다양한 문장형태에 대한 지리공간 분석절차 분류 연구 = A Study on the Classification of Geospatial Operation Workflow for Various Sentence Types

다국어 초록 (Multilingual Abstract)

Geo-analytical QA (QA: Question Answering)is a study that derives geospatial operation workflow included ingeospatial question and it is necessary to derive workflow for various sentence types in order to perform realistic geoanalyticalQA. Therefore, the purpose of this study is to develop an algorithm that can derive workflow for varioussentence types. To this end, text classification which matches a question to predefined workflows is used . To use textclassification, we need to select a corpus, label the corpus, extract feature which converts text into numbers, and trainthe classification models using the extracted feature. GeoAnQu, which is known to require various types of geospatialoperation workflows, was selected as a corpus and 23 workflows were derived by analyzing it. We then paraphrase itfor algorithm generalization verification and data augmentation and label it using 23 workflows. We performed corpusfeature extraction using TF-IDF that is based on statistical methodology. We trained linear SVM, decision tree, Multilayerperceptron classifier, and Naive Bayes classifier, and then selected the best performing linear SVM as the finalmodel. It was confirmed that the workflow for various sentence types can be derived through the model.

국문 초록 (Abstract)

지리공간분석 질의응답(Geo-analytical QA)은 지리공간질의에 내포된 지리공간 분석절차를 도출하는 연구로서 현실적인 지리공간분석 질의응답을 수행하기 위해서는 다양한 문장형태에 대해 분석절차를 도출할 수 있어야 한다. 따라서 본 연구에서는 다양한 문장형태에 대해 분석절차를 도출할 수 있는 알고리즘을 개발하는 것을 목표로 한다. 이를위해 미리 정의한 지리공간 분석절차 중 하나로 질의를 대응시키는 text classification을 사용했다. text classification을사용하기 위해서는 말뭉치 선정, 말뭉치 라벨링, 말뭉치를 숫자로 변환하는 특징추출, 마지막으로 특징추출 한 값을 이용해 분류모델을 학습시키는 과정이 필요하다. 말뭉치로는 지리공간질의를 답하기 위해 다양한 유형의 분석절차를 필요로 하는 것으로 알려진 GeoAnQu를 선정했고 해당 말뭉치를 분석해 23개의 분석절차를 도출했다. 그 후, 알고리즘일반화 검증 및 데이터 증강 목적으로 GeoAnQu말뭉치를 패러프레이징 한 후 이를 23개의 분석절차를 이용해 라벨링했다. 통계학적 방식에 기반한 TF-IDF를 이용해 말뭉치 특징추출을 수행하고 선형 SVM(Linear SVM), Decision tree,Multi-layer perceptron classifier, Naive Bayes classifier을 학습시킨 후 가장 높은 성능을 보인 선형 SVM을 최종 모델로 선정했다. 해당 모델을 통해 다양한 문장형태에 대해 분석절차를 도출할 수 있는 것을 확인했다.

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