scholarly journals Introducing MathQA: a Math-Aware question answering system

2018 ◽  
Vol 46 (4) ◽  
pp. 214-224 ◽  
Author(s):  
Moritz Schubotz ◽  
Philipp Scharpf ◽  
Kaushal Dudhat ◽  
Yash Nagar ◽  
Felix Hamborg ◽  
...  
Keyword(s):  
Mathematical Knowledge ◽  
Design Methodology ◽  
Question Answering ◽  
User Study ◽  
Content Type ◽  
Question Answering System ◽  
Knowledge Based ◽  
Natural Language Question ◽  
Language Question

Purpose This paper aims to present an open source math-aware Question Answering System based on Ask Platypus. Design/methodology/approach The system returns as a single mathematical formula for a natural language question in English or Hindi. These formulae originate from the knowledge-based Wikidata. The authors translate these formulae to computable data by integrating the calculation engine sympy into the system. This way, users can enter numeric values for the variables occurring in the formula. Moreover, the system loads numeric values for constants occurring in the formula from Wikidata. Findings In a user study, this system outperformed a commercial computational mathematical knowledge engine by 13 per cent. However, the performance of this system heavily depends on the size and quality of the formula data available in Wikidata. As only a few items in Wikidata contained formulae when the project started, the authors facilitated the import process by suggesting formula edits to Wikidata editors. With the simple heuristic that the first formula is significant for the paper, 80 per cent of the suggestions were correct. Originality/value This research was presented at the JCDL17 KDD workshop.

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
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