Question Answering with DBpedia Based on the Dependency Parser and Entity-centric Index

2016 ◽  
Author(s):  
Huiying Li ◽  
Feifei Xu
Keyword(s):  
Question Answering ◽  
Dependency Parser ◽  
Entity Centric

scholarly journals Multi-step Entity-centric Information Retrieval for Multi-Hop Question Answering

2019 ◽  
Author(s):  
Rajarshi Das ◽  
Ameya Godbole ◽  
Dilip Kavarthapu ◽  
Zhiyu Gong ◽  
Abhishek Singhal ◽  
...  
Keyword(s):  
Information Retrieval ◽  
Question Answering ◽  
Entity Centric

scholarly journals Employing Dependency Tree in Machine Learning Based Indonesian Factoid Question Answering

2019 ◽  
Vol 2 (1) ◽  
pp. 28
Author(s):  
Irfan Afif ◽  
Ayu Purwarianti
Keyword(s):  
Machine Learning ◽  
Correct Answer ◽  
Question Answering ◽  
Machine Learning Technique ◽  
Heuristic Rule ◽  
Question Answering System ◽  
Dependency Relation ◽  
Dependency Tree ◽  
Learning Technique ◽  
Dependency Parser

We proposed the usage of dependency tree information to increase the accuracy of Indonesian factoid question answering. We employed MSTParser and Universal Dependency corpus to build the Indonesian dependency parser. The dependency tree information as the result of the Indonesian dependency parse is used in the answer finder component of Indonesian factoid question answering system. Here, we used dependency tree information in two ways: 1) as one of the features in machine learning based answer finder (classifying each term in the retrieved passage as part of a correct answer or not); 2) as an additional heuristic rule after conducting the machine learning technique. For the machine learning technique, we combined word based calculation, phrase based calculation and similarity dependency relation based calculation as the complete features. Using 203 data, we were able to enhance the accuracy for the Indonesian factoid QA system compared to related work by only using the phrase information. The best accuracy was 84.34% for the correct answer classification and the best MRR was 0.954.

A closed-domain question answering framework using reliable resources to assist students

2018 ◽  
Vol 24 (5) ◽  
pp. 725-762 ◽  
Author(s):  
CANER DERİCİ ◽  
YİĞİT AYDIN ◽  
ÇİĞDEM YENİALACA ◽  
NİHAL YAĞMUR AYDIN ◽  
GÜNİZİ KARTAL ◽  
...  
Keyword(s):  
Pilot Study ◽  
Question Answering ◽  
Data Bank ◽  
Relevant Information ◽  
Closed Domain ◽  
Rule Based ◽  
Coverage Metrics ◽  
Student Questions ◽  
Turkish Language ◽  
Dependency Parser

AbstractThis paper describes a question answering framework that can answer student questions given in natural language. We suggest a methodology that makes use of reliable resources only, provides the answer in the form of a multi-document summary for both factoid and open-ended questions, and produces an answer also from foreign resources by translating into the native language. The resources are compiled using a question database in the selected domains based on reliability and coverage metrics. A question is parsed using a dependency parser, important parts are extracted by rule-based and statistical methods, the question is converted into a representation, and a query is built. Documents relevant to the query are retrieved from the set of resources. The documents are summarized and the answers to the question together with other relevant information about the topic of the question are shown to the user. A summary answer from the foreign resources is also built by the translation of the input question and the retrieved documents. The proposed approach was applied to the Turkish language and it was tested with several experiments and a pilot study. The experiments have shown that the summaries returned include the answer for about 50–60 percent of the questions. The data bank built for factoid and open-ended questions in the two domains covered is made publicly available.

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

AI Magazine ◽  
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.

Automated question answering in Webclopedia

2002 ◽  
Author(s):  
Ulf Hermjakob ◽  
Eduard Hovy ◽  
Chin-Yew Lin
Keyword(s):  
Question Answering

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

2018 ◽  
Vol 10 (1) ◽  
pp. 57-64 ◽  
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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