TY - GEN
T1 - Information Retrieval-Based Question Answering System on Foods and Recipes
AU - Manna, Riyanka
AU - Das, Dipankar
AU - Gelbukh, Alexander
N1 - Publisher Copyright:
© 2020, Springer Nature Switzerland AG.
PY - 2020
Y1 - 2020
N2 - Question Answering (QA) is an emerging domain of research that retrieves a textual segment from the set of documents in response to user’s queries. To recommend the answer in response to cooking recipe related questions is just an early stage of research and requires the significant refinement. In this paper, we have developed a question answering system on cooking recipes by using Natural Language Processing (NLP) and Information Retrieval (IR) technique. In recent years, with the rapid growth of information, the IR system has more importance in question answering domain. Users can also face difficulties to find expected answers from a huge amount of information. QA solves the information-overloading problem and IR returns the precise answers to the users. Answers from search engines are not only the results for a user’s query but these collective words should justify the questions. We have a standard dataset on recipes and foods from famous cities in India which is collected from various Indian recipe websites. We have used Apache Lucene for information retrieval and we have prepared the gold standard dataset for the question answering system on cooking recipes.
AB - Question Answering (QA) is an emerging domain of research that retrieves a textual segment from the set of documents in response to user’s queries. To recommend the answer in response to cooking recipe related questions is just an early stage of research and requires the significant refinement. In this paper, we have developed a question answering system on cooking recipes by using Natural Language Processing (NLP) and Information Retrieval (IR) technique. In recent years, with the rapid growth of information, the IR system has more importance in question answering domain. Users can also face difficulties to find expected answers from a huge amount of information. QA solves the information-overloading problem and IR returns the precise answers to the users. Answers from search engines are not only the results for a user’s query but these collective words should justify the questions. We have a standard dataset on recipes and foods from famous cities in India which is collected from various Indian recipe websites. We have used Apache Lucene for information retrieval and we have prepared the gold standard dataset for the question answering system on cooking recipes.
KW - Apache Lucene
KW - Information retrieval
KW - Natural language processing
KW - Question Answering
KW - Question types
UR - http://www.scopus.com/inward/record.url?scp=85092921438&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-60887-3_23
DO - 10.1007/978-3-030-60887-3_23
M3 - Contribución a la conferencia
AN - SCOPUS:85092921438
SN - 9783030608866
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 260
EP - 270
BT - Advances in Computational Intelligence - 19th Mexican International Conference on Artificial Intelligence, MICAI 2020, Proceedings
A2 - Martínez-Villaseñor, Lourdes
A2 - Ponce, Hiram
A2 - Herrera-Alcántara, Oscar
A2 - Castro-Espinoza, Félix A.
PB - Springer Science and Business Media Deutschland GmbH
T2 - 19th Mexican International Conference on Artificial Intelligence, MICAI 2020
Y2 - 12 October 2020 through 17 October 2020
ER -