Exploiting Search Logs to Aid in Training and Automating Infrastructure for Question Answering in Professional Domains

Abstract. Understanding syntactic and semantic structure of geographic questions is a necessary step towards true geographic question-answering (GeoQA) machines. The empirical basis for the understanding of the capabilities expected from GeoQA systems are geographic question corpora. Available corpora in English have been mostly drawn from generic Web search logs or limited user studies, supporting the focus of GeoQA systems on retrieving factoids: factual knowledge about particular places and everyday processes. Yet, the majority of questions enquired about in the spatial sciences go beyond simple place facts, with more complex analytical intents informing the questions. In this paper, we introduce a new corpus of geo-analytic questions drawn from English textbooks and scientific articles. We analyse and compare this corpus with two general-purpose GeoQA corpora in terms of grammatical complexity and semantic concepts, using a new parsing method that allows us to differentiate and quantify patterns of a question’s intent.

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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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