• Environmental and Resource Economics Review
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• v.17 no.2
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• pp.327-347
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• 2008

This study proposes a new answering mechanism of deciding whether to purchase or not under uncertainty-real risk answering mechanism (R-RAM) for real transaction and hypothetical risk answering mechanism (H-RAM). It IS also. shown that the traditional answering mechanism (TAM) assumed in most of existing contingent valuation methods should be applied in a real transaction without uncertainty. While the willingness to pay (WTP) being simply compared with the bidding price m TAM, the mean of WTP should be greater than the bidding pnce at least by the risk premium for a purchase decision m R-RAM and H-RAM. Only difference between R-RAM and H-RAM is risk premium. The H-RAM takes a smaller risk premium than the R-RAM. This study proposes the contingent valuation method (CVM) with H-RAM could be an alternative to reducing the under-or over-estimation of WTP by comparing the WTP estimates obtained from three different CVMs with TAM, R-RAM and H-RAM.

• The Korean Journal of Applied Statistics
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• v.34 no.2
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• pp.191-204
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• 2021

Visual Question Answering (VQA) and image captioning are tasks that require understanding of the features of images and linguistic features of text. Therefore, co-attention may be the key to both tasks, which can connect image and text. In this paper, we propose a model to achieve high performance for VQA by image caption generated using a pretrained standard transformer model based on MSCOCO dataset. Captions unrelated to the question can rather interfere with answering, so some captions similar to the question were selected to use based on a similarity to the question. In addition, stopwords in the caption could not affect or interfere with answering, so the experiment was conducted after removing stopwords. Experiments were conducted on VQA-v2 data to compare the proposed model with the deep modular co-attention network (MCAN) model, which showed good performance by using co-attention between images and text. As a result, the proposed model outperformed the MCAN model.

• ETRI Journal
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• v.31 no.4
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• pp.419-428
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• 2009

A question answering (QA) system can be built using multiple QA modules that can individually serve as a QA system in and of themselves. This paper proposes a learnable, strategy-driven QA model that aims at enhancing both efficiency and effectiveness. A strategy is learned using a learning-based classification algorithm that determines the sequence of QA modules to be invoked and decides when to stop invoking additional modules. The learned strategy invokes the most suitable QA module for a given question and attempts to verify the answer by consulting other modules until the level of confidence reaches a threshold. In our experiments, our strategy learning approach obtained improvement over a simple routing approach by 10.5% in effectiveness and 27.2% in efficiency.

• Proceedings of the Korea Contents Association Conference
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• 2005.05a
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• pp.448-451
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• 2005

In this paper, we describe a method of answer extraction on a concept-based question-answering system. The concept-based question answering system is a system which extract answer using concept information. we have researched the method of answer extraction using concepts which analyzed and extracted through question analysing with answer extracting rules. We analyzed documents including answers and then composed answer extracting rules. Rules consist of concept and syntactic information, we generated candidates of answer through the rules and then chose answer.

• International Journal of Contents
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• v.2 no.1
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• pp.17-21
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• 2006

In this paper, we describe a concept-based question-answering system in which concept rather than keyword itself makes an important role on both question analysis and answer extraction. Our idea is that concepts occurred in same type of questions are similar, and if a question is analyzed according to those concepts then we can extract more accurate answer because we know the semantic role of each word or phrase in question. Concept frame is defined for each type of question, and it is composed of important concepts in that question type. Currently the number of question type is 79 including 34 types for person, 14 types for location, and so on. We experiment this concept-based approach about questions which require person s name as their answer. Experimental results show that our system has high accuracy in answer extraction. Also, this concept-based approach can be used in combination with conventional approaches.

• ETRI Journal
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• v.42 no.2
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• pp.239-246
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• 2020

Recently, Linked Open Data has become a large set of knowledge bases. Therefore, the need to query Linked Data using question answering (QA) techniques has attracted the attention of many researchers. A QA system translates natural language questions into structured queries, such as SPARQL queries, to be executed over Linked Data. The two main challenges in such systems are lexical and semantic gaps. A lexical gap refers to the difference between the vocabularies used in an input question and those used in the knowledge base. A semantic gap refers to the difference between expressed information needs and the representation of the knowledge base. In this paper, we present a novel method using an ontology lexicon and dependency parse trees to overcome lexical and semantic gaps. The proposed technique is evaluated on the QALD-5 benchmark and exhibits promising results.

• Annual Conference on Human and Language Technology
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• 2022.10a
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• pp.95-99
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• 2022

오픈 도메인 질의 응답 (ODQA, Open-Domain Question Answering)은 주어진 질문에 대한 답을 찾는 과업으로서 질문과 관련있는 지식을 찾는 "검색" 단계를 필요로 한다. 최근 이미지, 테이블 등의 검색을 요구하는 멀티 모달 ODQA에 대한 연구가 많이 진행되었을 뿐만 아니라 산업에서의 중요도 또한 높아지고 있다. 본 논문은 여러 종류의 멀티 모달 ODQA 중에서도 테이블 - 텍스트 기반 멀티 모달 ODQA 데이터 집합으로 Fusion-in-Decoder (FiD)를 이용한 멀티 모달 오픈 도메인 질의 응답 연구를 제안하며 베이스라인 대비 최대 EM 20.5, F1 23.2 향상을 보였다.

• Journal of Digital Contents Society
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• v.11 no.3
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• pp.375-383
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• 2010

In developing question-answering (QA) systems, it is hard to analyze natural language questions syntactically and semantically and to find exact answers to given query questions. In order to avoid these difficulties, we propose a new style of question-answering system that automatically generate natural language queries and can allow to search queries fit for given keywords. The key idea behind generating natural queries is that after significant sentences within text documents are applied to the named entity recognition technique, we can generate a natural query (interrogative sentence) for each named entity (such as person, location, and time). The natural query is divided into two types: simple type and sentence structure type. With the large database of question-answer pairs, the system can easily obtain natural queries and their corresponding answers for given keywords. The most important issue is how to generate meaningful queries which can present unambiguous answers. To this end, we propose two principles to decide which declarative sentences can be the sources of natural queries and a pattern-based method for generating meaningful queries from the selected sentences.

• The KIPS Transactions:PartD
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• v.10D no.4
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• pp.661-670
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• 2003

Materialized views which are stored views of the database offer opportunities for significant performance gain in query valuation by providing fast access to pre-computed data. The question of when and how to use a materialized view in processing a given query is a difficult one attracting a significant amount of research. Whether a materialized view can be used in answering a query depends on the relationship between the view and the query. Proposed in this paper are new ways of utilizing materialized views in answering a query. Semantic relationships are used in addition to syntactic ones. We also utilize a materialized view in answering a query even if it has relations unrelated to the query. We first show the conditions for testing whether a materialized view can be utilized in answering a query and then present the algorithms for testing the conditions and reformulating a query with a materialized view.

• Environmental and Resource Economics Review
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• v.14 no.4
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• pp.793-816
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• 2005

This paper proposes a new answering mechanism called RAM (Risk Answering Mechanism) which can be applied to a contingent valuation method. The RAM is derived from the theory of expected utility maximization under the assumption that there exists an uncertainty in a nonmarket good of interest. In RAM, a respondent would accept an uncertain offer only if his or her WTP for the mean of the offer is large enough to exceed the bidding price by more than a risk premium. This is in a striking contrast with a traditional answering mechanism (TAM) in which the WTP is simply compared with the bidding price. Therefore, the TAM would underestimate the WTP by a risk premium without considering the uncertainty a respondent may face. An empirical comparison is made between RAM and TAM using a survey data on the Tong river. It is found that underestimation problem is very serious in the TAM.