Question Answering-based Socio-economic Indicator Extraction

The dairy industry seems to have convinced the food industry that whey is a miracle product. The list of supposed benefits it gives to food is as long as your arm. Some of the benefits may be real. Whey is the liquid remaining after milk has been curdled and strained. It is a by-product of the manufacture of cheese or casein and has several commercial uses. To produce cheese, rennet or an edible acid is added to heated milk. This makes the milk coagulate or curdle, separating the milk solids (curds) from the liquid whey. Sweet whey is the byproduct of rennet-coagulated cheese and acid whey (also called sour whey) is the byproduct of acid-coagulated cheese. Sweet whey has a pH greater than or equal to 5.6, acid whey has a pH less than or equal to 5.1. Whey is also a great way to add sweetness to a product without having to list sugar as an ingredient as whey contains up to 75% lactose. And it sounds healthy. This study is done to research the examinations for the production of mozzarella cheese from Cow’s milk, after research and analyses of a physical-chemical peculiar feature of whey from coagulum. We have followed the processes from the drying of whey from the coagulum analyzer's physical-chemical peculiar feature. We carried out three experiments. For every experiment, we took three patterns and analyzed the physical-chemical. The calculation was appraised statistically. This paper deals with the research of% of whey fat during the process of milk production from standardized to non-standardized milk. Where% of whey fat should be an economic indicator for standardizing milk for dairy production.

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Reports of the Workshops Held at the 2019 AAAI Conference on Artificial Intelligence

AI Magazine ◽

10.1609/aimag.v40i3.4981 ◽

2019 ◽

Vol 40 (3) ◽

pp. 67-78

Author(s):

Guy Barash ◽

Mauricio Castillo-Effen ◽

Niyati Chhaya ◽

Peter Clark ◽

Huáscar Espinoza ◽

...

Keyword(s):

Artificial Intelligence ◽

Language Processing ◽

Cyber Security ◽

Question Answering ◽

Intent Recognition ◽

Affective Content ◽

Learning Plan ◽

Dialog System ◽

Affective Content Analysis ◽

Games And Simulations

The workshop program of the Association for the Advancement of Artificial Intelligence’s 33rd Conference on Artificial Intelligence (AAAI-19) was held in Honolulu, Hawaii, on Sunday and Monday, January 27–28, 2019. There were fifteen workshops in the program: Affective Content Analysis: Modeling Affect-in-Action, Agile Robotics for Industrial Automation Competition, Artificial Intelligence for Cyber Security, Artificial Intelligence Safety, Dialog System Technology Challenge, Engineering Dependable and Secure Machine Learning Systems, Games and Simulations for Artificial Intelligence, Health Intelligence, Knowledge Extraction from Games, Network Interpretability for Deep Learning, Plan, Activity, and Intent Recognition, Reasoning and Learning for Human-Machine Dialogues, Reasoning for Complex Question Answering, Recommender Systems Meet Natural Language Processing, Reinforcement Learning in Games, and Reproducible AI. This report contains brief summaries of the all the workshops that were held.

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proceeding of the 2002 conference on multilingual summarization and question answering - COLING-02

10.3115/1118845 ◽

2002 ◽

Keyword(s):

Question Answering

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Automated question answering in Webclopedia

10.3115/1289189.1289230 ◽

2002 ◽

Cited By ~ 2

Author(s):

Ulf Hermjakob ◽

Eduard Hovy ◽

Chin-Yew Lin

Keyword(s):

Question Answering

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Natural language understanding and the perspectives of question answering

10.3115/991813.991871 ◽

1982 ◽

Author(s):

Petr Sgall

Keyword(s):

Natural Language ◽

Question Answering ◽

Natural Language Understanding ◽

Language Understanding

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Pencarian Question-Answer Menggunakan Convolutional Neural Network Pada Topik Agama Berbahasa Indonesia

Jurnal ULTIMATICS ◽

10.31937/ti.v10i1.842 ◽

2018 ◽

Vol 10 (1) ◽

pp. 57-64 ◽

Cited By ~ 1

Author(s):

Rizqa Raaiqa Bintana ◽

Chastine Fatichah ◽

Diana Purwitasari

Keyword(s):

Neural Network ◽

Convolutional Neural Network ◽

Question Answering ◽

Mean Average Precision ◽

Average Precision ◽

Community Based ◽

Network Methods ◽

The Mean ◽

Index Terms ◽

Search Information

Community-based question answering (CQA) is formed to help people who search information that they need through a community. One condition that may occurs in CQA is when people cannot obtain the information that they need, thus they will post a new question. This condition can cause CQA archive increased because of duplicated questions. Therefore, it becomes important problems to find semantically similar questions from CQA archive towards a new question. In this study, we use convolutional neural network methods for semantic modeling of sentence to obtain words that they represent the content of documents and new question. The result for the process of finding the same question semantically to a new question (query) from the question-answer documents archive using the convolutional neural network method, obtained the mean average precision value is 0,422. Whereas by using vector space model, as a comparison, obtained mean average precision value is 0,282. Index Terms—community-based question answering, convolutional neural network, question retrieval

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