scholarly journals What Can We Learn from Almost a Decade of Food Tweets

2020 ◽  
Author(s):  
Uga Sproģis ◽  
Matīss Rikters
Keyword(s):  
Sentiment Analysis ◽  
Question Answering ◽  
Time Span ◽  
Use Cases ◽  
Specific Question ◽  
Domain Specific ◽  
Question And Answer ◽  
Analysis Models ◽  
Over Time

We present the Latvian Twitter Eater Corpus - a set of tweets in the narrow domain related to food, drinks, eating and drinking. The corpus has been collected over time-span of over 8 years and includes over 2 million tweets entailed with additional useful data. We also separate two sub-corpora of question and answer tweets and sentiment annotated tweets. We analyse the contents of the corpus and demonstrate use-cases for the sub-corpora by training domain-specific question-answering and sentiment-analysis models using the data from the corpus.

scholarly journals BUILDING QUESTION ANSWERING SYSTEM BASED ON COMPUTING DOMAIN ONTOLOGY

2020 ◽  
Vol 38 (02) ◽  
Author(s):  
TẠ DUY CÔNG CHIẾN
Keyword(s):  
Language Processing ◽  
Digital Libraries ◽  
Question Answering ◽  
Domain Ontology ◽  
Text Documents ◽  
Question Answering System ◽  
Domain Specific ◽  
Sql Database ◽  
Question Answering Systems ◽  
Education Business

Question answering systems are applied to many different fields in recent years, such as education, business, and surveys. The purpose of these systems is to answer automatically the questions or queries of users about some problems. This paper introduces a question answering system is built based on a domain specific ontology. This ontology, which contains the data and the vocabularies related to the computing domain are built from text documents of the ACM Digital Libraries. Consequently, the system only answers the problems pertaining to the information technology domains such as database, network, machine learning, etc. We use the methodologies of Natural Language Processing and domain ontology to build this system. In order to increase performance, I use a graph database to store the computing ontology and apply no-SQL database for querying data of computing ontology.

scholarly journals Penerapan Question Answering System Pada Pembahasan Agama Islam Dengan Pendekatan Metode Pattern Based

2018 ◽  
Vol 2 (4) ◽  
pp. 140 ◽  
Author(s):  
Ramadhana Rosyadi ◽  
Said Al-Faraby ◽  
Adiwijaya Adiwijaya
Keyword(s):  
Information Retrieval ◽  
Question Answering ◽  
Human Life ◽  
Specific Question ◽  
Question Answering System ◽  
Retrieval Systems ◽  
Question And Answer ◽  
Information Retrieval Systems ◽  
System Information ◽  
Selection Of

Islam has 25 prophets as guidelines for human life, documents containing information about the stories of the lives of the prophets during their lifetime. This study aims to build a more specific question and answer system by generating relevant answers not in the form of documents. Question Answering System is able to overcome problems in the Question and answer system, information retrieval systems where the answers issued are correct with responses to requests submitted, not in the form of documents that may contain answers. This study uses the Pattern Based method as extracting sentence pieces which are the answers to find answers that match the patterns that have been made. The selection of datasets causes a number of questions that can be submitted to be limited to information stored in the data itself. Besides that, questions are also limited in the form of Question words that are Factoid, namely Who, when, where, what and how. Accuracy results obtained using the Pattern Based method on Question Answering System are 39.36%.

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