, which comes both as structured and unstructured. The proposed solution allows users to express questions in natural language, makes apparent the system's interpretation of the query, and allows easy query adjustment and reformulation. The application is in use by more than 1500 users from Siemens Energy. We evaluate our approach on a data set consisting of fleet data.

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